qualitative research interview design

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J Basic Clin Pharm
v.5(4); September 2014-November 2014

Qualitative research method-interviewing and observation

Shazia jamshed.

Department of Pharmacy Practice, Kulliyyah of Pharmacy, International Islamic University Malaysia, Kuantan Campus, Pahang, Malaysia

Buckley and Chiang define research methodology as “a strategy or architectural design by which the researcher maps out an approach to problem-finding or problem-solving.”[ 1 ] According to Crotty, research methodology is a comprehensive strategy ‘that silhouettes our choice and use of specific methods relating them to the anticipated outcomes,[ 2 ] but the choice of research methodology is based upon the type and features of the research problem.[ 3 ] According to Johnson et al . mixed method research is “a class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, theories and or language into a single study.[ 4 ] In order to have diverse opinions and views, qualitative findings need to be supplemented with quantitative results.[ 5 ] Therefore, these research methodologies are considered to be complementary to each other rather than incompatible to each other.[ 6 ]

Qualitative research methodology is considered to be suitable when the researcher or the investigator either investigates new field of study or intends to ascertain and theorize prominent issues.[ 6 , 7 ] There are many qualitative methods which are developed to have an in depth and extensive understanding of the issues by means of their textual interpretation and the most common types are interviewing and observation.[ 7 ]

Interviewing

This is the most common format of data collection in qualitative research. According to Oakley, qualitative interview is a type of framework in which the practices and standards be not only recorded, but also achieved, challenged and as well as reinforced.[ 8 ] As no research interview lacks structure[ 9 ] most of the qualitative research interviews are either semi-structured, lightly structured or in-depth.[ 9 ] Unstructured interviews are generally suggested in conducting long-term field work and allow respondents to let them express in their own ways and pace, with minimal hold on respondents’ responses.[ 10 ]

Pioneers of ethnography developed the use of unstructured interviews with local key informants that is., by collecting the data through observation and record field notes as well as to involve themselves with study participants. To be precise, unstructured interview resembles a conversation more than an interview and is always thought to be a “controlled conversation,” which is skewed towards the interests of the interviewer.[ 11 ] Non-directive interviews, form of unstructured interviews are aimed to gather in-depth information and usually do not have pre-planned set of questions.[ 11 ] Another type of the unstructured interview is the focused interview in which the interviewer is well aware of the respondent and in times of deviating away from the main issue the interviewer generally refocuses the respondent towards key subject.[ 11 ] Another type of the unstructured interview is an informal, conversational interview, based on unplanned set of questions that are generated instantaneously during the interview.[ 11 ]

In contrast, semi-structured interviews are those in-depth interviews where the respondents have to answer preset open-ended questions and thus are widely employed by different healthcare professionals in their research. Semi-structured, in-depth interviews are utilized extensively as interviewing format possibly with an individual or sometimes even with a group.[ 6 ] These types of interviews are conducted once only, with an individual or with a group and generally cover the duration of 30 min to more than an hour.[ 12 ] Semi-structured interviews are based on semi-structured interview guide, which is a schematic presentation of questions or topics and need to be explored by the interviewer.[ 12 ] To achieve optimum use of interview time, interview guides serve the useful purpose of exploring many respondents more systematically and comprehensively as well as to keep the interview focused on the desired line of action.[ 12 ] The questions in the interview guide comprise of the core question and many associated questions related to the central question, which in turn, improve further through pilot testing of the interview guide.[ 7 ] In order to have the interview data captured more effectively, recording of the interviews is considered an appropriate choice but sometimes a matter of controversy among the researcher and the respondent. Hand written notes during the interview are relatively unreliable, and the researcher might miss some key points. The recording of the interview makes it easier for the researcher to focus on the interview content and the verbal prompts and thus enables the transcriptionist to generate “verbatim transcript” of the interview.

Similarly, in focus groups, invited groups of people are interviewed in a discussion setting in the presence of the session moderator and generally these discussions last for 90 min.[ 7 ] Like every research technique having its own merits and demerits, group discussions have some intrinsic worth of expressing the opinions openly by the participants. On the contrary in these types of discussion settings, limited issues can be focused, and this may lead to the generation of fewer initiatives and suggestions about research topic.

Observation

Observation is a type of qualitative research method which not only included participant's observation, but also covered ethnography and research work in the field. In the observational research design, multiple study sites are involved. Observational data can be integrated as auxiliary or confirmatory research.[ 11 ]

Research can be visualized and perceived as painstaking methodical efforts to examine, investigate as well as restructure the realities, theories and applications. Research methods reflect the approach to tackling the research problem. Depending upon the need, research method could be either an amalgam of both qualitative and quantitative or qualitative or quantitative independently. By adopting qualitative methodology, a prospective researcher is going to fine-tune the pre-conceived notions as well as extrapolate the thought process, analyzing and estimating the issues from an in-depth perspective. This could be carried out by one-to-one interviews or as issue-directed discussions. Observational methods are, sometimes, supplemental means for corroborating research findings.

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Methodology

Types of Interviews in Research | Guide & Examples

Types of Interviews in Research | Guide & Examples

Published on March 10, 2022 by Tegan George . Revised on June 22, 2023.

An interview is a qualitative research method that relies on asking questions in order to collect data . Interviews involve two or more people, one of whom is the interviewer asking the questions.

There are several types of interviews, often differentiated by their level of structure.

Structured interviews have predetermined questions asked in a predetermined order.
Unstructured interviews are more free-flowing.
Semi-structured interviews fall in between.

Interviews are commonly used in market research, social science, and ethnographic research .

What is a structured interview, what is a semi-structured interview, what is an unstructured interview, what is a focus group, examples of interview questions, advantages and disadvantages of interviews, other interesting articles, frequently asked questions about types of interviews.

Structured interviews have predetermined questions in a set order. They are often closed-ended, featuring dichotomous (yes/no) or multiple-choice questions. While open-ended structured interviews exist, they are much less common. The types of questions asked make structured interviews a predominantly quantitative tool.

Asking set questions in a set order can help you see patterns among responses, and it allows you to easily compare responses between participants while keeping other factors constant. This can mitigate research biases and lead to higher reliability and validity. However, structured interviews can be overly formal, as well as limited in scope and flexibility.

You feel very comfortable with your topic. This will help you formulate your questions most effectively.
You have limited time or resources. Structured interviews are a bit more straightforward to analyze because of their closed-ended nature, and can be a doable undertaking for an individual.
Your research question depends on holding environmental conditions between participants constant.

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Semi-structured interviews are a blend of structured and unstructured interviews. While the interviewer has a general plan for what they want to ask, the questions do not have to follow a particular phrasing or order.

Semi-structured interviews are often open-ended, allowing for flexibility, but follow a predetermined thematic framework, giving a sense of order. For this reason, they are often considered “the best of both worlds.”

However, if the questions differ substantially between participants, it can be challenging to look for patterns, lessening the generalizability and validity of your results.

You have prior interview experience. It’s easier than you think to accidentally ask a leading question when coming up with questions on the fly. Overall, spontaneous questions are much more difficult than they may seem.
Your research question is exploratory in nature. The answers you receive can help guide your future research.

An unstructured interview is the most flexible type of interview. The questions and the order in which they are asked are not set. Instead, the interview can proceed more spontaneously, based on the participant’s previous answers.

Unstructured interviews are by definition open-ended. This flexibility can help you gather detailed information on your topic, while still allowing you to observe patterns between participants.

However, so much flexibility means that they can be very challenging to conduct properly. You must be very careful not to ask leading questions, as biased responses can lead to lower reliability or even invalidate your research.

You have a solid background in your research topic and have conducted interviews before.
Your research question is exploratory in nature, and you are seeking descriptive data that will deepen and contextualize your initial hypotheses.
Your research necessitates forming a deeper connection with your participants, encouraging them to feel comfortable revealing their true opinions and emotions.

A focus group brings together a group of participants to answer questions on a topic of interest in a moderated setting. Focus groups are qualitative in nature and often study the group’s dynamic and body language in addition to their answers. Responses can guide future research on consumer products and services, human behavior, or controversial topics.

Focus groups can provide more nuanced and unfiltered feedback than individual interviews and are easier to organize than experiments or large surveys . However, their small size leads to low external validity and the temptation as a researcher to “cherry-pick” responses that fit your hypotheses.

Your research focuses on the dynamics of group discussion or real-time responses to your topic.
Your questions are complex and rooted in feelings, opinions, and perceptions that cannot be answered with a “yes” or “no.”
Your topic is exploratory in nature, and you are seeking information that will help you uncover new questions or future research ideas.

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Depending on the type of interview you are conducting, your questions will differ in style, phrasing, and intention. Structured interview questions are set and precise, while the other types of interviews allow for more open-endedness and flexibility.

Here are some examples.

Semi-structured
Unstructured
Focus group
Do you like dogs? Yes/No
Do you associate dogs with feeling: happy; somewhat happy; neutral; somewhat unhappy; unhappy
If yes, name one attribute of dogs that you like.
If no, name one attribute of dogs that you don’t like.
What feelings do dogs bring out in you?
When you think more deeply about this, what experiences would you say your feelings are rooted in?

Interviews are a great research tool. They allow you to gather rich information and draw more detailed conclusions than other research methods, taking into consideration nonverbal cues, off-the-cuff reactions, and emotional responses.

However, they can also be time-consuming and deceptively challenging to conduct properly. Smaller sample sizes can cause their validity and reliability to suffer, and there is an inherent risk of interviewer effect arising from accidentally leading questions.

Here are some advantages and disadvantages of each type of interview that can help you decide if you’d like to utilize this research method.

Advantages and disadvantages of interviews
Type of interview	Advantages	Disadvantages
Structured interview
Semi-structured interview		, , , and
Unstructured interview		, , , and
Focus group		, , and , since there are multiple people present

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

The four most common types of interviews are:

Structured interviews : The questions are predetermined in both topic and order.
Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
Unstructured interviews : None of the questions are predetermined.
Focus group interviews : The questions are presented to a group instead of one individual.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of 4 types of interviews .

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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Chapter 11. Interviewing

Introduction.

Interviewing people is at the heart of qualitative research. It is not merely a way to collect data but an intrinsically rewarding activity—an interaction between two people that holds the potential for greater understanding and interpersonal development. Unlike many of our daily interactions with others that are fairly shallow and mundane, sitting down with a person for an hour or two and really listening to what they have to say is a profound and deep enterprise, one that can provide not only “data” for you, the interviewer, but also self-understanding and a feeling of being heard for the interviewee. I always approach interviewing with a deep appreciation for the opportunity it gives me to understand how other people experience the world. That said, there is not one kind of interview but many, and some of these are shallower than others. This chapter will provide you with an overview of interview techniques but with a special focus on the in-depth semistructured interview guide approach, which is the approach most widely used in social science research.

An interview can be variously defined as “a conversation with a purpose” ( Lune and Berg 2018 ) and an attempt to understand the world from the point of view of the person being interviewed: “to unfold the meaning of peoples’ experiences, to uncover their lived world prior to scientific explanations” ( Kvale 2007 ). It is a form of active listening in which the interviewer steers the conversation to subjects and topics of interest to their research but also manages to leave enough space for those interviewed to say surprising things. Achieving that balance is a tricky thing, which is why most practitioners believe interviewing is both an art and a science. In my experience as a teacher, there are some students who are “natural” interviewers (often they are introverts), but anyone can learn to conduct interviews, and everyone, even those of us who have been doing this for years, can improve their interviewing skills. This might be a good time to highlight the fact that the interview is a product between interviewer and interviewee and that this product is only as good as the rapport established between the two participants. Active listening is the key to establishing this necessary rapport.

Patton ( 2002 ) makes the argument that we use interviews because there are certain things that are not observable. In particular, “we cannot observe feelings, thoughts, and intentions. We cannot observe behaviors that took place at some previous point in time. We cannot observe situations that preclude the presence of an observer. We cannot observe how people have organized the world and the meanings they attach to what goes on in the world. We have to ask people questions about those things” ( 341 ).

Types of Interviews

There are several distinct types of interviews. Imagine a continuum (figure 11.1). On one side are unstructured conversations—the kind you have with your friends. No one is in control of those conversations, and what you talk about is often random—whatever pops into your head. There is no secret, underlying purpose to your talking—if anything, the purpose is to talk to and engage with each other, and the words you use and the things you talk about are a little beside the point. An unstructured interview is a little like this informal conversation, except that one of the parties to the conversation (you, the researcher) does have an underlying purpose, and that is to understand the other person. You are not friends speaking for no purpose, but it might feel just as unstructured to the “interviewee” in this scenario. That is one side of the continuum. On the other side are fully structured and standardized survey-type questions asked face-to-face. Here it is very clear who is asking the questions and who is answering them. This doesn’t feel like a conversation at all! A lot of people new to interviewing have this ( erroneously !) in mind when they think about interviews as data collection. Somewhere in the middle of these two extreme cases is the “ semistructured” interview , in which the researcher uses an “interview guide” to gently move the conversation to certain topics and issues. This is the primary form of interviewing for qualitative social scientists and will be what I refer to as interviewing for the rest of this chapter, unless otherwise specified.

Types of Interviewing Questions: Unstructured conversations, Semi-structured interview, Structured interview, Survey questions

Informal (unstructured conversations). This is the most “open-ended” approach to interviewing. It is particularly useful in conjunction with observational methods (see chapters 13 and 14). There are no predetermined questions. Each interview will be different. Imagine you are researching the Oregon Country Fair, an annual event in Veneta, Oregon, that includes live music, artisan craft booths, face painting, and a lot of people walking through forest paths. It’s unlikely that you will be able to get a person to sit down with you and talk intensely about a set of questions for an hour and a half. But you might be able to sidle up to several people and engage with them about their experiences at the fair. You might have a general interest in what attracts people to these events, so you could start a conversation by asking strangers why they are here or why they come back every year. That’s it. Then you have a conversation that may lead you anywhere. Maybe one person tells a long story about how their parents brought them here when they were a kid. A second person talks about how this is better than Burning Man. A third person shares their favorite traveling band. And yet another enthuses about the public library in the woods. During your conversations, you also talk about a lot of other things—the weather, the utilikilts for sale, the fact that a favorite food booth has disappeared. It’s all good. You may not be able to record these conversations. Instead, you might jot down notes on the spot and then, when you have the time, write down as much as you can remember about the conversations in long fieldnotes. Later, you will have to sit down with these fieldnotes and try to make sense of all the information (see chapters 18 and 19).

Interview guide ( semistructured interview ). This is the primary type employed by social science qualitative researchers. The researcher creates an “interview guide” in advance, which she uses in every interview. In theory, every person interviewed is asked the same questions. In practice, every person interviewed is asked mostly the same topics but not always the same questions, as the whole point of a “guide” is that it guides the direction of the conversation but does not command it. The guide is typically between five and ten questions or question areas, sometimes with suggested follow-ups or prompts . For example, one question might be “What was it like growing up in Eastern Oregon?” with prompts such as “Did you live in a rural area? What kind of high school did you attend?” to help the conversation develop. These interviews generally take place in a quiet place (not a busy walkway during a festival) and are recorded. The recordings are transcribed, and those transcriptions then become the “data” that is analyzed (see chapters 18 and 19). The conventional length of one of these types of interviews is between one hour and two hours, optimally ninety minutes. Less than one hour doesn’t allow for much development of questions and thoughts, and two hours (or more) is a lot of time to ask someone to sit still and answer questions. If you have a lot of ground to cover, and the person is willing, I highly recommend two separate interview sessions, with the second session being slightly shorter than the first (e.g., ninety minutes the first day, sixty minutes the second). There are lots of good reasons for this, but the most compelling one is that this allows you to listen to the first day’s recording and catch anything interesting you might have missed in the moment and so develop follow-up questions that can probe further. This also allows the person being interviewed to have some time to think about the issues raised in the interview and go a little deeper with their answers.

Standardized questionnaire with open responses ( structured interview ). This is the type of interview a lot of people have in mind when they hear “interview”: a researcher comes to your door with a clipboard and proceeds to ask you a series of questions. These questions are all the same whoever answers the door; they are “standardized.” Both the wording and the exact order are important, as people’s responses may vary depending on how and when a question is asked. These are qualitative only in that the questions allow for “open-ended responses”: people can say whatever they want rather than select from a predetermined menu of responses. For example, a survey I collaborated on included this open-ended response question: “How does class affect one’s career success in sociology?” Some of the answers were simply one word long (e.g., “debt”), and others were long statements with stories and personal anecdotes. It is possible to be surprised by the responses. Although it’s a stretch to call this kind of questioning a conversation, it does allow the person answering the question some degree of freedom in how they answer.

Survey questionnaire with closed responses (not an interview!). Standardized survey questions with specific answer options (e.g., closed responses) are not really interviews at all, and they do not generate qualitative data. For example, if we included five options for the question “How does class affect one’s career success in sociology?”—(1) debt, (2) social networks, (3) alienation, (4) family doesn’t understand, (5) type of grad program—we leave no room for surprises at all. Instead, we would most likely look at patterns around these responses, thinking quantitatively rather than qualitatively (e.g., using regression analysis techniques, we might find that working-class sociologists were twice as likely to bring up alienation). It can sometimes be confusing for new students because the very same survey can include both closed-ended and open-ended questions. The key is to think about how these will be analyzed and to what level surprises are possible. If your plan is to turn all responses into a number and make predictions about correlations and relationships, you are no longer conducting qualitative research. This is true even if you are conducting this survey face-to-face with a real live human. Closed-response questions are not conversations of any kind, purposeful or not.

In summary, the semistructured interview guide approach is the predominant form of interviewing for social science qualitative researchers because it allows a high degree of freedom of responses from those interviewed (thus allowing for novel discoveries) while still maintaining some connection to a research question area or topic of interest. The rest of the chapter assumes the employment of this form.

Creating an Interview Guide

Your interview guide is the instrument used to bridge your research question(s) and what the people you are interviewing want to tell you. Unlike a standardized questionnaire, the questions actually asked do not need to be exactly what you have written down in your guide. The guide is meant to create space for those you are interviewing to talk about the phenomenon of interest, but sometimes you are not even sure what that phenomenon is until you start asking questions. A priority in creating an interview guide is to ensure it offers space. One of the worst mistakes is to create questions that are so specific that the person answering them will not stray. Relatedly, questions that sound “academic” will shut down a lot of respondents. A good interview guide invites respondents to talk about what is important to them, not feel like they are performing or being evaluated by you.

Good interview questions should not sound like your “research question” at all. For example, let’s say your research question is “How do patriarchal assumptions influence men’s understanding of climate change and responses to climate change?” It would be worse than unhelpful to ask a respondent, “How do your assumptions about the role of men affect your understanding of climate change?” You need to unpack this into manageable nuggets that pull your respondent into the area of interest without leading him anywhere. You could start by asking him what he thinks about climate change in general. Or, even better, whether he has any concerns about heatwaves or increased tornadoes or polar icecaps melting. Once he starts talking about that, you can ask follow-up questions that bring in issues around gendered roles, perhaps asking if he is married (to a woman) and whether his wife shares his thoughts and, if not, how they negotiate that difference. The fact is, you won’t really know the right questions to ask until he starts talking.

There are several distinct types of questions that can be used in your interview guide, either as main questions or as follow-up probes. If you remember that the point is to leave space for the respondent, you will craft a much more effective interview guide! You will also want to think about the place of time in both the questions themselves (past, present, future orientations) and the sequencing of the questions.

Researcher Note

Suggestion : As you read the next three sections (types of questions, temporality, question sequence), have in mind a particular research question, and try to draft questions and sequence them in a way that opens space for a discussion that helps you answer your research question.

Type of Questions

Experience and behavior questions ask about what a respondent does regularly (their behavior) or has done (their experience). These are relatively easy questions for people to answer because they appear more “factual” and less subjective. This makes them good opening questions. For the study on climate change above, you might ask, “Have you ever experienced an unusual weather event? What happened?” Or “You said you work outside? What is a typical summer workday like for you? How do you protect yourself from the heat?”

Opinion and values questions , in contrast, ask questions that get inside the minds of those you are interviewing. “Do you think climate change is real? Who or what is responsible for it?” are two such questions. Note that you don’t have to literally ask, “What is your opinion of X?” but you can find a way to ask the specific question relevant to the conversation you are having. These questions are a bit trickier to ask because the answers you get may depend in part on how your respondent perceives you and whether they want to please you or not. We’ve talked a fair amount about being reflective. Here is another place where this comes into play. You need to be aware of the effect your presence might have on the answers you are receiving and adjust accordingly. If you are a woman who is perceived as liberal asking a man who identifies as conservative about climate change, there is a lot of subtext that can be going on in the interview. There is no one right way to resolve this, but you must at least be aware of it.

Feeling questions are questions that ask respondents to draw on their emotional responses. It’s pretty common for academic researchers to forget that we have bodies and emotions, but people’s understandings of the world often operate at this affective level, sometimes unconsciously or barely consciously. It is a good idea to include questions that leave space for respondents to remember, imagine, or relive emotional responses to particular phenomena. “What was it like when you heard your cousin’s house burned down in that wildfire?” doesn’t explicitly use any emotion words, but it allows your respondent to remember what was probably a pretty emotional day. And if they respond emotionally neutral, that is pretty interesting data too. Note that asking someone “How do you feel about X” is not always going to evoke an emotional response, as they might simply turn around and respond with “I think that…” It is better to craft a question that actually pushes the respondent into the affective category. This might be a specific follow-up to an experience and behavior question —for example, “You just told me about your daily routine during the summer heat. Do you worry it is going to get worse?” or “Have you ever been afraid it will be too hot to get your work accomplished?”

Knowledge questions ask respondents what they actually know about something factual. We have to be careful when we ask these types of questions so that respondents do not feel like we are evaluating them (which would shut them down), but, for example, it is helpful to know when you are having a conversation about climate change that your respondent does in fact know that unusual weather events have increased and that these have been attributed to climate change! Asking these questions can set the stage for deeper questions and can ensure that the conversation makes the same kind of sense to both participants. For example, a conversation about political polarization can be put back on track once you realize that the respondent doesn’t really have a clear understanding that there are two parties in the US. Instead of asking a series of questions about Republicans and Democrats, you might shift your questions to talk more generally about political disagreements (e.g., “people against abortion”). And sometimes what you do want to know is the level of knowledge about a particular program or event (e.g., “Are you aware you can discharge your student loans through the Public Service Loan Forgiveness program?”).

Sensory questions call on all senses of the respondent to capture deeper responses. These are particularly helpful in sparking memory. “Think back to your childhood in Eastern Oregon. Describe the smells, the sounds…” Or you could use these questions to help a person access the full experience of a setting they customarily inhabit: “When you walk through the doors to your office building, what do you see? Hear? Smell?” As with feeling questions , these questions often supplement experience and behavior questions . They are another way of allowing your respondent to report fully and deeply rather than remain on the surface.

Creative questions employ illustrative examples, suggested scenarios, or simulations to get respondents to think more deeply about an issue, topic, or experience. There are many options here. In The Trouble with Passion , Erin Cech ( 2021 ) provides a scenario in which “Joe” is trying to decide whether to stay at his decent but boring computer job or follow his passion by opening a restaurant. She asks respondents, “What should Joe do?” Their answers illuminate the attraction of “passion” in job selection. In my own work, I have used a news story about an upwardly mobile young man who no longer has time to see his mother and sisters to probe respondents’ feelings about the costs of social mobility. Jessi Streib and Betsy Leondar-Wright have used single-page cartoon “scenes” to elicit evaluations of potential racial discrimination, sexual harassment, and classism. Barbara Sutton ( 2010 ) has employed lists of words (“strong,” “mother,” “victim”) on notecards she fans out and asks her female respondents to select and discuss.

Background/Demographic Questions

You most definitely will want to know more about the person you are interviewing in terms of conventional demographic information, such as age, race, gender identity, occupation, and educational attainment. These are not questions that normally open up inquiry. [1] For this reason, my practice has been to include a separate “demographic questionnaire” sheet that I ask each respondent to fill out at the conclusion of the interview. Only include those aspects that are relevant to your study. For example, if you are not exploring religion or religious affiliation, do not include questions about a person’s religion on the demographic sheet. See the example provided at the end of this chapter.

Temporality

Any type of question can have a past, present, or future orientation. For example, if you are asking a behavior question about workplace routine, you might ask the respondent to talk about past work, present work, and ideal (future) work. Similarly, if you want to understand how people cope with natural disasters, you might ask your respondent how they felt then during the wildfire and now in retrospect and whether and to what extent they have concerns for future wildfire disasters. It’s a relatively simple suggestion—don’t forget to ask about past, present, and future—but it can have a big impact on the quality of the responses you receive.

Question Sequence

Having a list of good questions or good question areas is not enough to make a good interview guide. You will want to pay attention to the order in which you ask your questions. Even though any one respondent can derail this order (perhaps by jumping to answer a question you haven’t yet asked), a good advance plan is always helpful. When thinking about sequence, remember that your goal is to get your respondent to open up to you and to say things that might surprise you. To establish rapport, it is best to start with nonthreatening questions. Asking about the present is often the safest place to begin, followed by the past (they have to know you a little bit to get there), and lastly, the future (talking about hopes and fears requires the most rapport). To allow for surprises, it is best to move from very general questions to more particular questions only later in the interview. This ensures that respondents have the freedom to bring up the topics that are relevant to them rather than feel like they are constrained to answer you narrowly. For example, refrain from asking about particular emotions until these have come up previously—don’t lead with them. Often, your more particular questions will emerge only during the course of the interview, tailored to what is emerging in conversation.

Once you have a set of questions, read through them aloud and imagine you are being asked the same questions. Does the set of questions have a natural flow? Would you be willing to answer the very first question to a total stranger? Does your sequence establish facts and experiences before moving on to opinions and values? Did you include prefatory statements, where necessary; transitions; and other announcements? These can be as simple as “Hey, we talked a lot about your experiences as a barista while in college.… Now I am turning to something completely different: how you managed friendships in college.” That is an abrupt transition, but it has been softened by your acknowledgment of that.

Probes and Flexibility

Once you have the interview guide, you will also want to leave room for probes and follow-up questions. As in the sample probe included here, you can write out the obvious probes and follow-up questions in advance. You might not need them, as your respondent might anticipate them and include full responses to the original question. Or you might need to tailor them to how your respondent answered the question. Some common probes and follow-up questions include asking for more details (When did that happen? Who else was there?), asking for elaboration (Could you say more about that?), asking for clarification (Does that mean what I think it means or something else? I understand what you mean, but someone else reading the transcript might not), and asking for contrast or comparison (How did this experience compare with last year’s event?). “Probing is a skill that comes from knowing what to look for in the interview, listening carefully to what is being said and what is not said, and being sensitive to the feedback needs of the person being interviewed” ( Patton 2002:374 ). It takes work! And energy. I and many other interviewers I know report feeling emotionally and even physically drained after conducting an interview. You are tasked with active listening and rearranging your interview guide as needed on the fly. If you only ask the questions written down in your interview guide with no deviations, you are doing it wrong. [2]

The Final Question

Every interview guide should include a very open-ended final question that allows for the respondent to say whatever it is they have been dying to tell you but you’ve forgotten to ask. About half the time they are tired too and will tell you they have nothing else to say. But incredibly, some of the most honest and complete responses take place here, at the end of a long interview. You have to realize that the person being interviewed is often discovering things about themselves as they talk to you and that this process of discovery can lead to new insights for them. Making space at the end is therefore crucial. Be sure you convey that you actually do want them to tell you more, that the offer of “anything else?” is not read as an empty convention where the polite response is no. Here is where you can pull from that active listening and tailor the final question to the particular person. For example, “I’ve asked you a lot of questions about what it was like to live through that wildfire. I’m wondering if there is anything I’ve forgotten to ask, especially because I haven’t had that experience myself” is a much more inviting final question than “Great. Anything you want to add?” It’s also helpful to convey to the person that you have the time to listen to their full answer, even if the allotted time is at the end. After all, there are no more questions to ask, so the respondent knows exactly how much time is left. Do them the courtesy of listening to them!

Conducting the Interview

Once you have your interview guide, you are on your way to conducting your first interview. I always practice my interview guide with a friend or family member. I do this even when the questions don’t make perfect sense for them, as it still helps me realize which questions make no sense, are poorly worded (too academic), or don’t follow sequentially. I also practice the routine I will use for interviewing, which goes something like this:

Introduce myself and reintroduce the study
Provide consent form and ask them to sign and retain/return copy
Ask if they have any questions about the study before we begin
Ask if I can begin recording
Ask questions (from interview guide)
Turn off the recording device
Ask if they are willing to fill out my demographic questionnaire
Collect questionnaire and, without looking at the answers, place in same folder as signed consent form
Thank them and depart

A note on remote interviewing: Interviews have traditionally been conducted face-to-face in a private or quiet public setting. You don’t want a lot of background noise, as this will make transcriptions difficult. During the recent global pandemic, many interviewers, myself included, learned the benefits of interviewing remotely. Although face-to-face is still preferable for many reasons, Zoom interviewing is not a bad alternative, and it does allow more interviews across great distances. Zoom also includes automatic transcription, which significantly cuts down on the time it normally takes to convert our conversations into “data” to be analyzed. These automatic transcriptions are not perfect, however, and you will still need to listen to the recording and clarify and clean up the transcription. Nor do automatic transcriptions include notations of body language or change of tone, which you may want to include. When interviewing remotely, you will want to collect the consent form before you meet: ask them to read, sign, and return it as an email attachment. I think it is better to ask for the demographic questionnaire after the interview, but because some respondents may never return it then, it is probably best to ask for this at the same time as the consent form, in advance of the interview.

What should you bring to the interview? I would recommend bringing two copies of the consent form (one for you and one for the respondent), a demographic questionnaire, a manila folder in which to place the signed consent form and filled-out demographic questionnaire, a printed copy of your interview guide (I print with three-inch right margins so I can jot down notes on the page next to relevant questions), a pen, a recording device, and water.

After the interview, you will want to secure the signed consent form in a locked filing cabinet (if in print) or a password-protected folder on your computer. Using Excel or a similar program that allows tables/spreadsheets, create an identifying number for your interview that links to the consent form without using the name of your respondent. For example, let’s say that I conduct interviews with US politicians, and the first person I meet with is George W. Bush. I will assign the transcription the number “INT#001” and add it to the signed consent form. [3] The signed consent form goes into a locked filing cabinet, and I never use the name “George W. Bush” again. I take the information from the demographic sheet, open my Excel spreadsheet, and add the relevant information in separate columns for the row INT#001: White, male, Republican. When I interview Bill Clinton as my second interview, I include a second row: INT#002: White, male, Democrat. And so on. The only link to the actual name of the respondent and this information is the fact that the consent form (unavailable to anyone but me) has stamped on it the interview number.

Many students get very nervous before their first interview. Actually, many of us are always nervous before the interview! But do not worry—this is normal, and it does pass. Chances are, you will be pleasantly surprised at how comfortable it begins to feel. These “purposeful conversations” are often a delight for both participants. This is not to say that sometimes things go wrong. I often have my students practice several “bad scenarios” (e.g., a respondent that you cannot get to open up; a respondent who is too talkative and dominates the conversation, steering it away from the topics you are interested in; emotions that completely take over; or shocking disclosures you are ill-prepared to handle), but most of the time, things go quite well. Be prepared for the unexpected, but know that the reason interviews are so popular as a technique of data collection is that they are usually richly rewarding for both participants.

One thing that I stress to my methods students and remind myself about is that interviews are still conversations between people. If there’s something you might feel uncomfortable asking someone about in a “normal” conversation, you will likely also feel a bit of discomfort asking it in an interview. Maybe more importantly, your respondent may feel uncomfortable. Social research—especially about inequality—can be uncomfortable. And it’s easy to slip into an abstract, intellectualized, or removed perspective as an interviewer. This is one reason trying out interview questions is important. Another is that sometimes the question sounds good in your head but doesn’t work as well out loud in practice. I learned this the hard way when a respondent asked me how I would answer the question I had just posed, and I realized that not only did I not really know how I would answer it, but I also wasn’t quite as sure I knew what I was asking as I had thought.

—Elizabeth M. Lee, Associate Professor of Sociology at Saint Joseph’s University, author of Class and Campus Life , and co-author of Geographies of Campus Inequality

How Many Interviews?

Your research design has included a targeted number of interviews and a recruitment plan (see chapter 5). Follow your plan, but remember that “ saturation ” is your goal. You interview as many people as you can until you reach a point at which you are no longer surprised by what they tell you. This means not that no one after your first twenty interviews will have surprising, interesting stories to tell you but rather that the picture you are forming about the phenomenon of interest to you from a research perspective has come into focus, and none of the interviews are substantially refocusing that picture. That is when you should stop collecting interviews. Note that to know when you have reached this, you will need to read your transcripts as you go. More about this in chapters 18 and 19.

Your Final Product: The Ideal Interview Transcript

A good interview transcript will demonstrate a subtly controlled conversation by the skillful interviewer. In general, you want to see replies that are about one paragraph long, not short sentences and not running on for several pages. Although it is sometimes necessary to follow respondents down tangents, it is also often necessary to pull them back to the questions that form the basis of your research study. This is not really a free conversation, although it may feel like that to the person you are interviewing.

Final Tips from an Interview Master

Annette Lareau is arguably one of the masters of the trade. In Listening to People , she provides several guidelines for good interviews and then offers a detailed example of an interview gone wrong and how it could be addressed (please see the “Further Readings” at the end of this chapter). Here is an abbreviated version of her set of guidelines: (1) interview respondents who are experts on the subjects of most interest to you (as a corollary, don’t ask people about things they don’t know); (2) listen carefully and talk as little as possible; (3) keep in mind what you want to know and why you want to know it; (4) be a proactive interviewer (subtly guide the conversation); (5) assure respondents that there aren’t any right or wrong answers; (6) use the respondent’s own words to probe further (this both allows you to accurately identify what you heard and pushes the respondent to explain further); (7) reuse effective probes (don’t reinvent the wheel as you go—if repeating the words back works, do it again and again); (8) focus on learning the subjective meanings that events or experiences have for a respondent; (9) don’t be afraid to ask a question that draws on your own knowledge (unlike trial lawyers who are trained never to ask a question for which they don’t already know the answer, sometimes it’s worth it to ask risky questions based on your hypotheses or just plain hunches); (10) keep thinking while you are listening (so difficult…and important); (11) return to a theme raised by a respondent if you want further information; (12) be mindful of power inequalities (and never ever coerce a respondent to continue the interview if they want out); (13) take control with overly talkative respondents; (14) expect overly succinct responses, and develop strategies for probing further; (15) balance digging deep and moving on; (16) develop a plan to deflect questions (e.g., let them know you are happy to answer any questions at the end of the interview, but you don’t want to take time away from them now); and at the end, (17) check to see whether you have asked all your questions. You don’t always have to ask everyone the same set of questions, but if there is a big area you have forgotten to cover, now is the time to recover ( Lareau 2021:93–103 ).

Sample: Demographic Questionnaire

ASA Taskforce on First-Generation and Working-Class Persons in Sociology – Class Effects on Career Success

Supplementary Demographic Questionnaire

Thank you for your participation in this interview project. We would like to collect a few pieces of key demographic information from you to supplement our analyses. Your answers to these questions will be kept confidential and stored by ID number. All of your responses here are entirely voluntary!

What best captures your race/ethnicity? (please check any/all that apply)

White (Non Hispanic/Latina/o/x)
Black or African American
Hispanic, Latino/a/x of Spanish
Asian or Asian American
American Indian or Alaska Native
Middle Eastern or North African
Native Hawaiian or Pacific Islander
Other : (Please write in: ________________)

What is your current position?

Grad Student
Full Professor

Please check any and all of the following that apply to you:

I identify as a working-class academic
I was the first in my family to graduate from college
I grew up poor

What best reflects your gender?

Transgender female/Transgender woman
Transgender male/Transgender man
Gender queer/ Gender nonconforming

Anything else you would like us to know about you?

Example: Interview Guide

In this example, follow-up prompts are italicized. Note the sequence of questions. That second question often elicits an entire life history , answering several later questions in advance.

Introduction Script/Question

Thank you for participating in our survey of ASA members who identify as first-generation or working-class. As you may have heard, ASA has sponsored a taskforce on first-generation and working-class persons in sociology and we are interested in hearing from those who so identify. Your participation in this interview will help advance our knowledge in this area.

The first thing we would like to as you is why you have volunteered to be part of this study? What does it mean to you be first-gen or working class? Why were you willing to be interviewed?
How did you decide to become a sociologist?
Can you tell me a little bit about where you grew up? ( prompts: what did your parent(s) do for a living? What kind of high school did you attend?)
Has this identity been salient to your experience? (how? How much?)
How welcoming was your grad program? Your first academic employer?
Why did you decide to pursue sociology at the graduate level?
Did you experience culture shock in college? In graduate school?
Has your FGWC status shaped how you’ve thought about where you went to school? debt? etc?
Were you mentored? How did this work (not work)? How might it?
What did you consider when deciding where to go to grad school? Where to apply for your first position?
What, to you, is a mark of career success? Have you achieved that success? What has helped or hindered your pursuit of success?
Do you think sociology, as a field, cares about prestige?
Let’s talk a little bit about intersectionality. How does being first-gen/working class work alongside other identities that are important to you?
What do your friends and family think about your career? Have you had any difficulty relating to family members or past friends since becoming highly educated?
Do you have any debt from college/grad school? Are you concerned about this? Could you explain more about how you paid for college/grad school? (here, include assistance from family, fellowships, scholarships, etc.)
(You’ve mentioned issues or obstacles you had because of your background.) What could have helped? Or, who or what did? Can you think of fortuitous moments in your career?
Do you have any regrets about the path you took?
Is there anything else you would like to add? Anything that the Taskforce should take note of, that we did not ask you about here?

Qualitative Research Design: Start

Qualitative Research Design

What is Qualitative research design?

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much . It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which involves collecting and analyzing numerical data for statistical analysis. Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Research Paradigms

Positivist versus Post-Positivist
Social Constructivist (this paradigm/ideology mostly birth qualitative studies)

Events Relating to the Qualitative Research and Community Engagement Workshops @ CMU Libraries

CMU Libraries is committed to helping members of our community become data experts. To that end, CMU is offering public facing workshops that discuss Qualitative Research, Coding, and Community Engagement best practices.

The following workshops are a part of a broader series on using data. Please follow the links to register for the events.

Qualitative Coding

Using Community Data to improve Outcome (Grant Writing)

Survey Design

Upcoming Event: March 21st, 2024 (12:00pm -1:00 pm)

Community Engagement and Collaboration Event

Join us for an event to improve, build on and expand the connections between Carnegie Mellon University resources and the Pittsburgh community. CMU resources such as the Libraries and Sustainability Initiative can be leveraged by users not affiliated with the university, but barriers can prevent them from fully engaging.

The conversation features representatives from CMU departments and local organizations about the community engagement efforts currently underway at CMU and opportunities to improve upon them. Speakers will highlight current and ongoing projects and share resources to support future collaboration.

Event Moderators:

Taiwo Lasisi, CLIR Postdoctoral Fellow in Community Data Literacy, Carnegie Mellon University Libraries

Emma Slayton, Data Curation, Visualization, & GIS Specialist, Carnegie Mellon University Libraries

Nicky Agate , Associate Dean for Academic Engagement, Carnegie Mellon University Libraries

Chelsea Cohen , The University’s Executive fellow for community engagement, Carnegie Mellon University

Sarah Ceurvorst , Academic Pathways Manager, Program Director, LEAP (Leadership, Excellence, Access, Persistence) Carnegie Mellon University

Julia Poeppibg , Associate Director of Partnership Development, Information Systems, Carnegie Mellon University

Scott Wolovich , Director of New Sun Rising, Pittsburgh

Additional workshops and events will be forthcoming. Watch this space for updates.

Workshop Organizer

Qualitative Research Methods

What are Qualitative Research methods?

Qualitative research adopts numerous methods or techniques including interviews, focus groups, and observation. Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one-on-one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be participant observers to share the experiences of the subject or non-participant or detached observers.

What constitutes a good research question? Does the question drive research design choices?

According to Doody and Bailey (2014);

We can only develop a good research question by consulting relevant literature, colleagues, and supervisors experienced in the area of research. (inductive interactions).

Helps to have a directed research aim and objective.

Researchers should not be “ research trendy” and have enough evidence. This is why research objectives are important. It helps to take time, and resources into consideration.

Research questions can be developed from theoretical knowledge, previous research or experience, or a practical need at work (Parahoo 2014). They have numerous roles, such as identifying the importance of the research and providing clarity of purpose for the research, in terms of what the research intends to achieve in the end.

Qualitative Research Questions

What constitutes a good Qualitative research question?

A good qualitative question answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. Qualitative research gathers participants' experiences, perceptions and behavior.

Examples of good Qualitative Research Questions:

What are people's thoughts on the new library?

How does it feel to be a first-generation student attending college?

Difference example (between Qualitative and Quantitative research questions):

How many college students signed up for the new semester? (Quan)

How do college students feel about the new semester? What are their experiences so far? (Qual)

Qualitative Research Design Workshop Powerpoint

Foley G, Timonen V. Using Grounded Theory Method to Capture and Analyze Health Care Experiences. Health Serv Res. 2015 Aug;50(4):1195-210. [ PMC free article: PMC4545354 ] [ PubMed: 25523315 ]

Devers KJ. How will we know "good" qualitative research when we see it? Beginning the dialogue in health services research. Health Serv Res. 1999 Dec;34(5 Pt 2):1153-88. [ PMC free article: PMC1089058 ] [ PubMed: 10591278 ]

Huston P, Rowan M. Qualitative studies. Their role in medical research. Can Fam Physician. 1998 Nov;44:2453-8. [ PMC free article: PMC2277956 ] [ PubMed: 9839063 ]

Corner EJ, Murray EJ, Brett SJ. Qualitative, grounded theory exploration of patients' experience of early mobilisation, rehabilitation and recovery after critical illness. BMJ Open. 2019 Feb 24;9(2):e026348. [ PMC free article: PMC6443050 ] [ PubMed: 30804034 ]

Moser A, Korstjens I. Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis. Eur J Gen Pract. 2018 Dec;24(1):9-18. [ PMC free article: PMC5774281 ] [ PubMed: 29199486 ]

Houghton C, Murphy K, Meehan B, Thomas J, Brooker D, Casey D. From screening to synthesis: using nvivo to enhance transparency in qualitative evidence synthesis. J Clin Nurs. 2017 Mar;26(5-6):873-881. [ PubMed: 27324875 ]

Soratto J, Pires DEP, Friese S. Thematic content analysis using ATLAS.ti software: Potentialities for researchs in health. Rev Bras Enferm. 2020;73(3):e20190250. [ PubMed: 32321144 ]

Zamawe FC. The Implication of Using NVivo Software in Qualitative Data Analysis: Evidence-Based Reflections. Malawi Med J. 2015 Mar;27(1):13-5. [ PMC free article: PMC4478399 ] [ PubMed: 26137192 ]

Korstjens I, Moser A. Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. Eur J Gen Pract. 2018 Dec;24(1):120-124. [ PMC free article: PMC8816392 ] [ PubMed: 29202616 ]

Saldaña, J. (2021). The coding manual for qualitative researchers. The coding manual for qualitative researchers, 1-440.

O'Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. 2014 Sep;89(9):1245-51. [ PubMed: 24979285 ]

Palermo C, King O, Brock T, Brown T, Crampton P, Hall H, Macaulay J, Morphet J, Mundy M, Oliaro L, Paynter S, Williams B, Wright C, E Rees C. Setting priorities for health education research: A mixed methods study. Med Teach. 2019 Sep;41(9):1029-1038. [ PubMed: 31141390 ]

Last Updated: Feb 14, 2024 4:25 PM
URL: https://guides.library.cmu.edu/c.php?g=1346006

Harvard Library
Research Guides
Faculty of Arts & Sciences Libraries

Library Support for Qualitative Research

Interview Research

General Handbooks and Overviews

Qualitative research communities.

Types of Interviews
Recruiting & Engaging Participants
Interview Questions
Conducting Interviews
Recording & Transcription
Data Analysis
Managing Interview Data
Finding Extant Interviews
Past Workshops on Interview Research
Methodological Resources
Remote & Virtual Fieldwork
Data Management & Repositories
Campus Access
Interviews as a Method for Qualitative Research (video) This short video summarizes why interviews can serve as useful data in qualitative research.
InterViews by Steinar Kvale Interviewing is an essential tool in qualitative research and this introduction to interviewing outlines both the theoretical underpinnings and the practical aspects of the process. After examining the role of the interview in the research process, Steinar Kvale considers some of the key philosophical issues relating to interviewing: the interview as conversation, hermeneutics, phenomenology, concerns about ethics as well as validity, and postmodernism. Having established this framework, the author then analyzes the seven stages of the interview process - from designing a study to writing it up.
Practical Evaluation by Michael Quinn Patton Surveys different interviewing strategies, from, a) informal/conversational, to b) interview guide approach, to c) standardized and open-ended, to d) closed/quantitative. Also discusses strategies for wording questions that are open-ended, clear, sensitive, and neutral, while supporting the speaker. Provides suggestions for probing and maintaining control of the interview process, as well as suggestions for recording and transcription.
The SAGE Handbook of Interview Research by Amir B. Marvasti (Editor); James A. Holstein (Editor); Jaber F. Gubrium (Editor); Karyn D. McKinney (Editor) The new edition of this landmark volume emphasizes the dynamic, interactional, and reflexive dimensions of the research interview. Contributors highlight the myriad dimensions of complexity that are emerging as researchers increasingly frame the interview as a communicative opportunity as much as a data-gathering format. The book begins with the history and conceptual transformations of the interview, which is followed by chapters that discuss the main components of interview practice. Taken together, the contributions to The SAGE Handbook of Interview Research: The Complexity of the Craft encourage readers simultaneously to learn the frameworks and technologies of interviewing and to reflect on the epistemological foundations of the interview craft.
International Congress of Qualitative Inquiry They host an annual confrerence at the University of Illinois at Urbana-Champaign, which aims to facilitate the development of qualitative research methods across a wide variety of academic disciplines, among other initiatives.
METHODSPACE An online home of the research methods community, where practicing researchers share how to make research easier.
Social Research Association, UK The SRA is the membership organisation for social researchers in the UK and beyond. It supports researchers via training, guidance, publications, research ethics, events, branches, and careers.
Social Science Research Council The SSRC administers fellowships and research grants that support the innovation and evaluation of new policy solutions. They convene researchers and stakeholders to share evidence-based policy solutions and incubate new research agendas, produce online knowledge platforms and technical reports that catalog research-based policy solutions, and support mentoring programs that broaden problem-solving research opportunities.
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Qualitative Interview Questions: Guidance for Novice Researchers

September 2020
The Qualitative Report
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Qualitative study design: Interviews

Qualitative study design
Phenomenology
Grounded theory
Ethnography
Narrative inquiry
Action research
Case Studies
Field research
Focus groups
Observation
Surveys & questionnaires
Study Designs Home

Interviews are intended to find out the experiences, understandings, opinions, or motivations of participants. The relationship between the interviewer and interviewee is crucial to the success of the research interview; the interviewer builds an environment of trust with the interviewee/s, guiding the interviewee/s through a set of topics or questions to be discussed in depth.

Interviews are the most commonly used qualitative data gathering technique and are used with grounded theory, focus groups, and case studies.

Interviews are purposive conversations between the researcher and the interviewee, either alone or as part of a group
Interviews can be face to face, via telecommunications (Skype, Facetime, or phone), or via email (internet or email interview)
The length of an interview varies. They may be anywhere from thirty minutes to several hours in length, depending on your research approach
Structured interviews use a set list of questions which need to be asked in order, increasing the reliability and credibility of the data but decreasing responsiveness to interviewee/s. Structured interviews are like a verbal survey
Unstructured interviews are where the interviewer has a set list of topics to address but no predetermined questions. This increases the flexibility of the interview but decreases the reliability of the data. Unstructured interviews may be used in long-term field observation research
Semi-structured interviews are the middle ground. Semi-structured interviews require the interviewer to have a list of questions and topics pre-prepared, which can be asked in different ways with different interviewee/s. Semi-structured interviews increase the flexibility and the responsiveness of the interview while keeping the interview on track, increasing the reliability and credibility of the data. Semi-structured interviews are one of the most common interview techniques.
Flexible – probing questions can be asked, and the order of questions changed, depending on the participant and how structured or unstructured the interview is
Quick way to collect data
Familiarity – most interviewees are familiar with the concept of an interview and are comfortable with this research approach

Limitations

Not all participants are equally articulate or perceptive
Questions must be worded carefully to reduce response bias
Transcription of interviews can be time and labour intensive

Example questions

What are the experiences of midwives in providing care to high-risk mothers, where there is a history of drug or alcohol use?

Example studies

Sandelin, A., Kalman, S., Gustafsson, B. (2019). Prerequisites for safe intraoperative nursing care and teamwork – operating theatre nurses’ perspectives: a qualitative interview study, Journal of Clinical Nursing, 28, 2635-2643. Doi: 10.1111/jocn.14850

Babbie, E. (2008). The basics of social research (4th ed). Belmont: Thomson Wadsworth

Creswell, J.W. & Creswell, J.D. (2018). Research design: Qualitative, quantitative and mixed methods approaches (5th ed). Thousand Oaks: SAGE

Jamshed, S. (2014). Qualitative research method-interviewing and observation. Journal of basic and clinical pharmacy, 5(4), 87-88. doi:10.4103/0976-0105.141942

Lindlof, T. & Taylor, B. (2002). Qualitative communication research methods (2nd ed). Thousand Oaks: SAGE .

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Appendix: Qualitative Interview Design

Daniel W. Turner III and Nicole Hagstrom-Schmidt

Qualitative Interview Design: A Practical Guide for Novice Investigators

Qualitative research design can be complicated depending upon the level of experience a researcher may have with a particular type of methodology. As researchers, many aspire to grow and expand their knowledge and experiences with qualitative design in order to better utilize a variety of research paradigms. One of the more popular areas of interest in qualitative research design is that of the interview protocol. Interviews provide in-depth information pertaining to participants’ experiences and viewpoints of a particular topic. Oftentimes, interviews are coupled with other forms of data collection in order to provide the researcher with a well-rounded collection of information for analyses. This paper explores the effective ways to conduct in-depth, qualitative interviews for novice investigators by expanding upon the practical components of each interview design.

Categories of Qualitative Interview Design

As common with quantitative analyses, there are various forms of interview design that can be developed to obtain thick, rich data utilizing a qualitative investigational perspective. [1] For the purpose of this examination, there are three formats for interview design that will be explored which are summarized by Gall, Gall, and Borg:

Informal conversational interview,
General interview guide approach,
Standardized open-ended interview. [2]

In addition, I will expand on some suggestions for conducting qualitative interviews which includes the construction of research questions as well as the analysis of interview data. These suggestions come from both my personal experiences with interviewing as well as the recommendations from the literature to assist novice interviewers.

Informal Conversational Interview

The informal conversational interview is outlined by Gall, Gall, and Borg for the purpose of relying “…entirely on the spontaneous generation of questions in a natural interaction, typically one that occurs as part of ongoing participant observation fieldwork.” [3] I am curious when it comes to other cultures or religions and I enjoy immersing myself in these environments as an active participant. I ask questions in order to learn more about these social settings without having a predetermined set of structured questions. Primarily the questions come from “in the moment experiences” as a means for further understanding or clarification of what I am witnessing or experiencing at a particular moment. With the informal conversational approach, the researcher does not ask any specific types of questions, but rather relies on the interaction with the participants to guide the interview process. [4] Think of this type of interview as an “off the top of your head” style of interview where you really construct questions as you move forward. Many consider this type of interview beneficial because of the lack of structure, which allows for flexibility in the nature of the interview. However, many researchers view this type of interview as unstable or unreliable because of the inconsistency in the interview questions, thus making it difficult to code data. [5] If you choose to conduct an informal conversational interview, it is critical to understand the need for flexibility and originality in the questioning as a key for success.

General Interview Guide Approach

The general interview guide approach is more structured than the informal conversational interview although there is still quite a bit of flexibility in its composition. [6] The ways that questions are potentially worded depend upon the researcher who is conducting the interview. Therefore, one of the obvious issues with this type of interview is the lack of consistency in the way research questions are posed because researchers can interchange the way he or she poses them. With that in mind, the respondents may not consistently answer the same question(s) based on how they were posed by the interviewer. [7] During research for my doctoral dissertation, I was able to interact with alumni participants in a relaxed and informal manner where I had the opportunity to learn more about the in-depth experiences of the participants through structured interviews. This informal environment allowed me the opportunity to develop rapport with the participants so that I was able to ask follow-up or probing questions based on their responses to pre-constructed questions. I found this quite useful in my interviews because I could ask questions or change questions based on participant responses to previous questions. The questions were structured, but adapting them allowed me to explore a more personal approach to each alumni interview.

According to McNamara, the strength of the general interview guide approach is the ability of the researcher “…to ensure that the same general areas of information are collected from each interviewee; this provides more focus than the conversational approach, but still allows a degree of freedom and adaptability in getting information from the interviewee.” [8] The researcher remains in the driver’s seat with this type of interview approach, but flexibility takes precedence based on perceived prompts from the participants.

You might ask, “What does this mean anyway?” The easiest way to answer that question is to think about your own personal experiences at a job interview. When you were invited to a job interview in the past, you might have prepared for all sorts of curve ball-style questions to come your way. You desired an answer for every potential question. If the interviewer were asking you questions using a general interview guide approach, he or she would ask questions using their own unique style, which might differ from the way the questions were originally created. You as the interviewee would then respond to those questions in the manner in which the interviewer asked which would dictate how the interview continued. Based on how the interviewer asked the question(s), you might have been able to answer more information or less information than that of other job candidates. Therefore, it is easy to see how this could positively or negatively influence a job candidate if the interviewer were using a general interview guide approach.

Standardized Open-Ended Interviews

The standardized open-ended interview is extremely structured in terms of the wording of the questions. Participants are always asked identical questions, but the questions are worded so that responses are open-ended. [9] This open-endedness allows the participants to contribute as much detailed information as they desire and it also allows the researcher to ask probing questions as a means of follow-up. Standardized open-ended interviews are likely the most popular form of interviewing utilized in research studies because of the nature of the open-ended questions, allowing the participants to fully express their viewpoints and experiences. If one were to identify weaknesses with open-ended interviewing, they would likely identify the difficulty with coding the data. [10] Since open-ended interviews in composition call for participants to fully express their responses in as much detail as desired, it can be quite difficult for researchers to extract similar themes or codes from the interview transcripts as they would with less open-ended responses. Although the data provided by participants are rich and thick with qualitative data, it can be a more cumbersome process for the researcher to sift through the narrative responses in order to fully and accurately reflect an overall perspective of all interview responses through the coding process. However, according to Gall, Gall, and Borg, this reduces researcher biases within the study, particularly when the interviewing process involves many participants. [11]

Suggestions for Conducting Qualitative Interviews

Now that we know a few of the more popular interview designs that are available to qualitative researchers, we can more closely examine various suggestions for conducting qualitative interviews based on the available research. These suggestions are designed to provide the researcher with the tools needed to conduct a well constructed, professional interview with their participants. Some of the most common information found within the literature relating to interviews, according to Creswell [12] :

The preparation for the interview,
The constructing effective research questions,
The actual implementation of the interview(s). [13]

Preparation for the Interview

Probably the most helpful tip with the interview process is that of interview preparation. This process can help make or break the process and can either alleviate or exacerbate the problematic circumstances that could potentially occur once the research is implemented. McNamara suggests the importance of the preparation stage in order to maintain an unambiguous focus as to how the interviews will be erected in order to provide maximum benefit to the proposed research study. [14] Along these lines Chenail provides a number of pre-interview exercises researchers can use to improve their instrumentality and address potential biases. [15] McNamara applies eight principles to the preparation stage of interviewing which includes the following ingredients:

Choose a setting with little distraction;
Explain the purpose of the interview;
Address terms of confidentiality;
Explain the format of the interview;
Indicate how long the interview usually takes;
Tell them how to get in touch with you later if they want to;
Ask them if they have any questions before you both get started with the interview;
Don’t count on your memory to recall their answers. [16]

Selecting Participants

Creswell discusses the importance of selecting the appropriate candidates for interviews. He asserts that the researcher should utilize one of the various types of sampling strategies such as criterion based sampling or critical case sampling (among many others) in order to obtain qualified candidates that will provide the most credible information to the study. [17] Creswell also suggests the importance of acquiring participants who will be willing to openly and honestly share information or “their story.” [18] It might be easier to conduct the interviews with participants in a comfortable environment where the participants do not feel restricted or uncomfortable to share information.

Pilot Testing

Another important element to the interview preparation is the implementation of a pilot test. The pilot test will assist the research in determining if there are flaws, limitations, or other weaknesses within the interview design and will allow him or her to make necessary revisions prior to the implementation of the study. [19] A pilot test should be conducted with participants that have similar interests as those that will participate in the implemented study. The pilot test will also assist the researchers with the refinement of research questions, which will be discussed in the next section.

Constructing Effective Research Questions

Creating effective research questions for the interview process is one of the most crucial components to interview design. Researchers desiring to conduct such an investigation should be careful that each of the questions will allow the examiner to dig deep into the experiences and/or knowledge of the participants in order to gain maximum data from the interviews. McNamara suggests several recommendations for creating effective research questions for interviews which includes the following elements:

Wording should be open-ended (respondents should be able to choose their own terms when answering questions);
Questions should be as neutral as possible (avoid wording that might influence answers, e.g., evocative, judgmental wording);
Questions should be asked one at a time;
Questions should be worded clearly (this includes knowing any terms particular to the program or the respondents’ culture); and
Be careful asking “why” questions. [20]

Examples of Useful and Not-So Useful Research Questions

To assist the novice interviewer with the preparation of research questions, I will propose a useful research question and a not so useful research question. Based on McNamara’s suggestion, it is important to ask an open-ended question. [21] So for the useful question, I will propose the following: “How have your experiences as a kindergarten teacher influenced or not influenced you in the decisions that you have made in raising your children”? As you can see, the question allows the respondent to discuss how his or her experiences as a kindergarten teacher have or have not affected their decision-making with their own children without making the assumption that the experience has influenced their decision-making. On the other hand, if you were to ask a similar question, but from a less than useful perspective, you might construct the same question in this manner: “How has your experiences as a kindergarten teacher affected you as a parent”? As you can see, the question is still open-ended, but it makes the assumption that the experiences have indeed affected them as a parent. We as the researcher cannot make this assumption in the wording of our questions.

Follow-Up Questions

Creswell also makes the suggestion of being flexible with research questions being constructed. [22] He makes the assertion that respondents in an interview will not necessarily answer the question being asked by the researcher and, in fact, may answer a question that is asked in another question later in the interview. Creswell believes that the researcher must construct questions in such a manner to keep participants on focus with their responses to the questions. In addition, the researcher must be prepared with follow-up questions or prompts in order to ensure that they obtain optimal responses from participants. When I was an Assistant Director for a large division at my University a couple of years ago, I was tasked with the responsibility of hiring student affairs coordinators at our off-campus educational centers. Throughout the interviewing process, I found that interviewees did indeed get off topic with certain questions because they either misunderstood the question(s) being asked or did not wish to answer the question(s) directly. I was able to utilize Creswell’s suggestion [23] by reconstructing questions so that they were clearly assembled in a manner to reduce misunderstanding and was able to erect effective follow-up prompts to further understanding. This alleviated many of the problems I had and assisted me in extracting the information I needed from the interview through my follow-up questioning.

Implementation of Interviews

As with other sections of interview design, McNamara makes some excellent recommendations for the implementation stage of the interview process. He includes the following tips for interview implementation:

Occasionally verify the tape recorder (if used) is working;
Ask one question at a time;
Attempt to remain as neutral as possible (that is, don’t show strong emotional reactions to their responses;
Encourage responses with occasional nods of the head, “uh huh”s, etc.;
Be careful about the appearance when note taking (that is, if you jump to take a note, it may appear as if you’re surprised or very pleased about an answer, which may influence answers to future questions);
Provide transition between major topics, e.g., “we’ve been talking about (some topic) and now I’d like to move on to (another topic);”
Don’t lose control of the interview (this can occur when respondents stray to another topic, take so long to answer a question that times begins to run out, or even begin asking questions to the interviewer). [24]

Interpreting Data

The final constituent in the interview design process is that of interpreting the data that was gathered during the interview process. During this phase, the researcher must make “sense” out of what was just uncovered and compile the data into sections or groups of information, also known as themes or codes. [25] These themes or codes are consistent phrases, expressions, or ideas that were common among research participants. [26] How the researcher formulates themes or codes vary. Many researchers suggest the need to employ a third party consultant who can review codes or themes in order to determine the quality and effectiveness based on their evaluation of the interview transcripts. [27] This helps alleviate researcher biases or potentially eliminate where over-analyzing of data has occurred. Many researchers may choose to employ an iterative review process where a committee of nonparticipating researchers can provide constructive feedback and suggestions to the researcher(s) primarily involved with the study.

From choosing the appropriate type of interview design process through the interpretation of interview data, this guide for conducting qualitative research interviews proposes a practical way to perform an investigation based on the recommendations and experiences of qualified researchers in the field and through my own personal experiences. Although qualitative investigation provides a myriad of opportunities for conducting investigational research, interview design has remained one of the more popular forms of analyses. As the variety of qualitative research methods become more widely utilized across research institutions, we will continue to see more practical guides for protocol implementation outlined in peer reviewed journals across the world.

This text was derived from

Turner, Daniel W., III. “Qualitative Interview Design: A Practical Guide for Novice Investigators.” The Qualitative Report 15, no. 3 (2010): 754-760. https://doi.org/10.46743/2160-3715/2010.1178 . Licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 International License .

It is edited and reformatted by Nicole Hagstrom-Schmidt.

John W. Creswell, Qualitative Inquiry and Research Design: Choosing Among Five Approaches , 2nd ed. (Thousand Oaks, CA: Sage, 2007). ↵
M.D. Gall, Walter R. Borg, and Joyce P. Gall, Educational Research: An Introduction , 7th ed. (Boston, MA: Pearson, 2003). ↵
M.D. Gall, Walter R. Borg, and Joyce P. Gall, Educational Research: An Introduction , 7th ed (Boston, MA: Pearson, 2003), 239. ↵
Carter McNamara, “General Guidelines for Conducting Interviews,” Free Management Library , accessed January 11, 2010, https://managementhelp.org/businessresearch/interviews.htm. ↵
M.D. Gall, Walter R. Borg, and Joyce P. Gall, Educational Research: An Introduction , 7th ed (Boston, MA: Pearson, 2003). ↵
Carter McNamara, “General Guidelines for Conducting Interviews,” Free Management Library , accessed January 11, 2010, https://managementhelp.org/businessresearch/interviews.htm . ↵
Carter McNamara, “General Guidelines for Conducting Interviews,” Free Management Library , “Types of Interviews” section, para. 1, accessed January 11, 2010, https://managementhelp.org/businessresearch/interviews.htm . ↵
John W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches , 3rd ed. (Thousand Oaks, CA: Sage, 2003); John W. Creswell, Qualitative Inquiry and Research Design: Choosing Among Five Approaches , 2nd ed. (Thousand Oaks, CA: Sage, 2007). ↵
Ronald J. Chenail, “Interviewing the Investigator: Strategies for Addressing Instrumentation and Researcher Bias Concerns in Qualitative Research,” The Qualitative Report 16, no. 1 (2011): 255–262, https://nsuworks.nova.edu/tqr/vol16/iss1/16/ . ↵
Carter McNamara, “General Guidelines for Conducting Interviews,” Free Management Library , “Preparation for Interview section,” para. 1, accessed January 11, 2010, https://managementhelp.org/businessresearch/interviews.htm . ↵
John W. Creswell, Qualitative Inquiry and Research Design: Choosing Among Five Approaches , 2nd ed. (Thousand Oaks, CA: Sage, 2007), 133. ↵
Steinar Kvale, Doing Interviews (London and Thousand Oaks, CA: Sage, 2007) https://doi.org/10.4135/9781849208963 . ↵
Carter McNamara, “General Guidelines for Conducting Interviews,” Free Management Library , “Wording of Questions” section, para. 1, accessed January 11, 2010, https://managementhelp.org/businessresearch/interviews.htm . ↵
Carter McNamara, “General Guidelines for Conducting Interviews,” Free Management Library , “Conducting Interview” section, para 1, accessed January 11, 2010, https://managementhelp.org/businessresearch/interviews.htm . ↵
Steinar Kvale, Doing Interviews (London and Thousand Oaks, CA: Sage, 2007) https://doi.org/10.4135/9781849208963 ↵

Appendix: Qualitative Interview Design Copyright © 2022 by Daniel W. Turner III and Nicole Hagstrom-Schmidt is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Qualitative Research 101: Interviewing

5 Common Mistakes To Avoid When Undertaking Interviews

By: David Phair (PhD) and Kerryn Warren (PhD) | March 2022

Undertaking interviews is potentially the most important step in the qualitative research process. If you don’t collect useful, useable data in your interviews, you’ll struggle through the rest of your dissertation or thesis. Having helped numerous students with their research over the years, we’ve noticed some common interviewing mistakes that first-time researchers make. In this post, we’ll discuss five costly interview-related mistakes and outline useful strategies to avoid making these.

Overview: 5 Interviewing Mistakes

Not having a clear interview strategy /plan
Not having good interview techniques /skills
Not securing a suitable location and equipment
Not having a basic risk management plan
Not keeping your “ golden thread ” front of mind

1. Not having a clear interview strategy

The first common mistake that we’ll look at is that of starting the interviewing process without having first come up with a clear interview strategy or plan of action. While it’s natural to be keen to get started engaging with your interviewees, a lack of planning can result in a mess of data and inconsistency between interviews.

There are several design choices to decide on and plan for before you start interviewing anyone. Some of the most important questions you need to ask yourself before conducting interviews include:

What are the guiding research aims and research questions of my study?
Will I use a structured, semi-structured or unstructured interview approach?
How will I record the interviews (audio or video)?
Who will be interviewed and by whom ?
What ethics and data law considerations do I need to adhere to?
How will I analyze my data?

Let’s take a quick look at some of these.

The core objective of the interviewing process is to generate useful data that will help you address your overall research aims. Therefore, your interviews need to be conducted in a way that directly links to your research aims, objectives and research questions (i.e. your “golden thread”). This means that you need to carefully consider the questions you’ll ask to ensure that they align with and feed into your golden thread. If any question doesn’t align with this, you may want to consider scrapping it.

Another important design choice is whether you’ll use an unstructured, semi-structured or structured interview approach . For semi-structured interviews, you will have a list of questions that you plan to ask and these questions will be open-ended in nature. You’ll also allow the discussion to digress from the core question set if something interesting comes up. This means that the type of information generated might differ a fair amount between interviews.

Contrasted to this, a structured approach to interviews is more rigid, where a specific set of closed questions is developed and asked for each interviewee in exactly the same order. Closed questions have a limited set of answers, that are often single-word answers. Therefore, you need to think about what you’re trying to achieve with your research project (i.e. your research aims) and decided on which approach would be best suited in your case.

It is also important to plan ahead with regards to who will be interviewed and how. You need to think about how you will approach the possible interviewees to get their cooperation, who will conduct the interviews, when to conduct the interviews and how to record the interviews. For each of these decisions, it’s also essential to make sure that all ethical considerations and data protection laws are taken into account.

Finally, you should think through how you plan to analyze the data (i.e., your qualitative analysis method) generated by the interviews. Different types of analysis rely on different types of data, so you need to ensure you’re asking the right types of questions and correctly guiding your respondents.

Simply put, you need to have a plan of action regarding the specifics of your interview approach before you start collecting data. If not, you’ll end up drifting in your approach from interview to interview, which will result in inconsistent, unusable data.

Your interview questions need to directly link to your research aims, objectives and research questions - your "golden thread”.

2. Not having good interview technique

While you’re generally not expected to become you to be an expert interviewer for a dissertation or thesis, it is important to practice good interview technique and develop basic interviewing skills .

Let’s go through some basics that will help the process along.

Firstly, before the interview , make sure you know your interview questions well and have a clear idea of what you want from the interview. Naturally, the specificity of your questions will depend on whether you’re taking a structured, semi-structured or unstructured approach, but you still need a consistent starting point . Ideally, you should develop an interview guide beforehand (more on this later) that details your core question and links these to the research aims, objectives and research questions.

Before you undertake any interviews, it’s a good idea to do a few mock interviews with friends or family members. This will help you get comfortable with the interviewer role, prepare for potentially unexpected answers and give you a good idea of how long the interview will take to conduct. In the interviewing process, you’re likely to encounter two kinds of challenging interviewees ; the two-word respondent and the respondent who meanders and babbles. Therefore, you should prepare yourself for both and come up with a plan to respond to each in a way that will allow the interview to continue productively.

To begin the formal interview , provide the person you are interviewing with an overview of your research. This will help to calm their nerves (and yours) and contextualize the interaction. Ultimately, you want the interviewee to feel comfortable and be willing to be open and honest with you, so it’s useful to start in a more casual, relaxed fashion and allow them to ask any questions they may have. From there, you can ease them into the rest of the questions.

As the interview progresses , avoid asking leading questions (i.e., questions that assume something about the interviewee or their response). Make sure that you speak clearly and slowly , using plain language and being ready to paraphrase questions if the person you are interviewing misunderstands. Be particularly careful with interviewing English second language speakers to ensure that you’re both on the same page.

Engage with the interviewee by listening to them carefully and acknowledging that you are listening to them by smiling or nodding. Show them that you’re interested in what they’re saying and thank them for their openness as appropriate. This will also encourage your interviewee to respond openly.

Need a helping hand?

3. Not securing a suitable location and quality equipment

Where you conduct your interviews and the equipment you use to record them both play an important role in how the process unfolds. Therefore, you need to think carefully about each of these variables before you start interviewing.

Poor location: A bad location can result in the quality of your interviews being compromised, interrupted, or cancelled. If you are conducting physical interviews, you’ll need a location that is quiet, safe, and welcoming . It’s very important that your location of choice is not prone to interruptions (the workplace office is generally problematic, for example) and has suitable facilities (such as water, a bathroom, and snacks).

If you are conducting online interviews , you need to consider a few other factors. Importantly, you need to make sure that both you and your respondent have access to a good, stable internet connection and electricity. Always check before the time that both of you know how to use the relevant software and it’s accessible (sometimes meeting platforms are blocked by workplace policies or firewalls). It’s also good to have alternatives in place (such as WhatsApp, Zoom, or Teams) to cater for these types of issues.

Poor equipment: Using poor-quality recording equipment or using equipment incorrectly means that you will have trouble transcribing, coding, and analyzing your interviews. This can be a major issue , as some of your interview data may go completely to waste if not recorded well. So, make sure that you use good-quality recording equipment and that you know how to use it correctly.

To avoid issues, you should always conduct test recordings before every interview to ensure that you can use the relevant equipment properly. It’s also a good idea to spot check each recording afterwards, just to make sure it was recorded as planned. If your equipment uses batteries, be sure to always carry a spare set.

Where you conduct your interviews and the equipment you use to record them play an important role in how the process unfolds.

4. Not having a basic risk management plan

Many possible issues can arise during the interview process. Not planning for these issues can mean that you are left with compromised data that might not be useful to you. Therefore, it’s important to map out some sort of risk management plan ahead of time, considering the potential risks, how you’ll minimize their probability and how you’ll manage them if they materialize.

Common potential issues related to the actual interview include cancellations (people pulling out), delays (such as getting stuck in traffic), language and accent differences (especially in the case of poor internet connections), issues with internet connections and power supply. Other issues can also occur in the interview itself. For example, the interviewee could drift off-topic, or you might encounter an interviewee who does not say much at all.

You can prepare for these potential issues by considering possible worst-case scenarios and preparing a response for each scenario. For instance, it is important to plan a backup date just in case your interviewee cannot make it to the first meeting you scheduled with them. It’s also a good idea to factor in a 30-minute gap between your interviews for the instances where someone might be late, or an interview runs overtime for other reasons. Make sure that you also plan backup questions that could be used to bring a respondent back on topic if they start rambling, or questions to encourage those who are saying too little.

In general, it’s best practice to plan to conduct more interviews than you think you need (this is called oversampling ). Doing so will allow you some room for error if there are interviews that don’t go as planned, or if some interviewees withdraw. If you need 10 interviews, it is a good idea to plan for 15. Likely, a few will cancel , delay, or not produce useful data.

You should consider all the potential risks, how you’ll reduce their probability and how you'll respond if they do indeed materialize.

5. Not keeping your golden thread front of mind

We touched on this a little earlier, but it is a key point that should be central to your entire research process. You don’t want to end up with pages and pages of data after conducting your interviews and realize that it is not useful to your research aims . Your research aims, objectives and research questions – i.e., your golden thread – should influence every design decision and should guide the interview process at all times.

A useful way to avoid this mistake is by developing an interview guide before you begin interviewing your respondents. An interview guide is a document that contains all of your questions with notes on how each of the interview questions is linked to the research question(s) of your study. You can also include your research aims and objectives here for a more comprehensive linkage.

You can easily create an interview guide by drawing up a table with one column containing your core interview questions . Then add another column with your research questions , another with expectations that you may have in light of the relevant literature and another with backup or follow-up questions . As mentioned, you can also bring in your research aims and objectives to help you connect them all together. If you’d like, you can download a copy of our free interview guide here .

Recap: Qualitative Interview Mistakes

In this post, we’ve discussed 5 common costly mistakes that are easy to make in the process of planning and conducting qualitative interviews.

To recap, these include:

If you have any questions about these interviewing mistakes, drop a comment below. Alternatively, if you’re interested in getting 1-on-1 help with your thesis or dissertation , check out our dissertation coaching service or book a free initial consultation with one of our friendly Grad Coaches.

Psst... there’s more!

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Published: 05 October 2018

Interviews and focus groups in qualitative research: an update for the digital age

P. Gill 1 &
J. Baillie 2

British Dental Journal volume 225 , pages 668–672 ( 2018 ) Cite this article

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Highlights that qualitative research is used increasingly in dentistry. Interviews and focus groups remain the most common qualitative methods of data collection.

Suggests the advent of digital technologies has transformed how qualitative research can now be undertaken.

Suggests interviews and focus groups can offer significant, meaningful insight into participants' experiences, beliefs and perspectives, which can help to inform developments in dental practice.

Qualitative research is used increasingly in dentistry, due to its potential to provide meaningful, in-depth insights into participants' experiences, perspectives, beliefs and behaviours. These insights can subsequently help to inform developments in dental practice and further related research. The most common methods of data collection used in qualitative research are interviews and focus groups. While these are primarily conducted face-to-face, the ongoing evolution of digital technologies, such as video chat and online forums, has further transformed these methods of data collection. This paper therefore discusses interviews and focus groups in detail, outlines how they can be used in practice, how digital technologies can further inform the data collection process, and what these methods can offer dentistry.

You have full access to this article via your institution.

Interviews in the social sciences

Professionalism in dentistry: deconstructing common terminology

A review of technical and quality assessment considerations of audio-visual and web-conferencing focus groups in qualitative health research, introduction.

Traditionally, research in dentistry has primarily been quantitative in nature. 1 However, in recent years, there has been a growing interest in qualitative research within the profession, due to its potential to further inform developments in practice, policy, education and training. Consequently, in 2008, the British Dental Journal (BDJ) published a four paper qualitative research series, 2 , 3 , 4 , 5 to help increase awareness and understanding of this particular methodological approach.

Since the papers were originally published, two scoping reviews have demonstrated the ongoing proliferation in the use of qualitative research within the field of oral healthcare. 1 , 6 To date, the original four paper series continue to be well cited and two of the main papers remain widely accessed among the BDJ readership. 2 , 3 The potential value of well-conducted qualitative research to evidence-based practice is now also widely recognised by service providers, policy makers, funding bodies and those who commission, support and use healthcare research.

Besides increasing standalone use, qualitative methods are now also routinely incorporated into larger mixed method study designs, such as clinical trials, as they can offer additional, meaningful insights into complex problems that simply could not be provided by quantitative methods alone. Qualitative methods can also be used to further facilitate in-depth understanding of important aspects of clinical trial processes, such as recruitment. For example, Ellis et al . investigated why edentulous older patients, dissatisfied with conventional dentures, decline implant treatment, despite its established efficacy, and frequently refuse to participate in related randomised clinical trials, even when financial constraints are removed. 7 Through the use of focus groups in Canada and the UK, the authors found that fears of pain and potential complications, along with perceived embarrassment, exacerbated by age, are common reasons why older patients typically refuse dental implants. 7

The last decade has also seen further developments in qualitative research, due to the ongoing evolution of digital technologies. These developments have transformed how researchers can access and share information, communicate and collaborate, recruit and engage participants, collect and analyse data and disseminate and translate research findings. 8 Where appropriate, such technologies are therefore capable of extending and enhancing how qualitative research is undertaken. 9 For example, it is now possible to collect qualitative data via instant messaging, email or online/video chat, using appropriate online platforms.

These innovative approaches to research are therefore cost-effective, convenient, reduce geographical constraints and are often useful for accessing 'hard to reach' participants (for example, those who are immobile or socially isolated). 8 , 9 However, digital technologies are still relatively new and constantly evolving and therefore present a variety of pragmatic and methodological challenges. Furthermore, given their very nature, their use in many qualitative studies and/or with certain participant groups may be inappropriate and should therefore always be carefully considered. While it is beyond the scope of this paper to provide a detailed explication regarding the use of digital technologies in qualitative research, insight is provided into how such technologies can be used to facilitate the data collection process in interviews and focus groups.

In light of such developments, it is perhaps therefore timely to update the main paper 3 of the original BDJ series. As with the previous publications, this paper has been purposely written in an accessible style, to enhance readability, particularly for those who are new to qualitative research. While the focus remains on the most common qualitative methods of data collection – interviews and focus groups – appropriate revisions have been made to provide a novel perspective, and should therefore be helpful to those who would like to know more about qualitative research. This paper specifically focuses on undertaking qualitative research with adult participants only.

Overview of qualitative research

Qualitative research is an approach that focuses on people and their experiences, behaviours and opinions. 10 , 11 The qualitative researcher seeks to answer questions of 'how' and 'why', providing detailed insight and understanding, 11 which quantitative methods cannot reach. 12 Within qualitative research, there are distinct methodologies influencing how the researcher approaches the research question, data collection and data analysis. 13 For example, phenomenological studies focus on the lived experience of individuals, explored through their description of the phenomenon. Ethnographic studies explore the culture of a group and typically involve the use of multiple methods to uncover the issues. 14

While methodology is the 'thinking tool', the methods are the 'doing tools'; 13 the ways in which data are collected and analysed. There are multiple qualitative data collection methods, including interviews, focus groups, observations, documentary analysis, participant diaries, photography and videography. Two of the most commonly used qualitative methods are interviews and focus groups, which are explored in this article. The data generated through these methods can be analysed in one of many ways, according to the methodological approach chosen. A common approach is thematic data analysis, involving the identification of themes and subthemes across the data set. Further information on approaches to qualitative data analysis has been discussed elsewhere. 1

Qualitative research is an evolving and adaptable approach, used by different disciplines for different purposes. Traditionally, qualitative data, specifically interviews, focus groups and observations, have been collected face-to-face with participants. In more recent years, digital technologies have contributed to the ongoing evolution of qualitative research. Digital technologies offer researchers different ways of recruiting participants and collecting data, and offer participants opportunities to be involved in research that is not necessarily face-to-face.

Research interviews are a fundamental qualitative research method 15 and are utilised across methodological approaches. Interviews enable the researcher to learn in depth about the perspectives, experiences, beliefs and motivations of the participant. 3 , 16 Examples include, exploring patients' perspectives of fear/anxiety triggers in dental treatment, 17 patients' experiences of oral health and diabetes, 18 and dental students' motivations for their choice of career. 19

Interviews may be structured, semi-structured or unstructured, 3 according to the purpose of the study, with less structured interviews facilitating a more in depth and flexible interviewing approach. 20 Structured interviews are similar to verbal questionnaires and are used if the researcher requires clarification on a topic; however they produce less in-depth data about a participant's experience. 3 Unstructured interviews may be used when little is known about a topic and involves the researcher asking an opening question; 3 the participant then leads the discussion. 20 Semi-structured interviews are commonly used in healthcare research, enabling the researcher to ask predetermined questions, 20 while ensuring the participant discusses issues they feel are important.

Interviews can be undertaken face-to-face or using digital methods when the researcher and participant are in different locations. Audio-recording the interview, with the consent of the participant, is essential for all interviews regardless of the medium as it enables accurate transcription; the process of turning the audio file into a word-for-word transcript. This transcript is the data, which the researcher then analyses according to the chosen approach.

Types of interview

Qualitative studies often utilise one-to-one, face-to-face interviews with research participants. This involves arranging a mutually convenient time and place to meet the participant, signing a consent form and audio-recording the interview. However, digital technologies have expanded the potential for interviews in research, enabling individuals to participate in qualitative research regardless of location.

Telephone interviews can be a useful alternative to face-to-face interviews and are commonly used in qualitative research. They enable participants from different geographical areas to participate and may be less onerous for participants than meeting a researcher in person. 15 A qualitative study explored patients' perspectives of dental implants and utilised telephone interviews due to the quality of the data that could be yielded. 21 The researcher needs to consider how they will audio record the interview, which can be facilitated by purchasing a recorder that connects directly to the telephone. One potential disadvantage of telephone interviews is the inability of the interviewer and researcher to see each other. This is resolved using software for audio and video calls online – such as Skype – to conduct interviews with participants in qualitative studies. Advantages of this approach include being able to see the participant if video calls are used, enabling observation of non-verbal communication, and the software can be free to use. However, participants are required to have a device and internet connection, as well as being computer literate, potentially limiting who can participate in the study. One qualitative study explored the role of dental hygienists in reducing oral health disparities in Canada. 22 The researcher conducted interviews using Skype, which enabled dental hygienists from across Canada to be interviewed within the research budget, accommodating the participants' schedules. 22

A less commonly used approach to qualitative interviews is the use of social virtual worlds. A qualitative study accessed a social virtual world – Second Life – to explore the health literacy skills of individuals who use social virtual worlds to access health information. 23 The researcher created an avatar and interview room, and undertook interviews with participants using voice and text methods. 23 This approach to recruitment and data collection enables individuals from diverse geographical locations to participate, while remaining anonymous if they wish. Furthermore, for interviews conducted using text methods, transcription of the interview is not required as the researcher can save the written conversation with the participant, with the participant's consent. However, the researcher and participant need to be familiar with how the social virtual world works to engage in an interview this way.

Conducting an interview

Ensuring informed consent before any interview is a fundamental aspect of the research process. Participants in research must be afforded autonomy and respect; consent should be informed and voluntary. 24 Individuals should have the opportunity to read an information sheet about the study, ask questions, understand how their data will be stored and used, and know that they are free to withdraw at any point without reprisal. The qualitative researcher should take written consent before undertaking the interview. In a face-to-face interview, this is straightforward: the researcher and participant both sign copies of the consent form, keeping one each. However, this approach is less straightforward when the researcher and participant do not meet in person. A recent protocol paper outlined an approach for taking consent for telephone interviews, which involved: audio recording the participant agreeing to each point on the consent form; the researcher signing the consent form and keeping a copy; and posting a copy to the participant. 25 This process could be replicated in other interview studies using digital methods.

There are advantages and disadvantages of using face-to-face and digital methods for research interviews. Ultimately, for both approaches, the quality of the interview is determined by the researcher. 16 Appropriate training and preparation are thus required. Healthcare professionals can use their interpersonal communication skills when undertaking a research interview, particularly questioning, listening and conversing. 3 However, the purpose of an interview is to gain information about the study topic, 26 rather than offering help and advice. 3 The researcher therefore needs to listen attentively to participants, enabling them to describe their experience without interruption. 3 The use of active listening skills also help to facilitate the interview. 14 Spradley outlined elements and strategies for research interviews, 27 which are a useful guide for qualitative researchers:

Greeting and explaining the project/interview

Asking descriptive (broad), structural (explore response to descriptive) and contrast (difference between) questions

Asymmetry between the researcher and participant talking

Expressing interest and cultural ignorance

Repeating, restating and incorporating the participant's words when asking questions

Creating hypothetical situations

Asking friendly questions

Knowing when to leave.

For semi-structured interviews, a topic guide (also called an interview schedule) is used to guide the content of the interview – an example of a topic guide is outlined in Box 1 . The topic guide, usually based on the research questions, existing literature and, for healthcare professionals, their clinical experience, is developed by the research team. The topic guide should include open ended questions that elicit in-depth information, and offer participants the opportunity to talk about issues important to them. This is vital in qualitative research where the researcher is interested in exploring the experiences and perspectives of participants. It can be useful for qualitative researchers to pilot the topic guide with the first participants, 10 to ensure the questions are relevant and understandable, and amending the questions if required.

Regardless of the medium of interview, the researcher must consider the setting of the interview. For face-to-face interviews, this could be in the participant's home, in an office or another mutually convenient location. A quiet location is preferable to promote confidentiality, enable the researcher and participant to concentrate on the conversation, and to facilitate accurate audio-recording of the interview. For interviews using digital methods the same principles apply: a quiet, private space where the researcher and participant feel comfortable and confident to participate in an interview.

Box 1: Example of a topic guide

Study focus: Parents' experiences of brushing their child's (aged 0–5) teeth

1. Can you tell me about your experience of cleaning your child's teeth?

How old was your child when you started cleaning their teeth?

Why did you start cleaning their teeth at that point?

How often do you brush their teeth?

What do you use to brush their teeth and why?

2. Could you explain how you find cleaning your child's teeth?

Do you find anything difficult?

What makes cleaning their teeth easier for you?

3. How has your experience of cleaning your child's teeth changed over time?

Has it become easier or harder?

Have you changed how often and how you clean their teeth? If so, why?

4. Could you describe how your child finds having their teeth cleaned?

What do they enjoy about having their teeth cleaned?

Is there anything they find upsetting about having their teeth cleaned?

5. Where do you look for information/advice about cleaning your child's teeth?

What did your health visitor tell you about cleaning your child's teeth? (If anything)

What has the dentist told you about caring for your child's teeth? (If visited)

Have any family members given you advice about how to clean your child's teeth? If so, what did they tell you? Did you follow their advice?

6. Is there anything else you would like to discuss about this?

Focus groups

A focus group is a moderated group discussion on a pre-defined topic, for research purposes. 28 , 29 While not aligned to a particular qualitative methodology (for example, grounded theory or phenomenology) as such, focus groups are used increasingly in healthcare research, as they are useful for exploring collective perspectives, attitudes, behaviours and experiences. Consequently, they can yield rich, in-depth data and illuminate agreement and inconsistencies 28 within and, where appropriate, between groups. Examples include public perceptions of dental implants and subsequent impact on help-seeking and decision making, 30 and general dental practitioners' views on patient safety in dentistry. 31

Focus groups can be used alone or in conjunction with other methods, such as interviews or observations, and can therefore help to confirm, extend or enrich understanding and provide alternative insights. 28 The social interaction between participants often results in lively discussion and can therefore facilitate the collection of rich, meaningful data. However, they are complex to organise and manage, due to the number of participants, and may also be inappropriate for exploring particularly sensitive issues that many participants may feel uncomfortable about discussing in a group environment.

Focus groups are primarily undertaken face-to-face but can now also be undertaken online, using appropriate technologies such as email, bulletin boards, online research communities, chat rooms, discussion forums, social media and video conferencing. 32 Using such technologies, data collection can also be synchronous (for example, online discussions in 'real time') or, unlike traditional face-to-face focus groups, asynchronous (for example, online/email discussions in 'non-real time'). While many of the fundamental principles of focus group research are the same, regardless of how they are conducted, a number of subtle nuances are associated with the online medium. 32 Some of which are discussed further in the following sections.

Focus group considerations

Some key considerations associated with face-to-face focus groups are: how many participants are required; should participants within each group know each other (or not) and how many focus groups are needed within a single study? These issues are much debated and there is no definitive answer. However, the number of focus groups required will largely depend on the topic area, the depth and breadth of data needed, the desired level of participation required 29 and the necessity (or not) for data saturation.

The optimum group size is around six to eight participants (excluding researchers) but can work effectively with between three and 14 participants. 3 If the group is too small, it may limit discussion, but if it is too large, it may become disorganised and difficult to manage. It is, however, prudent to over-recruit for a focus group by approximately two to three participants, to allow for potential non-attenders. For many researchers, particularly novice researchers, group size may also be informed by pragmatic considerations, such as the type of study, resources available and moderator experience. 28 Similar size and mix considerations exist for online focus groups. Typically, synchronous online focus groups will have around three to eight participants but, as the discussion does not happen simultaneously, asynchronous groups may have as many as 10–30 participants. 33

The topic area and potential group interaction should guide group composition considerations. Pre-existing groups, where participants know each other (for example, work colleagues) may be easier to recruit, have shared experiences and may enjoy a familiarity, which facilitates discussion and/or the ability to challenge each other courteously. 3 However, if there is a potential power imbalance within the group or if existing group norms and hierarchies may adversely affect the ability of participants to speak freely, then 'stranger groups' (that is, where participants do not already know each other) may be more appropriate. 34 , 35

Focus group management

Face-to-face focus groups should normally be conducted by two researchers; a moderator and an observer. 28 The moderator facilitates group discussion, while the observer typically monitors group dynamics, behaviours, non-verbal cues, seating arrangements and speaking order, which is essential for transcription and analysis. The same principles of informed consent, as discussed in the interview section, also apply to focus groups, regardless of medium. However, the consent process for online discussions will probably be managed somewhat differently. For example, while an appropriate participant information leaflet (and consent form) would still be required, the process is likely to be managed electronically (for example, via email) and would need to specifically address issues relating to technology (for example, anonymity and use, storage and access to online data). 32

The venue in which a face to face focus group is conducted should be of a suitable size, private, quiet, free from distractions and in a collectively convenient location. It should also be conducted at a time appropriate for participants, 28 as this is likely to promote attendance. As with interviews, the same ethical considerations apply (as discussed earlier). However, online focus groups may present additional ethical challenges associated with issues such as informed consent, appropriate access and secure data storage. Further guidance can be found elsewhere. 8 , 32

Before the focus group commences, the researchers should establish rapport with participants, as this will help to put them at ease and result in a more meaningful discussion. Consequently, researchers should introduce themselves, provide further clarity about the study and how the process will work in practice and outline the 'ground rules'. Ground rules are designed to assist, not hinder, group discussion and typically include: 3 , 28 , 29

Discussions within the group are confidential to the group

Only one person can speak at a time

All participants should have sufficient opportunity to contribute

There should be no unnecessary interruptions while someone is speaking

Everyone can be expected to be listened to and their views respected

Challenging contrary opinions is appropriate, but ridiculing is not.

Moderating a focus group requires considered management and good interpersonal skills to help guide the discussion and, where appropriate, keep it sufficiently focused. Avoid, therefore, participating, leading, expressing personal opinions or correcting participants' knowledge 3 , 28 as this may bias the process. A relaxed, interested demeanour will also help participants to feel comfortable and promote candid discourse. Moderators should also prevent the discussion being dominated by any one person, ensure differences of opinions are discussed fairly and, if required, encourage reticent participants to contribute. 3 Asking open questions, reflecting on significant issues, inviting further debate, probing responses accordingly, and seeking further clarification, as and where appropriate, will help to obtain sufficient depth and insight into the topic area.

Moderating online focus groups requires comparable skills, particularly if the discussion is synchronous, as the discussion may be dominated by those who can type proficiently. 36 It is therefore important that sufficient time and respect is accorded to those who may not be able to type as quickly. Asynchronous discussions are usually less problematic in this respect, as interactions are less instant. However, moderating an asynchronous discussion presents additional challenges, particularly if participants are geographically dispersed, as they may be online at different times. Consequently, the moderator will not always be present and the discussion may therefore need to occur over several days, which can be difficult to manage and facilitate and invariably requires considerable flexibility. 32 It is also worth recognising that establishing rapport with participants via online medium is often more challenging than via face-to-face and may therefore require additional time, skills, effort and consideration.

As with research interviews, focus groups should be guided by an appropriate interview schedule, as discussed earlier in the paper. For example, the schedule will usually be informed by the review of the literature and study aims, and will merely provide a topic guide to help inform subsequent discussions. To provide a verbatim account of the discussion, focus groups must be recorded, using an audio-recorder with a good quality multi-directional microphone. While videotaping is possible, some participants may find it obtrusive, 3 which may adversely affect group dynamics. The use (or not) of a video recorder, should therefore be carefully considered.

At the end of the focus group, a few minutes should be spent rounding up and reflecting on the discussion. 28 Depending on the topic area, it is possible that some participants may have revealed deeply personal issues and may therefore require further help and support, such as a constructive debrief or possibly even referral on to a relevant third party. It is also possible that some participants may feel that the discussion did not adequately reflect their views and, consequently, may no longer wish to be associated with the study. 28 Such occurrences are likely to be uncommon, but should they arise, it is important to further discuss any concerns and, if appropriate, offer them the opportunity to withdraw (including any data relating to them) from the study. Immediately after the discussion, researchers should compile notes regarding thoughts and ideas about the focus group, which can assist with data analysis and, if appropriate, any further data collection.

Qualitative research is increasingly being utilised within dental research to explore the experiences, perspectives, motivations and beliefs of participants. The contributions of qualitative research to evidence-based practice are increasingly being recognised, both as standalone research and as part of larger mixed-method studies, including clinical trials. Interviews and focus groups remain commonly used data collection methods in qualitative research, and with the advent of digital technologies, their utilisation continues to evolve. However, digital methods of qualitative data collection present additional methodological, ethical and practical considerations, but also potentially offer considerable flexibility to participants and researchers. Consequently, regardless of format, qualitative methods have significant potential to inform important areas of dental practice, policy and further related research.

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Gill, P., Baillie, J. Interviews and focus groups in qualitative research: an update for the digital age. Br Dent J 225 , 668–672 (2018). https://doi.org/10.1038/sj.bdj.2018.815

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Qualitative Research: Characteristics, Design, Methods & Examples

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On This Page:

Qualitative research is a type of research methodology that focuses on gathering and analyzing non-numerical data to gain a deeper understanding of human behavior, experiences, and perspectives.

It aims to explore the “why” and “how” of a phenomenon rather than the “what,” “where,” and “when” typically addressed by quantitative research.

Unlike quantitative research, which focuses on gathering and analyzing numerical data for statistical analysis, qualitative research involves researchers interpreting data to identify themes, patterns, and meanings.

Qualitative research can be used to:

Gain deep contextual understandings of the subjective social reality of individuals
To answer questions about experience and meaning from the participant’s perspective
To design hypotheses, theory must be researched using qualitative methods to determine what is important before research can begin.

Examples of qualitative research questions include:

How does stress influence young adults’ behavior?
What factors influence students’ school attendance rates in developed countries?
How do adults interpret binge drinking in the UK?
What are the psychological impacts of cervical cancer screening in women?
How can mental health lessons be integrated into the school curriculum?

Characteristics

Naturalistic setting.

Individuals are studied in their natural setting to gain a deeper understanding of how people experience the world. This enables the researcher to understand a phenomenon close to how participants experience it.

Naturalistic settings provide valuable contextual information to help researchers better understand and interpret the data they collect.

The environment, social interactions, and cultural factors can all influence behavior and experiences, and these elements are more easily observed in real-world settings.

Reality is socially constructed

Qualitative research aims to understand how participants make meaning of their experiences – individually or in social contexts. It assumes there is no objective reality and that the social world is interpreted (Yilmaz, 2013).

The primacy of subject matter

The primary aim of qualitative research is to understand the perspectives, experiences, and beliefs of individuals who have experienced the phenomenon selected for research rather than the average experiences of groups of people (Minichiello, 1990).

An in-depth understanding is attained since qualitative techniques allow participants to freely disclose their experiences, thoughts, and feelings without constraint (Tenny et al., 2022).

Variables are complex, interwoven, and difficult to measure

Factors such as experiences, behaviors, and attitudes are complex and interwoven, so they cannot be reduced to isolated variables , making them difficult to measure quantitatively.

However, a qualitative approach enables participants to describe what, why, or how they were thinking/ feeling during a phenomenon being studied (Yilmaz, 2013).

Emic (insider’s point of view)

The phenomenon being studied is centered on the participants’ point of view (Minichiello, 1990).

Emic is used to describe how participants interact, communicate, and behave in the research setting (Scarduzio, 2017).

Interpretive analysis

In qualitative research, interpretive analysis is crucial in making sense of the collected data.

This process involves examining the raw data, such as interview transcripts, field notes, or documents, and identifying the underlying themes, patterns, and meanings that emerge from the participants’ experiences and perspectives.

Collecting Qualitative Data

There are four main research design methods used to collect qualitative data: observations, interviews, focus groups, and ethnography.

Observations

This method involves watching and recording phenomena as they occur in nature. Observation can be divided into two types: participant and non-participant observation.

In participant observation, the researcher actively participates in the situation/events being observed.

In non-participant observation, the researcher is not an active part of the observation and tries not to influence the behaviors they are observing (Busetto et al., 2020).

Observations can be covert (participants are unaware that a researcher is observing them) or overt (participants are aware of the researcher’s presence and know they are being observed).

However, awareness of an observer’s presence may influence participants’ behavior.

Interviews give researchers a window into the world of a participant by seeking their account of an event, situation, or phenomenon. They are usually conducted on a one-to-one basis and can be distinguished according to the level at which they are structured (Punch, 2013).

Structured interviews involve predetermined questions and sequences to ensure replicability and comparability. However, they are unable to explore emerging issues.

Informal interviews consist of spontaneous, casual conversations which are closer to the truth of a phenomenon. However, information is gathered using quick notes made by the researcher and is therefore subject to recall bias.

Semi-structured interviews have a flexible structure, phrasing, and placement so emerging issues can be explored (Denny & Weckesser, 2022).

The use of probing questions and clarification can lead to a detailed understanding, but semi-structured interviews can be time-consuming and subject to interviewer bias.

Focus groups

Similar to interviews, focus groups elicit a rich and detailed account of an experience. However, focus groups are more dynamic since participants with shared characteristics construct this account together (Denny & Weckesser, 2022).

A shared narrative is built between participants to capture a group experience shaped by a shared context.

The researcher takes on the role of a moderator, who will establish ground rules and guide the discussion by following a topic guide to focus the group discussions.

Typically, focus groups have 4-10 participants as a discussion can be difficult to facilitate with more than this, and this number allows everyone the time to speak.

Ethnography

Ethnography is a methodology used to study a group of people’s behaviors and social interactions in their environment (Reeves et al., 2008).

Data are collected using methods such as observations, field notes, or structured/ unstructured interviews.

The aim of ethnography is to provide detailed, holistic insights into people’s behavior and perspectives within their natural setting. In order to achieve this, researchers immerse themselves in a community or organization.

Due to the flexibility and real-world focus of ethnography, researchers are able to gather an in-depth, nuanced understanding of people’s experiences, knowledge and perspectives that are influenced by culture and society.

In order to develop a representative picture of a particular culture/ context, researchers must conduct extensive field work.

This can be time-consuming as researchers may need to immerse themselves into a community/ culture for a few days, or possibly a few years.

Qualitative Data Analysis Methods

Different methods can be used for analyzing qualitative data. The researcher chooses based on the objectives of their study.

The researcher plays a key role in the interpretation of data, making decisions about the coding, theming, decontextualizing, and recontextualizing of data (Starks & Trinidad, 2007).

Grounded theory

Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967 (Glaser & Strauss, 2017).

This methodology aims to develop theories (rather than test hypotheses) that explain a social process, action, or interaction (Petty et al., 2012). To inform the developing theory, data collection and analysis run simultaneously.

There are three key types of coding used in grounded theory: initial (open), intermediate (axial), and advanced (selective) coding.

Throughout the analysis, memos should be created to document methodological and theoretical ideas about the data. Data should be collected and analyzed until data saturation is reached and a theory is developed.

Content analysis

Content analysis was first used in the early twentieth century to analyze textual materials such as newspapers and political speeches.

Content analysis is a research method used to identify and analyze the presence and patterns of themes, concepts, or words in data (Vaismoradi et al., 2013).

This research method can be used to analyze data in different formats, which can be written, oral, or visual.

The goal of content analysis is to develop themes that capture the underlying meanings of data (Schreier, 2012).

Qualitative content analysis can be used to validate existing theories, support the development of new models and theories, and provide in-depth descriptions of particular settings or experiences.

The following six steps provide a guideline for how to conduct qualitative content analysis.

Define a Research Question : To start content analysis, a clear research question should be developed.
Identify and Collect Data : Establish the inclusion criteria for your data. Find the relevant sources to analyze.
Define the Unit or Theme of Analysis : Categorize the content into themes. Themes can be a word, phrase, or sentence.
Develop Rules for Coding your Data : Define a set of coding rules to ensure that all data are coded consistently.
Code the Data : Follow the coding rules to categorize data into themes.
Analyze the Results and Draw Conclusions : Examine the data to identify patterns and draw conclusions in relation to your research question.

Discourse analysis

Discourse analysis is a research method used to study written/ spoken language in relation to its social context (Wood & Kroger, 2000).

In discourse analysis, the researcher interprets details of language materials and the context in which it is situated.

Discourse analysis aims to understand the functions of language (how language is used in real life) and how meaning is conveyed by language in different contexts. Researchers use discourse analysis to investigate social groups and how language is used to achieve specific communication goals.

Different methods of discourse analysis can be used depending on the aims and objectives of a study. However, the following steps provide a guideline on how to conduct discourse analysis.

Define the Research Question : Develop a relevant research question to frame the analysis.
Gather Data and Establish the Context : Collect research materials (e.g., interview transcripts, documents). Gather factual details and review the literature to construct a theory about the social and historical context of your study.
Analyze the Content : Closely examine various components of the text, such as the vocabulary, sentences, paragraphs, and structure of the text. Identify patterns relevant to the research question to create codes, then group these into themes.
Review the Results : Reflect on the findings to examine the function of the language, and the meaning and context of the discourse.

Thematic analysis

Thematic analysis is a method used to identify, interpret, and report patterns in data, such as commonalities or contrasts.

Although the origin of thematic analysis can be traced back to the early twentieth century, understanding and clarity of thematic analysis is attributed to Braun and Clarke (2006).

Thematic analysis aims to develop themes (patterns of meaning) across a dataset to address a research question.

In thematic analysis, qualitative data is gathered using techniques such as interviews, focus groups, and questionnaires. Audio recordings are transcribed. The dataset is then explored and interpreted by a researcher to identify patterns.

This occurs through the rigorous process of data familiarisation, coding, theme development, and revision. These identified patterns provide a summary of the dataset and can be used to address a research question.

Themes are developed by exploring the implicit and explicit meanings within the data. Two different approaches are used to generate themes: inductive and deductive.

An inductive approach allows themes to emerge from the data. In contrast, a deductive approach uses existing theories or knowledge to apply preconceived ideas to the data.

Phases of Thematic Analysis

Braun and Clarke (2006) provide a guide of the six phases of thematic analysis. These phases can be applied flexibly to fit research questions and data.

Phase
1. Gather and transcribe data	Gather raw data, for example interviews or focus groups, and transcribe audio recordings fully
2. Familiarization with data	Read and reread all your data from beginning to end; note down initial ideas
3. Create initial codes	Start identifying preliminary codes which highlight important features of the data and may be relevant to the research question
4. Create new codes which encapsulate potential themes	Review initial codes and explore any similarities, differences, or contradictions to uncover underlying themes; create a map to visualize identified themes
5. Take a break then return to the data	Take a break and then return later to review themes
6. Evaluate themes for good fit	Last opportunity for analysis; check themes are supported and saturated with data

Template analysis

Template analysis refers to a specific method of thematic analysis which uses hierarchical coding (Brooks et al., 2014).

Template analysis is used to analyze textual data, for example, interview transcripts or open-ended responses on a written questionnaire.

To conduct template analysis, a coding template must be developed (usually from a subset of the data) and subsequently revised and refined. This template represents the themes identified by researchers as important in the dataset.

Codes are ordered hierarchically within the template, with the highest-level codes demonstrating overarching themes in the data and lower-level codes representing constituent themes with a narrower focus.

A guideline for the main procedural steps for conducting template analysis is outlined below.

Familiarization with the Data : Read (and reread) the dataset in full. Engage, reflect, and take notes on data that may be relevant to the research question.
Preliminary Coding : Identify initial codes using guidance from the a priori codes, identified before the analysis as likely to be beneficial and relevant to the analysis.
Organize Themes : Organize themes into meaningful clusters. Consider the relationships between the themes both within and between clusters.
Produce an Initial Template : Develop an initial template. This may be based on a subset of the data.
Apply and Develop the Template : Apply the initial template to further data and make any necessary modifications. Refinements of the template may include adding themes, removing themes, or changing the scope/title of themes.
Finalize Template : Finalize the template, then apply it to the entire dataset.

Frame analysis

Frame analysis is a comparative form of thematic analysis which systematically analyzes data using a matrix output.

Ritchie and Spencer (1994) developed this set of techniques to analyze qualitative data in applied policy research. Frame analysis aims to generate theory from data.

Frame analysis encourages researchers to organize and manage their data using summarization.

This results in a flexible and unique matrix output, in which individual participants (or cases) are represented by rows and themes are represented by columns.

Each intersecting cell is used to summarize findings relating to the corresponding participant and theme.

Frame analysis has five distinct phases which are interrelated, forming a methodical and rigorous framework.

Familiarization with the Data : Familiarize yourself with all the transcripts. Immerse yourself in the details of each transcript and start to note recurring themes.
Develop a Theoretical Framework : Identify recurrent/ important themes and add them to a chart. Provide a framework/ structure for the analysis.
Indexing : Apply the framework systematically to the entire study data.
Summarize Data in Analytical Framework : Reduce the data into brief summaries of participants’ accounts.
Mapping and Interpretation : Compare themes and subthemes and check against the original transcripts. Group the data into categories and provide an explanation for them.

Preventing Bias in Qualitative Research

To evaluate qualitative studies, the CASP (Critical Appraisal Skills Programme) checklist for qualitative studies can be used to ensure all aspects of a study have been considered (CASP, 2018).

The quality of research can be enhanced and assessed using criteria such as checklists, reflexivity, co-coding, and member-checking.

Co-coding

Relying on only one researcher to interpret rich and complex data may risk key insights and alternative viewpoints being missed. Therefore, coding is often performed by multiple researchers.

A common strategy must be defined at the beginning of the coding process (Busetto et al., 2020). This includes establishing a useful coding list and finding a common definition of individual codes.

Transcripts are initially coded independently by researchers and then compared and consolidated to minimize error or bias and to bring confirmation of findings.

Member checking

Member checking (or respondent validation) involves checking back with participants to see if the research resonates with their experiences (Russell & Gregory, 2003).

Data can be returned to participants after data collection or when results are first available. For example, participants may be provided with their interview transcript and asked to verify whether this is a complete and accurate representation of their views.

Participants may then clarify or elaborate on their responses to ensure they align with their views (Shenton, 2004).

This feedback becomes part of data collection and ensures accurate descriptions/ interpretations of phenomena (Mays & Pope, 2000).

Reflexivity in qualitative research

Reflexivity typically involves examining your own judgments, practices, and belief systems during data collection and analysis. It aims to identify any personal beliefs which may affect the research.

Reflexivity is essential in qualitative research to ensure methodological transparency and complete reporting. This enables readers to understand how the interaction between the researcher and participant shapes the data.

Depending on the research question and population being researched, factors that need to be considered include the experience of the researcher, how the contact was established and maintained, age, gender, and ethnicity.

These details are important because, in qualitative research, the researcher is a dynamic part of the research process and actively influences the outcome of the research (Boeije, 2014).

Reflexivity Example

Who you are and your characteristics influence how you collect and analyze data. Here is an example of a reflexivity statement for research on smoking. I am a 30-year-old white female from a middle-class background. I live in the southwest of England and have been educated to master’s level. I have been involved in two research projects on oral health. I have never smoked, but I have witnessed how smoking can cause ill health from my volunteering in a smoking cessation clinic. My research aspirations are to help to develop interventions to help smokers quit.

Establishing Trustworthiness in Qualitative Research

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability.

1. Credibility in Qualitative Research

Credibility refers to how accurately the results represent the reality and viewpoints of the participants.

To establish credibility in research, participants’ views and the researcher’s representation of their views need to align (Tobin & Begley, 2004).

To increase the credibility of findings, researchers may use data source triangulation, investigator triangulation, peer debriefing, or member checking (Lincoln & Guba, 1985).

2. Transferability in Qualitative Research

Transferability refers to how generalizable the findings are: whether the findings may be applied to another context, setting, or group (Tobin & Begley, 2004).

Transferability can be enhanced by giving thorough and in-depth descriptions of the research setting, sample, and methods (Nowell et al., 2017).

3. Dependability in Qualitative Research

Dependability is the extent to which the study could be replicated under similar conditions and the findings would be consistent.

Researchers can establish dependability using methods such as audit trails so readers can see the research process is logical and traceable (Koch, 1994).

4. Confirmability in Qualitative Research

Confirmability is concerned with establishing that there is a clear link between the researcher’s interpretations/ findings and the data.

Researchers can achieve confirmability by demonstrating how conclusions and interpretations were arrived at (Nowell et al., 2017).

This enables readers to understand the reasoning behind the decisions made.

Audit Trails in Qualitative Research

An audit trail provides evidence of the decisions made by the researcher regarding theory, research design, and data collection, as well as the steps they have chosen to manage, analyze, and report data.

The researcher must provide a clear rationale to demonstrate how conclusions were reached in their study.

A clear description of the research path must be provided to enable readers to trace through the researcher’s logic (Halpren, 1983).

Researchers should maintain records of the raw data, field notes, transcripts, and a reflective journal in order to provide a clear audit trail.

Discovery of unexpected data

Open-ended questions in qualitative research mean the researcher can probe an interview topic and enable the participant to elaborate on responses in an unrestricted manner.

This allows unexpected data to emerge, which can lead to further research into that topic.

The exploratory nature of qualitative research helps generate hypotheses that can be tested quantitatively (Busetto et al., 2020).

Flexibility

Data collection and analysis can be modified and adapted to take the research in a different direction if new ideas or patterns emerge in the data.

This enables researchers to investigate new opportunities while firmly maintaining their research goals.

Naturalistic settings

The behaviors of participants are recorded in real-world settings. Studies that use real-world settings have high ecological validity since participants behave more authentically.

Limitations

Time-consuming .

Qualitative research results in large amounts of data which often need to be transcribed and analyzed manually.

Even when software is used, transcription can be inaccurate, and using software for analysis can result in many codes which need to be condensed into themes.

Subjectivity

The researcher has an integral role in collecting and interpreting qualitative data. Therefore, the conclusions reached are from their perspective and experience.

Consequently, interpretations of data from another researcher may vary greatly.

Limited generalizability

The aim of qualitative research is to provide a detailed, contextualized understanding of an aspect of the human experience from a relatively small sample size.

Despite rigorous analysis procedures, conclusions drawn cannot be generalized to the wider population since data may be biased or unrepresentative.

Therefore, results are only applicable to a small group of the population.

While individual qualitative studies are often limited in their generalizability due to factors such as sample size and context, metasynthesis enables researchers to synthesize findings from multiple studies, potentially leading to more generalizable conclusions.

By integrating findings from studies conducted in diverse settings and with different populations, metasynthesis can provide broader insights into the phenomenon of interest.

Extraneous variables

Qualitative research is often conducted in real-world settings. This may cause results to be unreliable since extraneous variables may affect the data, for example:

Situational variables : different environmental conditions may influence participants’ behavior in a study. The random variation in factors (such as noise or lighting) may be difficult to control in real-world settings.
Participant characteristics : this includes any characteristics that may influence how a participant answers/ behaves in a study. This may include a participant’s mood, gender, age, ethnicity, sexual identity, IQ, etc.
Experimenter effect : experimenter effect refers to how a researcher’s unintentional influence can change the outcome of a study. This occurs when (i) their interactions with participants unintentionally change participants’ behaviors or (ii) due to errors in observation, interpretation, or analysis.

What sample size should qualitative research be?

The sample size for qualitative studies has been recommended to include a minimum of 12 participants to reach data saturation (Braun, 2013).

Are surveys qualitative or quantitative?

Surveys can be used to gather information from a sample qualitatively or quantitatively. Qualitative surveys use open-ended questions to gather detailed information from a large sample using free text responses.

The use of open-ended questions allows for unrestricted responses where participants use their own words, enabling the collection of more in-depth information than closed-ended questions.

In contrast, quantitative surveys consist of closed-ended questions with multiple-choice answer options. Quantitative surveys are ideal to gather a statistical representation of a population.

What are the ethical considerations of qualitative research?

Before conducting a study, you must think about any risks that could occur and take steps to prevent them. Participant Protection : Researchers must protect participants from physical and mental harm. This means you must not embarrass, frighten, offend, or harm participants. Transparency : Researchers are obligated to clearly communicate how they will collect, store, analyze, use, and share the data. Confidentiality : You need to consider how to maintain the confidentiality and anonymity of participants’ data.

What is triangulation in qualitative research?

Triangulation refers to the use of several approaches in a study to comprehensively understand phenomena. This method helps to increase the validity and credibility of research findings.

Types of triangulation include method triangulation (using multiple methods to gather data); investigator triangulation (multiple researchers for collecting/ analyzing data), theory triangulation (comparing several theoretical perspectives to explain a phenomenon), and data source triangulation (using data from various times, locations, and people; Carter et al., 2014).

Why is qualitative research important?

Qualitative research allows researchers to describe and explain the social world. The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively.

In qualitative research, participants are able to express their thoughts, experiences, and feelings without constraint.

Additionally, researchers are able to follow up on participants’ answers in real-time, generating valuable discussion around a topic. This enables researchers to gain a nuanced understanding of phenomena which is difficult to attain using quantitative methods.

What is coding data in qualitative research?

Coding data is a qualitative data analysis strategy in which a section of text is assigned with a label that describes its content.

These labels may be words or phrases which represent important (and recurring) patterns in the data.

This process enables researchers to identify related content across the dataset. Codes can then be used to group similar types of data to generate themes.

What is the difference between qualitative and quantitative research?

Qualitative research involves the collection and analysis of non-numerical data in order to understand experiences and meanings from the participant’s perspective.

This can provide rich, in-depth insights on complicated phenomena. Qualitative data may be collected using interviews, focus groups, or observations.

In contrast, quantitative research involves the collection and analysis of numerical data to measure the frequency, magnitude, or relationships of variables. This can provide objective and reliable evidence that can be generalized to the wider population.

Quantitative data may be collected using closed-ended questionnaires or experiments.

What is trustworthiness in qualitative research?

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability.

Credibility refers to how accurately the results represent the reality and viewpoints of the participants. Transferability refers to whether the findings may be applied to another context, setting, or group.

Dependability is the extent to which the findings are consistent and reliable. Confirmability refers to the objectivity of findings (not influenced by the bias or assumptions of researchers).

What is data saturation in qualitative research?

Data saturation is a methodological principle used to guide the sample size of a qualitative research study.

Data saturation is proposed as a necessary methodological component in qualitative research (Saunders et al., 2018) as it is a vital criterion for discontinuing data collection and/or analysis.

The intention of data saturation is to find “no new data, no new themes, no new coding, and ability to replicate the study” (Guest et al., 2006). Therefore, enough data has been gathered to make conclusions.

Why is sampling in qualitative research important?

In quantitative research, large sample sizes are used to provide statistically significant quantitative estimates.

This is because quantitative research aims to provide generalizable conclusions that represent populations.

However, the aim of sampling in qualitative research is to gather data that will help the researcher understand the depth, complexity, variation, or context of a phenomenon. The small sample sizes in qualitative studies support the depth of case-oriented analysis.

What is narrative analysis?

Narrative analysis is a qualitative research method used to understand how individuals create stories from their personal experiences.

There is an emphasis on understanding the context in which a narrative is constructed, recognizing the influence of historical, cultural, and social factors on storytelling.

Researchers can use different methods together to explore a research question.

Some narrative researchers focus on the content of what is said, using thematic narrative analysis, while others focus on the structure, such as holistic-form or categorical-form structural narrative analysis. Others focus on how the narrative is produced and performed.

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Unpacking complexity in addressing the contribution of trauma to women’s ill health: a qualitative study of perspectives from general practice

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Background There is an intricate relationship between the mind and the body in experiences of health and wellbeing. This can result in complexity of both symptom presentation and experience. Although the contribution of life trauma to illness experience is well described, this is not always fully recognised or addressed in healthcare encounters. Negotiating effective and acceptable trauma-informed conversations can be difficult for clinicians and patients.

Aim To explore the experience of primary care practitioners caring for women through a trauma-informed care lens.

Design and setting Qualitative study in the general practice setting of England, with reflections from representatives of a group with lived experience of trauma.

Method This was a secondary thematic analysis of 46 qualitative interviews conducted online/by telephone to explore primary care practitioners’ experiences of supporting women’s health needs in general practice, alongside consultation with representatives of a lived-experience group to contextualise the findings.

Results Four themes were constructed: ‘you prioritise physical symptoms because you don’t want to miss something’; you do not want to alienate people by saying the wrong thing; the system needs to support trauma-informed care; and delivering trauma-informed care takes work that can have an impact on practitioners.

Conclusion Primary care practitioners are aware of the difficulties in discussing the interface between trauma and illness with patients, and request support and guidance in how to negotiate this supportively. Lack of support for practitioners moves the focus of trauma-informed care from a whole-systems approach towards individual clinician–patient interactions.

biopsychosocial models
communication
general practice
trauma-informed care
Introduction

As evident in the Women’s Health Strategy for England 1 and its underlying public consultation, 2 women’s health is complex and embedded in historical dismissal and stigma. There is an intricate relationship between the mind and the body in experiences of health and wellbeing. One facet of this complexity includes the possible contribution of trauma to the woman’s illness experience. The physical response to, and pathways of bodily damage as a result of, the hormonal environment of chronic stress has revealed links between unresolved emotional distress and autoimmune conditions. 3 Trauma has an impact on people in different ways. Although some people make positive adjustments, others experience mental ill health and/or develop physical symptoms from emotional distress. 4 This can result in complexity both in symptom presentation and health experience.

Trauma can result from an event, series of events, or set of circumstances that is experienced by an individual as harmful or life threatening and can include past experiences of care (including in maternity), adverse childhood events (ACEs), and other life experiences as an adult. ACEs are stressful or traumatic events that occur specifically during childhood or adolescence 5 and can include: abuse (physical, emotional, and sexual); neglect; living in a household with domestic violence, experience of illness, or bereavement. 6 In a systematic review and meta-analysis of 96 studies of adult health behaviours, the risk of poorer health outcomes (including cardiovascular disease, respiratory disorders, gastrointestinal disorders, and mental ill health) increased with the number of ACEs. 4 Experiences of trauma at any stage in life can cause lasting adverse effects on health. 3 In the UK, women are disproportionally affected by violence (twice as likely as men to experience domestic violence), 7 , 8 trauma, 9 , 10 and ill health, 11 , 12 highlighting the potential complexity of women’s health presentation.

Although the contribution of life trauma to illness experience is well described, primary care professionals do not always fully address it. Potential reasons include clinician concerns about missing a serious illness in a complex presentation or about alienating or upsetting the patient. 11 Addressing trauma often necessitates introducing conversations about the link between mind and body, which can be difficult to navigate. Significant challenges and uncertainties reside in how best to manage the link between mind and body in communication with patients and in healthcare pathways. Qualitative research indicates that primary care professionals can find it challenging to navigate this mind–body presentation. Suggestions from primary care professionals that physical symptoms are amplified by (or a manifestation of) distress can be experienced as dismissal and invalidation by patients. 13 – 15 Attempts to bridge these health needs are therefore not always experienced as supportive. This illustrates the potential challenges of negotiating trauma-informed conversations in ways that are experienced as acceptable and supportive by patients.

Significant challenges and uncertainties reside in how best to manage the link between mind and body in communication with patients and in healthcare pathways. Lack of supportive resources to deliver holistic, trauma-informed care risks practitioners (inadvertently) avoiding discussion of the contribution of distress in the illness presentation. A trauma-informed systems-level approach would support integration of psychological support within multiple care pathways and support wellbeing of practitioners providing care.

How this fits in

Trauma-informed care is a framework founded on five core practices: safety, trustworthiness, choice, collaboration, and empowerment. These can be used to address the impact of trauma on patients and healthcare professionals and prevent re-traumatisation in healthcare services. 16 However, definitions, guidance, practitioner training, delivery, and support for trauma-informed approaches vary between healthcare settings according to local-level funding priorities with implementation described as disjointed. 16 Little is known about how healthcare professionals experience trying to effectively deliver trauma-informed care. The aim of this study was to explore the experiences of primary care practitioners caring for women through a trauma-informed care lens.

This study was a secondary analysis of qualitative interview data gathered to explore primary care practitioners’ experiences of supporting women’s health needs in primary care. Between March and September 2022, we interviewed a sample of 46 primary care practitioners across England (GPs n = 31, nurses n = 9, other professionals n = 6, with an average of 12 years’ experience [1 to 30 years], 41/46 female), ensuring representation from practices working in areas of deprivation where health inequalities and multimorbidity are significant challenges. Detailed methods and participant characteristics of the parent study are reported elsewhere. 17

The original topic guide was developed by three authors in response to a perceived gap in knowledge about women’s health care in primary care and commissioned by the National Institute of Health Research (NIHR) Policy Research Programme. Data were collected through single-episode, one-to-one interviews with fully informed consent. They were conducted virtually online or by telephone by two experienced qualitative researchers and audio-recorded. These were transcribed verbatim, checked against the original recording, and thematically analysed.

The team then undertook a focused enquiry using secondary thematic analysis of the dataset to explore primary care professionals’ navigation of women’s experiences of distress as a contribution to their symptoms. 18 We recoded the transcripts line-by-line where distress, emotional, or psychological impact or contribution to health experience was mentioned. We discussed the constructed data categories within the research team to create interpretive themes. We reflected on these themes with representatives of three charities supporting women with significant experience of historical and contemporary trauma to add a lived-experience perspective to the data.

Four themes were constructed from the data:

‘you prioritise physical symptoms because you don’t want to miss something’;

you do not want to alienate people by saying the wrong thing;

the system needs to support trauma-informed care; and

delivering trauma-informed care takes work that can have an impact on practitioners.

Theme 1: ‘you prioritise physical symptoms because you don’t want to miss something’ (PC30, female [F], GP for 5 years)

Practitioners described women’s health consultations as often complex and difficult to manage in a single, constrained time slot. A significant concern was the fear of missing a physical condition requiring specific or prompt treatment as many women’s health complaints could present with similar but vague symptomatology and could suggest multiple possible diagnoses. Some participants reflected that a challenge of navigating diagnostic processes, by first excluding potential causes that need specific interventions such as cancer, meant the contribution of distress to physical symptoms was pushed down the list of considerations: ‘It’s definitely sort of a symptom sieve to start with, and to adequately hear your patient and really hear them and really listen to what they’re saying […] There are many things that are difficult to do in ten minutes, but I … women’s health is particularly difficult.’ (PC17, F, advanced nurse practitioner [ANP] for more than 15 years) ‘They’re often quite vague symptoms: bloating, things like that, so you either have a very low index of suspicion and you’re seeing ca-125s [blood test that may indicate ovarian cancer] and you’re scanning everybody, or things get missed, and [sighs] yeah, it can be very challenging and obviously if you miss something like that it’s devastating for everybody involved, but it’s very difficult.’ (PC12, F, GP for more than 15 years)

Participants described how investigation pathways move through a hierarchy of potential causes and may involve a stepped process that did not always yield a confirmatory or unifying diagnosis. This meant that the participants had to manage patients’ expectations of diagnosis throughout this process.

Theme 2: you do not want to alienate people by saying the wrong thing

Some felt that a cultural shift was needed for the wider healthcare system to acknowledge the mind–body interplay as a legitimate expression of distress, to support practitioners to discuss this with their patients along their care pathway, and to provide timely access to psychological support services: ‘Perhaps some of training for staff would be about how you talk about the connection between your brain and your body […] without sounding dismissive and actually, training individuals to become more sensitive to these types of, conversations.’ (PC46, male [M], GP for 15 years)

However, some felt that patients were not always receptive to recognising the contribution of emotions or past experiences to physical symptoms, the idea of an integral link between mind and body, or the offer of psychological support to cope with the distress of physical symptoms. Some participants were worried about alienating women who might interpret this suggestion as devaluing or de-legitimising their symptom experience, and were therefore sometimes unsure when or how to navigate this: ‘I don’t think many patients like it when we end up going down that route when it comes to pain, any pain, not just pelvic pain in itself, because they want a diagnosis of some form or another, whatever it’s called, rather than being given some antidepressants or some counselling.’ (PC18, F, GP for 10 years)

Participants described the essential first step to be validation of the woman’s experience, emphasising understanding and genuine belief in the symptoms as ‘real’ (although perhaps currently unexplained) before exploring the impact of trauma or life stress in its aetiology: ‘It’s just spending the time with them and actually acknowledging, yes the pain is real, but are we not just saying you know, “you’ve got pain and we can’t find any cause for it”, “the pain is actually real”, and what we can do is maybe go down the route of psychological sort of therapy for that, that might be the best route of managing it.’ (PC18, F, GP for 10 years) ‘The first lady I was talking about absolutely wasn’t having any of it […] I got her some interesting resources […] and I just mis-pitched it […] the fact that this is her body feeling overwhelmed and feeling overwhelmed with the difficulties in her life and how to explain that in a way that seems scientific … it’s quite difficult, isn’t it?’ (PC14, F, GP for 1 year)

Healthcare professionals were aware and worried that exploring the contribution of trauma or distress in the physical symptom experience and that physical and emotional symptoms can coexist was not always well received. Restricted time in consultations highlighted the need for resources that could support this mind–body understanding in a positive and affirming way for the patient: ‘Often there is something organic, or something organic that has started it off, but then it often becomes this kind of complex combination of physical and then also psychological symptoms together, and I think kind of having resources to explain how psychological symptoms can impact pelvic pain […] I think kind of having good resources to try and back up what I’m saying would be quite helpful.’ (PC21, F, GP for more than 20 years)

Participants described how the net effect of these considerations could result in practitioners (inadvertently) avoiding discussion of the contribution of distress in the illness presentation: ‘ […] I think you can shut it down easily and not get emotionally involved, but you do not actually solve any of the issues unless they are straight up, simple, physical problems that you can just treat, but for the most part it doesn’t work very well.’ (PC30, F, GP for 5 years)

Participants recognised the importance of a trauma-informed approach in the complex and holistic care needs of women’s health. This extended to considerations about trauma-informed approaches to physical examination and how this could be enabled. Some highlighted the unique position of the primary care practitioner, in a potentially protracted diagnostic or support pathway, to communicate the contribution of distress in a supportive and helpful way to their patients.

Theme 3: the system needs to support trauma-informed care

Participants described four systemic challenges to the provision of trauma-informed care:

inadequate time allocated for appointments;

waiting times for specialist practitioner review in secondary care;

limited access to services; and

providing care for women returning from secondary care without a unifying diagnosis.

The challenges of time were frequently reported by participants: ‘I already know that I can’t do everything for you [the patient] in ten minutes, which isn’t always like a nice feeling for me, because we want to be able to help and you know do that within the time … who knows when they’ll be able to get an appointment again or you don’t want it to be frustrating for them, but equally you don’t want to rush yourself.’ (PC35, F, GP for less than 6 months) ‘They come back two months later and say, “I’ve still … I’m still … still haven’t seen the hospital”, and that there’s a certain amount of workload in primary care just because of … just because secondary care can’t take that on.’ (PC23, M, GP for more than 20 years)

In some areas they reported limited access to services such as counselling or psychological support services and community gynaecology because of local funding models and the challenges of providing care for women returning from secondary care without a unifying diagnosis. This often led to practitioners ‘holding the distress’ of the woman (see theme 4). Despite the challenges identified, participants described how they worked within the system constraints to offer the best service for their patients, for example, planning activities across multiple appointments: ‘In fifteen minutes it’s quite challenging, or if I’m trying to examine somebody […] that’s difficult, that’s when I sometimes ask them […] to come back for the examination so that I can do all the other things that are needed.’ (PC25, F, GP for 25 years)

Participants spoke of the structural supports that were in place that worked well in their efforts to deliver trauma-informed care, such as support networks, the ‘advice and guidance’ contact service to access secondary care (a system where GPs can access specialist advice before or instead of referral), and working with social prescribers (link workers who help patients to access non-medical support services in their community): ‘I mean advice and guidance [are] probably helpful I think, you write and you say, “What do I do?” and they tell you, and you then say to the patient, “this is what the specialist has said”, and that’s great, and that’s a really good idea.’ (PC23, M, GP for more than 20 years) ‘[Access to a social prescriber] is definitely making a difference; I don’t know what we did before to be quite honest. I don’t know what we would do because it’s just improved the quality of life for our patients, and it’s just helped us cope because you know we often see mental health problems, social problems, and with such a limited time constraint, limited resources, now that investment has been put in, it is definitely making a difference.’ (PC16, F, ANP for more than 18 years)

Theme 4: delivering trauma-informed care takes work and can have an impact on practitioners

Taking a trauma-informed approach relied heavily on the practitioner–patient relationship and some felt that the impact on practitioners was not always accounted for. The work involved in taking a trauma-informed approach to care had an impact on clinician workload. When they were able to navigate this challenge participants reported job satisfaction that was a positive impact. Conversely, when participants were unable to deliver the care they aspired to and believed they should, this had a negative impact. Protracted routes to diagnosis (or not getting a diagnosis), exacerbated by long waits to access specialist review in secondary care, left participants ‘holding the distress’ of women managing symptoms while they waited for a management plan: ‘I mean typically what happens is when a referral is done, the patient is waiting three, four, five months to be seen sometimes, but the patient’s still got those symptoms, so what do they do?’ (PC18, F, GP for 10 years) ‘So pain is complex. I think every pain service in the country is poorly funded and poorly accessible […] The challenge we have is these patients are constantly accessing us and, you know, I don’t want to label anything but they do end up becoming frequent attenders, which you know … and all we are is becoming a holding person in all of this.’ (PC46, M, GP for 15 years)

This increased the pressure on primary care practitioners who were operating without adequate system support. Although participants knew that managing uncertainty was integral to the role of the primary care practitioner, holding distress added to the challenge of appropriately broaching or exploring the mind–body link. Participants described feeling overwhelmed and personally affected by managing the expectations of patients held in limbo and holding their distress: ‘Women who have complex, like intractable symptoms that have been investigated and no one’s really come up with anything […] it’s more psycho-social input that’s needed, and they’ve seen a gynaecologist and they’re still struggling and there’s not really a solution, and so they’re … they’re the ones who you think, “oh my gosh, I … I’m … I’m not sure what I can offer … offer you”.’ (PC34, F, GP for 15 years) ‘I mean women’s health is a prime one, it causes so much anxiety, stress, impact on the family, and I think with the complexities around the referral pathways and who’s doing what, which has been one of my biggest stresses, people can fall through the gaps very easily.’ (PC26, F, GP for 5 years)

Participants sought support from colleagues within their daily work routines to reflect on clinical questions or patients with complex cases. However, some felt that there were limited support services for practitioners’ mental wellbeing in a more formalised and structured way: ‘We have our annual appraisal but that is very much to make sure that we’re not total lunatics […] but other than that […] they do support us, but they … you know it’s once a year, there’s no capacity to debrief on individual challenging cases or anything like that, it’s very much to check-in that we are sort of on the rails.’ (PC30, F, GP for 5 years)

Participants described how not being able to deliver high-quality, holistic care because of structural constraints was unsatisfying and challenging: ‘I was so unhappy in my previous job really, I’d say we still had support, but the patients were a lot more demanding and it just comes with that, you know a lot more child protection issues safeguarding and it … you know, it’s just a really challenging job and that, and not necessary work satisfying either.’ (PC04, F, GP for 3 years)

Lack of personal and systems support for practitioners moves the focus of trauma-informed care from a whole-systems approach to the clinician–patient interaction.

Our findings indicate that clinicians are aware of the contribution of trauma and distress to the presentation of physical symptomatology within women’s health consultations but that conversations about this could be difficult. Some participants felt confident and willing to discuss the role of distress in symptom presentation; others felt that these conversations were difficult and sometimes avoided the topic. Constraints such as limited time in consultations and the training and resources to facilitate discussions about the minded-body (the interconnection of physical and emotional health) and the role of trauma and distress could mean that clinicians did not always talk to patients about the impact of distress. This was exacerbated by system constraints such as limited support services for referral. Practitioners described building support mechanisms for themselves at work through debrief and clinical conversations with colleagues but told us that there were no formal supervision or support services routinely available for practitioners. The heavy work and emotional labour within an unsupportive system was described as contributing to practitioner frustration and burnout. Although patient relationships were framed within a trauma-informed lens, the organisational configuration was not always supportive to a trauma-informed approach.

Strengths and limitations

The use of secondary analysis has allowed us to conduct a focused analysis on a rich dataset of primary care professionals’ interviews. As this was done within the project timeline by the original research team, potential ethical concerns about the impact of the sociopolitical context that often accompanies secondary analysis were mitigated. 18 We were able to minimise participant burden and engage with a targeted group of women for whom trauma-informed care and its delivery has an immediate impact.

The principal limitation of our study is the restrictions offered by the original interview scope and guiding questions of the parent study that focused on women’s health. We are unable to report on experience in other areas of health care or by gender of care provider as this is unexplored. Gender was recorded; there were four male and 42 female responders. We purposively selected practitioners with an interest in women’s health rather than sampling an equally gender-split sample to derive patterns of experience that could be attributed to gender issues.

Comparison with existing literature

The link between trauma and ill health is well discussed in the literature, as are the principles of trauma-informed care. However, there appears to be little evidence of the clinician’s experience in discussing the interface between trauma and complexity with patients. The complexity of women’s health experiences challenges a dualistic approach to care and could respond better to the continuity model of primary care. 19 Practitioners in our data actively enacted the principles of trauma-informed care (such as safety, trustworthiness, and collaboration) in their personal practice with women. 16 However, the structural configuration of primary care services could complicate these care aspirations including when resources were limited or services were not flexible enough to support practitioner autonomy, which could hinder opportunities for timely care or follow-up. This could erode the practitioner’s efforts to deliver trauma-informed care, with potential consequences for both patients and clinicians. Such structural constraints in a climate of overwork are powerful sources of moral distress and burnout in studies of nurses, midwives, and doctors. 20 – 23 The risk of exposing practitioners to such moral distress can lead to the experience of vicarious trauma and reduced job satisfaction as they navigate the challenge of exploring the minded-body link with patients on their illness journey. 24 , 25 Primary care practitioners held women’s distress while they waited for specific therapies or supports, and yet the practitioners did not have adequate formal support systems to take care of their own wellbeing. This finding resonates with Pereira Gray et al , 25 who suggest that the UK shortage of GPs, erosion of continuity of care, sustained increase of remote consultation methods, and lack of structural support in the system may exacerbate challenges faced by practitioners to provide high-quality care. 26 – 28

Implications for research and practice

Our findings suggest that moving towards a trauma-informed systems-level approach would support integration of psychological support within multiple care pathways. A coordinated systems approach should support an integrated and holistic approach rather than encouraging a dichotomising split between physical or psychological services. Our findings suggest that this model would also support the wellbeing of practitioners delivering care and may have an impact on staff retention, making this a critical consideration at all system and service levels from individuals to practices to funders and commissioners. 28 , 29 However, less is known about how to enact or enable trauma-informed care at a systems level. 16 More research is needed about how to implement and support equitable, proportionate trauma-informed care in practice. This includes learning how to actively nurture equitable care within services, practices, and within primary care networks. At a funding and commissioning level, autonomy and equitable work need to be valued and enabled, and this requires policy attention; simplistic metrics of care such as numbers seen or a narrow focus on numerically quantifiable access will not capture either the impact on patients or practitioners. 28 Nor will this capture the contacts and appointments that did not happen. Furthermore, critical to effective equitable care is that practitioners need meaningful access to services that they can refer into and that will respond promptly and supportively to the needs identified. Work in areas of care such as female genital mutilation and domestic violence and abuse demonstrate that having acceptable accessible services to refer into enabled inquiry and compassionate care. 30 , 31 It is an ethical prerogative that trauma-informed enquiry is supported by trauma-informed services and support. Finally, support for staff is essential and the responsibility for this should not be devolved to individuals but commissioned and provided for. This contrasts with current policy, such as the wellbeing Quality and Outcomes Framework indicators that arguably devolve the responsibility for wellbeing to those in need of wellbeing support, without offering any tangible resources.

Healthcare professionals are aware of the difficulties in discussing the interface between trauma and complexity with patients 32 and our work shows they are requesting support and guidance in how to negotiate this supportively. The British Medical Association moral injury report 22 recommends systems changes that map onto the principles of trauma-informed care, including increased staffing, streamlining of bureaucracy, open and sharing work cultures, and provision of support for employees. However, although these recommendations acknowledge the problem and offer solutions, there is no requirement for organisations to address these structural concerns. Lack of these system supports for practitioners moves the focus of trauma-informed care from a whole-systems approach to the clinician–patient interaction. 16

To seek lived-experience perspectives on our findings, 33 we spoke with three representatives of charities supporting survival sex workers (SSW) in different regions of England as an exemplar vulnerable group with significant experience of historical and ongoing trauma. They told us how women experience stigma and are afraid of disclosure and confidentiality, particularly if their children have been removed and placed into social care. The charity representatives described how women engaged in SSW rarely sought medical care or achieved registration at a general practice surgery because of lifestyle circumstances and stigmatising experiences.

We asked what trauma-informed care looked like for their service and asked them to reflect on our findings. They recommended a systems-level approach to the delivery of trauma-informed services across the health service. Barriers to access were described as starting at the front door of the general practice surgery with the reaction of the receptionist. A lack of confidentiality in the reception area, closed consulting room doors, short consultation times, and the predominance of digital access methods for appointments were also cited. Beyond these, they suggested responsive, transparent pathways into support services for vulnerable women or those living in extreme circumstances would illustrate a trauma-informed approach to services. Individual practitioners were credited with adopting a trustworthy, trauma-informed approach but charity representatives, in consultation with the women they support, felt that the healthcare system could counteract individual good practice.

Acknowledgments

We would like to acknowledge the contributions of our Public Involvement participants and express our thanks for the insights they shared with the research team.

This study was funded by the National Institute for Health and Care Research (NIHR) Policy Research Programme (NIHR202450). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Ethical approval

This study has received ethical approval from the Health Research Authority (ref 22/HRA/0985).

The authors do not have ethical permission to share their dataset beyond the study team.

Freely submitted; externally peer reviewed.

Competing interests

The authors have declared no competing interests.

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Received January 12, 2024.
Revision requested February 19, 2024.
Accepted April 9, 2024.
© The Authors

This article is Open Access: CC BY 4.0 licence ( http://creativecommons.org/licences/by/4.0/ ).

Department of Health and Social Care
Fairweather D ,
Pearson WS ,
Petruccelli K ,
Di Lemma L ,
Davies AR ,
Dohrenwend BP
Office for National Statistics (ONS)
Andrews AR ,
Jobe-Shields L ,
Eckstrand KL ,
Alessi EJ ,
NHS England
Chew-Graham CA ,
Heyland S ,
Kingstone T ,
MacLellan J ,
Ruggiano N ,
Machtinger EL ,
Shohaimi S ,
Khaledi-Paveh B ,
McKeller L ,
Fleet J-A ,
British Medical Association
Molinaro ML ,
Agarwal G ,
Kokokyi S ,
Pereira Gray D ,
Sidaway-Lee K ,
De Simona A ,
Szilassy E ,
Vennik JL ,
NIHR School for Primary Care Research

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“I am there just to get on with it”: a qualitative study on the labour of the patient and public involvement workforce

Stan Papoulias ORCID: orcid.org/0000-0002-7891-0923 1 &
Louca-Mai Brady 2

Health Research Policy and Systems volume 22 , Article number: 118 ( 2024 ) Cite this article

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Workers tasked with specific responsibilities around patient and public involvement (PPI) are now routinely part of the organizational landscape for applied health research in the United Kingdom. Even as the National Institute for Health and Care Research (NIHR) has had a pioneering role in developing a robust PPI infrastructure for publicly funded health research in the United Kingdom, considerable barriers remain to embedding substantive and sustainable public input in the design and delivery of research. Notably, researchers and clinicians report a tension between funders’ orientation towards deliverables and the resources and labour required to embed public involvement in research. These and other tensions require further investigation.

This was a qualitative study with participatory elements. Using purposive and snowball sampling and attending to regional and institutional diversity, we conducted 21 semi-structured interviews with individuals holding NIHR-funded formal PPI roles across England. Interviews were analysed through reflexive thematic analysis with coding and framing presented and adjusted through two workshops with study participants.

We generated five overarching themes which signal a growing tension between expectations put on staff in PPI roles and the structural limitations of these roles: (i) the instability of support; (ii) the production of invisible labour; (iii) PPI work as more than a job; (iv) accountability without control; and (v) delivering change without changing.

Conclusions

The NIHR PPI workforce has enabled considerable progress in embedding patient and public input in research activities. However, the role has led not to a resolution of the tension between performance management priorities and the labour of PPI, but rather to its displacement and – potentially – its intensification. We suggest that the expectation to “deliver” PPI hinges on a paradoxical demand to deliver a transformational intervention that is fundamentally divorced from any labour of transformation. We conclude that ongoing efforts to transform health research ecologies so as to better respond to the needs of patients will need to grapple with the force and consequences of this paradoxical demand.

Peer Review reports

Introduction – the labour of PPI

The inclusion of patients, service users and members of the public in the design, delivery and governance of health research is increasingly embedded in policy internationally, as partnerships with the beneficiaries of health research are seen to increase its relevance, acceptability and implementability. In this context, a growing number of studies have sought to evaluate the impact of public participation on research, including identifying the barriers and facilitators of good practice [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Some of this inquiry has centred on power, control and agency. Attention has been drawn, for example, to the scarcity of user or community-led research and to the low status of experiential knowledge in the hierarchies of knowledge production guiding evidence-based medicine [ 9 ]. Such hierarchies, authors have argued, constrain the legitimacy that the experiential knowledge of patients can achieve within academic-led research [ 10 ], may block the possibility of equitable partnerships such as those envisioned in co-production [ 11 ] and may function as a pull back against more participatory or emancipatory models of research [ 12 , 13 , 14 ]. In this way, patient and public inclusion in research may become less likely to aim towards inclusion of public and patient-led priorities, acting instead as kind of a “handmaiden” to research, servicing and validating institutionally pre-defined research goals [ 15 , 16 , 17 ].

Research on how public participation-related activities function as a form of labour within a research ecosystem, however, is scarce [ 18 ]. In this paper, we examine the labour of embedding such participation, with the aim of understanding how such labour fits within the regimes of performance management underpinning current research systems. We argue that considering this “fit” is crucial for a broader understanding of the implementation of public participation and therefore its potential impact on research delivery. To this end, we present findings from a UK study of the labour of an emerging professional cadre: “patient and public involvement” leads, managers and co-ordinators (henceforth PPI, the term routinely used for public participation in the United Kingdom). We concentrate specifically on staff working on research partnerships and centres funded by the National Institute for Health and Care Research (NIHR). This focus on the NIHR is motivated by the organization’s status as the centralized research and development arm of the National Health Service (NHS), with an important role in shaping health research systems in the United Kingdom since 2006. NIHR explicitly installed PPI in research as a foundational part of its mission and is currently considered a global leader in the field [ 19 ]. We contend that exploring the labour of this radically under-investigated workforce is crucial for understanding what we see as the shifting tensions – outlined in later sections – that underpin the key policy priority of embedding patients as collaborators in applied health research. To contextualize our study, we first consider how the requirement for PPI in research relates to the overall policy rationale underpinning the organizational mission of the NIHR as the NHS’s research arm, then consider existing research on tensions identified in efforts to embed PPI in a health system governed through regimes of performance management and finally articulate the ways in which dedicated PPI workers’ responsibilities have been developed as a way to address these tensions.

The NIHR as a site of “reformed managerialism”

The NIHR was founded in 2006 with the aim of centralizing and rationalizing NHS research and development activities. Its foundation instantiated the then Labour government’s efforts to strengthen and consolidate health research in the UK while also tackling some of the problems associated with the earlier introduction of new public management (NPM) principles in the governance of public services. NPM had been introduced in the UK public sector by Margaret Thatcher’s government, in line with similar trends in much of the Global North [ 20 ]. The aim was to curb what the Conservatives saw as saw as excesses in both public spending and professional autonomy. NPM consisted in management techniques adapted from the private sector: in the NHS this introduction was formalized via the 1990 National Health Service and Community Care Act, which created an internal market for services, with local authorities purchasing services from local health providers (NHS Trusts) [ 21 ]; top-down management control; an emphasis on cost-efficiency; a focus on targets and outputs over process; an intensification of metrics for performance management; and a positioning of patients and the public as consumers of health services with a right to choose [ 22 , 23 ]. In the context of the NHS, cost-efficiency meant concentrating on services and on research which would have the greatest positive impact on population health while preventing research waste [ 24 ]. By the mid-1990s, however, considerable criticism had been directed towards this model, including concerns that NPM techniques resulted in silo-like operations and public sector fragmentation, which limited the capacity for collaboration between services essential for effective policy. Importantly, there was also a sense that an excessive managerialism had resulted in a disconnection of public services from public and civic aims, that is, from the values, voices and interests of the public [ 25 , 26 ].

In this context, the emergence of the NIHR can be contextualized through the succeeding Labour government’s much publicized reformed managerialism, announced in their 1997 white paper “The New NHS: Modern, Dependable” [ 27 ]. Here, the reworking of NPM towards “network governance” meant that the silo-like effects of competition and marketization were to be attenuated through a turn to cross-sector partnerships and a renewed attention to quality standards and to patients’ voices [ 28 ]. It has been argued, however, that the new emphasis on partnerships did not undermine the dominance of performance management, while the investment in national standards for quality and safety resulted in an intensified metricization, with the result that this reform may have been more apparent than real, amounting to “NPM with a human face” [ 29 , 30 , 31 ]. Indeed, the NIHR can be seen as an exemplary instantiation of this model: as a centralized commissioner of research for the NHS, the NIHR put in place reporting mechanisms and performance indicators to ensure transparent and cost-efficient use of funds, with outputs and impact measured, managed and ranked [ 24 ]. At the same time, the founding document of the NIHR, Best Research for Best Health, articulates the redirection of such market-oriented principles towards a horizon of public good and patient benefit. The document firmly and explicitly positioned patients and the public as both primary beneficiaries of and important partners in the delivery of health research. People (patients) were to be placed “at the centre of a research system that focuses on quality, transparency and value for money” [ 32 ], a mission implemented through the installation of “structures and mechanisms to facilitate increased involvement of patients and the public in all stages of NHS Research & Development” [ 33 ]. This involvement would be supported by the advisory group INVOLVE, a key part of the new centralized health research system. INVOLVE, which had started life in 1996 as Consumers in NHS Research, funded by the Department of Health, testified to the Labour administration’s investment in championing “consumer” involvement in NHS research as a means of increasing research relevance [ 34 ]. The foundation of the NIHR then exemplified the beneficent alignment of NPM with public benefit, represented through the imaginary of a patient-centred NHS, performing accountability to the consumers/taxpayers through embedding PPI in all its activities. In this context, “public involvement” functioned as the lynchpin through which such alignment could be effected.

PPI work and the “logic of deliverables”: a site of tension

Existing research on the challenges of embedding PPI has typically focussed on the experiences of academics tasked with doing so within university research processes. For example, Pollard and Evans, in a 2013 paper, argue that undertaking PPI work in mental health research can be arduous, emotionally taxing and time consuming, and as such, can be in tension with expectations for cost-efficient and streamlined delivery of research outputs [ 35 ]. Similarly, Papoulias and Callard found that the “logic of deliverables” governing research funding can militate against undertaking PPI or even constitute PPI as “out of sync” with research timelines [ 36 ]. While recent years have seen a deepening operationalization of PPI in the NIHR and beyond, there are indications that this process, rather than removing these tensions, may have recast them in a different form. For example, when PPI is itself set up as performance-based obligation, researchers, faced with the requirement to satisfy an increasing number of such obligations, may either engage in “surface-level spectacles” to impress the funder while eschewing the long-term commitment necessary for substantive and ongoing PPI, or altogether refuse to undertake PPI, relegating the responsibility to others [ 37 , 38 ]. Such refusals may then contribute to a sharpening of workplace inequalities: insofar as PPI work is seen as “low priority” for more established academic staff, it can be unevenly distributed within research organizations, with precariously employed junior researchers and women typically assigned PPI responsibilities with the assumption that they possess the “soft skills” necessary for these roles [ 39 ].

Notably, the emergence of a dedicated PPI workforce is intended as a remedy for this tension by providing support, expertise and ways of negotiating the challenges associated with undertaking PPI responsibilities. In the NIHR, this workforce is part of a burgeoning infrastructure for public involvement which includes national standards, training programmes, payment guidelines, reporting frameworks and impact assessments [ 40 , 41 , 42 , 43 , 44 , 45 ]. By 2015, an INVOLVE review of PPI activities during the first 10 years of the NIHR attested to “a frenzy of involvement activity…across the system”, including more than 200 staff in PPI-related roles [ 40 ]. As NIHR expectations regarding PPI have become more extensive, responsibilities of PPI workers have proliferated, with INVOLVE organizing surveys and national workshops to identify their skills and support needs [ 41 , 42 ]. In 2019, the NIHR mandated the inclusion of a “designated PPI lead” in all funding applications, listing an extensive and complex roster of responsibilities. These now included delivery and implementation of long-term institutional strategies and objectives, thus testifying to the assimilation of involvement activities within the roster of “performance-based obligations” within research delivery systems [ 43 ]. Notably however, this formalization of PPI responsibilities is ambiguous: the website states that the role “should be a budgeted and resourced team member” and that they should have “the relevant skills, experience and authority”, but it does not specify whether this should be a researcher with skills in undertaking PPI or indeed someone hired specifically for their skills in PPI, that is, a member of the PPI workforce. Equally, the specifications, skills and support needs, which have been brought together into a distinct role, have yet to crystallize into a distinct career trajectory.

Case studies and evaluations of PPI practice often reference the skills and expertise required in leading and managing PPI. Chief among them are relational and communication skills: PPI workers have been described as “brokers” who mediate and enable learning between research and lay spaces [ 44 , 45 ]; skilled facilitators enabling inclusive practice [ 46 , 47 , 48 ]; “boundary spanners” navigating the complexities of bridging researchers with public contributors and undertaking community engagement through ongoing relational work [ 49 ]. While enumerating the skillset required for PPI work, some of these studies have identified a broader organizational devaluation of PPI workers: Brady and colleagues write of PPI roles as typically underfunded with poor job security, which undermines the continuity necessary for generating trust in PPI work [ 46 ], while Mathie and colleagues report that many PPI workers describe their work as “invisible”, a term which the authors relate to the sociological work on women’s labour (particularly housework and care labour) which is unpaid and rendered invisible insofar as it is naturalized as “care” [ 50 ]. Research on the neighbouring role of public engagement professionals in UK universities, which has been more extensive than that on PPI roles, can be instructive in fleshing out some of these points: public engagement professionals (PEPs) are tasked with mediating between academics and various publics in the service of a publicly accountable university. In a series of papers on the status of PEPs in university workplaces, Watermeyer and colleagues argue that, since public engagement labour is relegated to non-academic forms of expertise which lack recognition, PEPs’ efforts in boundary spanning do not confer prestige. This lack of prestige can, in effect, function as a “boundary block” obstructing PEPs’ work [ 51 , 52 ]. Furthermore, like Mathie and Brady, Watermeyer and colleagues also argue that the relational and facilitative nature of engagement labour constitutes such labour as feminized and devalued, with PEPs also reporting that their work remains invisible to colleagues and institutional audit instruments alike [ 50 , 53 ].

The present study seeks to explore further these suggestions that PPI labour, like that of public engagement professionals, lacks recognition and is constituted as invisible. However, we maintain that there are significant differences between the purpose and moral implications of involvement and engagement activities. PPI constitutes an amplification of the moral underpinnings of engagement policies: while public engagement seeks to showcase the public utility of academic research, public involvement aims to directly contribute to optimizing and personalizing healthcare provision by minimizing research waste, ensuring that treatments and services tap into the needs of patient groups, and delivering the vision of a patient-centred NHS. Therefore, even as PPI work may be peripheral to other auditable research activities, it is nevertheless central to the current rationale for publicly funded research ecosystems: by suturing performance management and efficiency metrics onto a discourse of public benefit, such work constitutes the moral underpinnings of performance management in health research systems. Therefore, an analysis of the labour of the dedicated PPI workforce is crucial for understanding how this suturing of performance management and “public benefit” works over the conjured figures of patients in need of benefit. This issue lies at the heart of our research study.

Our interview study formed the first phase of a multi-method qualitative inquiry into the working practices of NIHR-funded PPI leads. While PPI lead posts are in evidence in most NIHR-funded research, we decided to focus on NIHR infrastructure funding specifically: these are 5-year grants absorbing a major tranche of NIHR funds (over £600 million annually in 2024). They function as “strategic investments” embodying the principles outlined in Best Research for Best Health: they are awarded to research organizations and NHS Trusts for the purposes of developing and consolidating capacious environments for early stage and applied clinical research, including building a research delivery workforce and embedding a regional infrastructure of partnerships with industry, the third sector and patients and communities [ 55 ]. We believe that understanding the experience of the PPI workforce funded by these grants may give better insights into NIHR’s ecosystem and priorities, since they are specifically set up to support the development of sustainable partnerships and embed the translational pipeline into clinical practice.

The study used purposive sampling with snowball elements. In 2020–2021, we mapped all 72 NIHR infrastructure grants, identified the PPI teams working in each of these using publicly available information (found on the NIHR website and the websites and PPI pages of every organization awarded infrastructure grants) and sent out invitation emails to all teams. Where applicable, we also sent invitations to mailing lists of PPI-lead national networks connected to these grants. Inclusion criteria were that potential participants should have oversight roles, and/or be tasked with cross-programme/centre responsibilities, meaning that their facilitative and strategy building roles should cover the entirety of activities funded by one (and sometimes more than one) NIHR infrastructure grant or centres including advisory roles over most or all research projects associated with the centre of grant, and that they had worked in this or a comparable environment for 2 years.

The individuals who showed interest received detailed information sheets. Once they agreed to participate, they were sent a consent form and a convenient interview time was agreed. We conducted 21 semi-structured interviews online, between March and June 2021, lasting 60–90 min. The interview topic guide was developed in part through a review of organizational documents outlining the role and through a consideration of existing research on the labour of PPI within health research environments. It focussed on how PPI workers fit within the organization relationship between the actual work undertaken and the way this work is represented to both the organization and the funder. Interview questions included how participants understand their role; how they fit in the organization; how their actual work relates to the job description; how their work is understood by both colleagues and public contributors; the relationship between the work they undertake and how this is represented in reports to funder and presentations; and what they find challenging about their work. Information about participants’ background and what brought them to their present role was also gathered. Audio files were checked, transcribed and the transcripts fully de-identified. All participants were given the opportunity to check transcripts and withdraw them at any point until December 2021. None withdrew.

We analysed the interviews using reflexive thematic analysis with participatory elements [ 54 , 55 ]. Reflexive thematic analysis emphasizes the interpretative aspects of the analytical process, including the data “collection” process itself, which this approach recognizes as a generative act, where meaning is co-created between interviewer and participant and the discussion may be guided by the participant rather than strictly adhering to the topic guide [ 56 ]. We identified patterns of meaning through sustained and immersive engagement with the data. NVivo 12 was used for coding, while additional notes and memos on the Word documents themselves mitigated the over-fragmentation that might potentially limit NVivo as a tool for qualitative analysis. Once we had developed themes which gave a thorough interpretation of the data, we presented these to participants in two separate workshops to test for credibility and ensure that participants felt ownership of the process [ 57 ].

As the population from which the sample was taken is quite small, with some teams working across different infrastructure grants, confidentiality and anonymity were important concerns for participants. We therefore decided neither to collect nor to present extensive demographic information to preserve confidentiality and avoid deductive disclosure [ 58 ]. Out of our 21 participants 20 were women; there was some diversity in age, ethnicity and heritage, with a significant majority identifying as white (British or other European). Participants had diverse employment histories: many had come from other university or NHS posts, often in communications, programme management or human resources; a significant minority had come from the voluntary sector; and a small minority from the private sector. As there was no accredited qualification in PPI at the time this study was undertaken, participants had all learned their skills on their present or previous jobs. A total of 13 participants were on full-time contracts, although in several cases funding for these posts was finite and fragmented, often coming from different budgets.

In this paper we present five inter-related themes drawing on the conceptual architecture we outlined in the first half of this paper to explore how PPI workers navigate a research ecosystem of interlocking institutional spaces that is governed by “NPM with a human face”, while striving to align patients and the public with the imaginary of the patient-centred NHS that mobilizes the NIHR mission. These five themes are: (i) the instability of support; (ii) the production of invisible labour; (iii) PPI as moral imperative; (iv) accountability without control; and (v) delivering change without changing.

“There to grease the cogs rather than be the cogs”: the instability of “support”

Infrastructure grants act as a hub for large numbers of studies, often in diverse health fields, most of which should, ideally, include PPI activities. Here, dedicated PPI staff typically fulfil a cross-cutting role: they are meant to oversee, provide training and advise on embedding PPI activities across the grant and, in so doing, support researchers in undertaking PPI. On paper, support towards the institution in the form of training, delivering strategy for and evaluating PPI is associated with more senior roles (designated manager or lead) whereas support towards so-called public contributors is the remit of more junior roles (designated co-ordinator or officer) and can include doing outreach, facilitating, attending to access needs and developing payment and compensation procedures. However, these distinctions rarely applied in practice: participants typically reported that their work did not neatly fit into these categories and that they often had to fulfil both roles regardless of their title. Some were the only person in the team specifically tasked with PPI, and so their “lead” or “manager” designation was more symbolic than actual:

I have no person to manage, although sometimes I do get a little bit of admin support, but I don’t have any line management responsibility. It is really about managing my workload, working with people and managing the volunteers that I work with and administrating those groups and supporting them (P11).

P11’s title was manager but, as they essentially worked alone, shuttling between junior and senior role responsibilities, they justified and made sense of their title by reframing their support work with public contributors as “management”. Furthermore, other participants reported that researchers often misunderstood PPI workers’ cross-cutting role and expected them to both advise on and deliver PPI activities themselves, even in the context of multiple projects, thus altogether releasing researchers of such responsibility.

As a PPI lead, it is very difficult to define what your role is in different projects….and tasks … So, for example, I would imagine in [some cases] we are seen as the go-to if they have questions. [..] whereas, in [other cases], it is like, “Well, that’s your job because you’re the PPI lead” […] there is not a real understanding that PPI is everyone’s responsibility and that the theme leads are there to facilitate and to grease the cogs rather than be the cogs (P20).

Furthermore, participants reported that the NIHR requirement for a PPI lead in all funding applications might in fact have facilitated this slippage. As already mentioned, the NIHR requirement does not differentiate between someone hired specifically to undertake PPI and a researcher tasked with PPI activities. The presence of a member of staff with a “PPI lead” title thus meant that PPI responsibilities in individual research studies could continue to accrue on that worker:

The people who have been left with the burden of implementing [the NIHR specified PPI lead role] are almost exclusively people like me, though, because now researchers expect me to allow myself to be listed on their project as the PPI lead, and I actually wrote a document about what they can do for the PPI lead that more or less says, “Please don’t list me as your PPI lead. Please put aside funds to buy a PPI lead and I will train them, because there is only one me; I can’t be the PPI lead for everyone” (P10).

This expectation that core members of staff with responsibilities for PPI would also be able to act as PPI leads for numerous research projects suggests that this role lacks firm organizational co-ordinates and boundaries. Here, the presence of a PPI workforce does not, in fact, constitute an appropriate allocation of PPI labour but rather testifies to a continuing institutional misapprehension of the nature of such labour particularly in terms of its duration, location and value.

Conjuring PPI: the production of invisible labour

Participants consistently emphasized the invisibility of the kinds of labour, both administrative and relational, specific to public involvement as a process, confirming the findings of Mathie and colleagues [ 50 ]. This invisibility took different forms and had different justifications. Some argued that key aspects of their work, which are foundational to involvement, such as the process of relationship building, do not lend themselves to recognition as a performance indicator: “ There is absolutely no measure for that because how long is a piece of string” (P11). In addition, relationship building necessitated a considerably greater time investment than was institutionally acceptable, and this was particularly evident when it came to outreach. Participants who did their work in community spaces told stories of uncomprehending line-managers, or annoyed colleagues who wondered where the PPI worker goes and what they do all day:

There is very little understanding from colleagues about what I do on a day-to-day basis, and it has led to considerable conflict …. I would arrive at the office and then I would be disappearing quite promptly out into the community, because that is where I belong […] So, it is actually quite easy to become an absent person (P3).

Once again, the NIHR requirement for designated PPI leads in funding applications, intended to raise the visibility of PPI work by formalizing it as costed labour, could instead further consolidate its invisibility:

I am constantly shoved onto bids as 2% of my full-time equivalent and I think I worked out for a year that would be about 39 hours a year. For a researcher, popping the statistician down and all these different people on that bid, “Everyone is 2% and we need the money to run the trial, so 2% is fine”. And if I said to them, “Well, what do you think I would do in those 39 hours?” they wouldn’t have a clue, not a clue (P17).

The 2% of a full-time allocation is accorded to the PPI worker because 2–5% is the time typically costed for leadership roles or for roles with a circumscribed remit (e.g. statisticians). However, this allocation, in making PPI workers’ labour visible either as oversight (what project leads do) or as methodological expertise (what statisticians do), ends up producing the wrong kind of visibility: the 39 h mentioned here might make sense when the role mainly involves chairing weekly meetings or delivering statistical models but are in no way sufficient for the intense and ongoing labour of trust-building and alignment between institutions and public contributors in PPI.

Indeed, such costings, by eliding the complexity and duration of involvement, may reinforce expectations that PPI can be simply conjured up at will and delivered on demand:

A researcher will say to us, “I would really like you to help me to find some people with lived experience, run a focus group and then I’ll be away”. To them, that is the half-hour meeting to talk about this request, maybe 10 minutes to draft a tweet and an email to a charity that represents people with that condition […] the reality is it is astronomically more than that, because there is all this hidden back and forth. […] [researchers] expect to be able to hand over their protocol and then I will find them patients and those patients will be … representative and I will be able to talk to all of those patients and … write them up a report and …send it all back and they will be able to be like, “Thanks for the PPI”, and be on their merry way (P13).

What P13 communicates in this story is the researcher’s failure to perceive the difference between PPI work and institutional norms for project delivery: the researcher who asks for “some people with lived experience” is not simply underestimating how long this process will take. Rather, involvement work is perceived as homologous to metricized and institutionally recognizable activities (for example, recruitment to trials or producing project reports) for which there already exist standard procedures. Here, the relational complexity and improvised dynamic of involvement is turned into a deliverable (“the PPI”) that can be produced through following an appropriate procedure. When PPI workers are expected to instantly deliver the right contributors to fit the project needs, PPI labour is essentially black boxed and in its place sits “the PPI”, a kind of magical object seemingly conjured out of nowhere.

Such invisibility, however, may also be purposefully produced by the PPI workers themselves. One participant spoke of this at length, when detailing how they worked behind the scenes to ensure public contributors have input into research documents:

When we get a plain English summary from a researcher, we rewrite them completely. If the advisory group [see] … a really bad plain English summary, they are just going to go, “I don’t understand anything”. I might as well do the translation straight away so that they can actually review something they understand. [Researchers then] think, “Oh, [the public advisory group] are so good at writing” … and I am thinking, “Well, they don’t … write, they review, and they will say to me, ‘Maybe move this up there and that up there, and I don’t understand these’”, … They are great, don’t get me wrong, but they don’t write it. And it is the same with a lot of things. They think that [the group] are the ones that do it when it is actually the team (P7).

Here, the invisibility of the PPI worker’s labour is purposefully wrought to create good will and lubricate collaboration. Several participants said that they chose to engage in such purposeful invisibility because they knew that resources were not available to train researchers in plain writing and public contributors in academic writing. PPI workers, in ghost-writing accessible texts, thus effect a shortcut in the institutional labour required to generate alignment between researchers and public contributors. However, this shortcut comes at a price: in effecting it, PPI workers may collude in conjuring “the PPI” – they may themselves make their own work disappear.

“Not a 9 to 5”: PPI work as more than a job

Most participants reported that overtime working was common for themselves and their teammates, whether they were on a fractional or full-time contract. Overall, participants saw undertaking extra work as a necessary consequence of their commitment towards public contributors, a commitment which made it difficult to turn work down:

Everyone loses if you say no: the public contributors aren’t involved in a meaningful way, the project won’t be as good because it doesn’t have meaningful PPI involvement (P20).

While overwork was a common result of this commitment, some participants described such overwork as the feature that distinguished PPI work from what one commonly understands as a “job”, because, in this case, over-work was seen as freely chosen rather than externally imposed:

It is me pushing myself or wanting to get things done because I started it and I think I would get less done if I worked less and that would bother me, but I don’t think it is a pressure necessarily from [line manager] or [the institution] or anyone to be like, “No, do more” (P13).

Participants presented relationship building not only as the most time-consuming but also the most enjoyable aspect of PPI work. Community engagement was a key site for this and once again participants tended to represent this type of work as freely chosen:

I did most of the work in my free time in the end because you have to go into communities and you spend a lot longer there. […] So, all of that kind of thing I was just doing in my spare time and I didn’t really notice at the time because I really enjoyed it (P6).

Thus, time spent in relationship building was constituted as both work and not work. It did not lend itself to metricization via workplace time management and additionally, was not perceived by participants themselves as labour (“I didn’t really notice it at the time”). At the same time, out-of-hours work was rationalized as necessary for inclusivity, set up to enable collaboration with public contributors in so far as these do not have a contractual relationship to the employer:

That is not a 9–5. That is a weekends and holidays sort of job, because our job is to reduce the barriers to involvement and some of those barriers are hours – 9–5 is a barrier for some people (P17).

If working overtime allows PPI workers to reduce barriers and enable collaboration with those who are not employed by the institution, that same overtime work also serves to conceal the contractual nature of the PPI workers’ own labour, which now becomes absorbed into the moral requirements of PPI.

“Caught in the middle”: accountability without control

Participants repeatedly emphasized that their ability to contribute to research delivery was stymied by their lack of control over specific projects and over broader institutional priority setting:

… as a PPI lead we are not full member of staff, we are not responsible for choosing the research topics. We […] can only guide researchers who come to us and tell us what they are doing … we don’t have any power to define what the public involvement looks like in a research project (P6).

Tasked with creating alignments and partnerships between the publics and institutions, participants argued that they did not have the power to make them “stick” because they are not “really” part of the team. However, even as PPI workers lacked the power to cement partnerships, any failure in the partnership could be ascribed to them, perceived as a failure of the PPI worker by both funder and public contributors:

Often you have to hand over responsibility and the researcher [who] can let the panel down and … I feel like I have let the panel member down because … I am the one who said, “Oh yes, this person wants to talk to you”, and I find that really challenging, getting caught in the middle like that (P21).

This pairing of accountability with lack of control became more pronounced in grant applications or reports to the funder:

It is also quite frustrating in the sense that, just because I advise something, it doesn’t necessarily mean that it gets implemented or even included in the final grant. [even so] whatever the feedback is still reflects on us, not necessarily on the people who were making the wider decisions […] As PPI leads, we are still usually the ones that get the blame (P10).

Several participants testified to this double frustration: having to witness their PPI plans being rewritten to fit the constraints (financial, pragmatic) of the funding application, they then often found themselves held accountable if the PPI plans fail to carry favour with the funder. PPI workers then become the site where institutional accountability to both its public partners and to the funder gathers – it is as though, while located outside most decision-making, they nevertheless become the attractors for the institution’s missing accountability, which they experience, in the words of P21, as “ being caught in the middle ” or, as another participant put it, as “ the worry you carry around ” (P16).

“There to just get on with it”: delivering change without changing

Participants recognized that effective collaboration between research institutions and various publics requires fundamental institutional changes. Yet they also argued that while PPI workers are not themselves capable of effecting such change, there is nevertheless considerable institutional pressure to deliver on promises made in grant applications and build PPI strategies on this basis:

So, there is that tension about […] pushing this agenda and encouraging people to do more [….] rather than just accepting the status quo. But actually, the reality is that it is very, very hard to get everybody in [grant name] to change what they do and I can’t make that happen, [senior PPI staff] can’t make that happen, nobody can. The whole systemic issue … But you have got, somehow in the strategy and what you say you are going to do, that tension between aspiration and reality (P4).

This tension between aspiration and reality identified here could not be spelled out in reports for fear of reputational damage. In fact, the expectation to have delivered meaningful PPI, now routinely set up in NIHR applications, could itself militate against such change. For example, a frequently voiced concern was that PPI was being progressively under-resourced:

I feel the bar is getting higher and higher and higher and expectations are higher and we have got no extra resource (P16).

However, annual reports, the mechanism through which the doing of PPI is evidenced, made it difficult to be open about any such under-resourcing.

We will allude to [the lack of resources]. So, we will say things like, “We punch above our weight”, but I am not sure that message gets home to the NIHR very clearly. It is not like the annual report is used to say, “Hey, you’re underfunding this systematically, but here’s all the good stuff we do”, because the annual report is, by essence, a process of saying how great you are, isn’t it? (P3).

The inclusion of PPI as a “deliverable” meant that, in a competitive ecosystem, the pressure is on to report that PPI has always already been delivered. As another participant put it, “ no one is going to report the bad stuff ” (P17). Hence reporting, in setting up PPI as a deliverable, reinforced new zones of invisibility for PPI labour and made it harder to surface any under-resourcing for such labour. Furthermore, such reporting also played down any association between successful PPI and system transformation. Another participant described the resistance they encountered after arguing the organization should move away from “last-minute” PPI:

I think it is really hard when […] these people are essentially paying your pay cheque, to then try to push back on certain things that I don’t think are truly PPI ….[A]s somebody who I felt my role was really to show best practice, for then [to be] seen as this difficult person for raising issues or pushing back rather than just getting things done, is really hard [….] I get the impression, at least within the [organization] … that I am not there to really point out any of the issues. I am there just to get on with it (P14).

This opposition between pointing out the issues and “getting on with it” is telling. It names a contradiction at the heart of PPI labour: here, the very act of pushing back – in this case asking for a commitment to more meaningful and ongoing PPI – can be perceived as going against the PPI worker’s responsibilities, insofar as it delays and undoes team expectations for getting things done, for delivering PPI. Here, then, we find an exemplary instance of the incommensurability between the temporal demands of research and those of meaningful PPI practice.

How do the five themes we have presented help open out how policies around public participation are put into practice—as well as the contradictions that this practice navigates – in health systems organized by the rhetorical suturing of performance management onto public benefit? We have argued that the development of a dedicated workforce represents an attempt to “repair” the tension experienced by researchers between the administrative, facilitative and emotional work of PPI and the kinds of deliverables that the institution requires them to prioritize. We argue that our findings indicate that insofar as PPI workers’ role then becomes one of “delivering” PPI, this tension is reproduced and at times intensified within their work. This is because, as actors in the health research ecosystem, PPI staff are tethered to the very regimes of performance management, which give rise to an institutional misapprehension of the actual labour associated with delivering PPI.

This misapprehension surfaces in the instruments through which the funder costs, measures and generates accountability for PPI – namely, the requirement for a costed PPI lead and the mandatory inclusion of a PPI section in applications and regular reports to funder. The NIHR requirement for a costed PPI lead, intended to legitimize the undertaking of PPI as an integral part of a research team’s responsibilities, may instead continue to position the PPI worker as a site for the research team’s wholesale outsourcing of responsibility for PPI, since this responsibility, while in tension with other institutional priorities, cannot nevertheless be refused by the team. Furthermore, the use of titles such as lead, manager or co-ordinator not only signal an orderly distinction between junior and senior roles, which often does not apply in practice, but also reframes the extra-institutional work of PPI (the forging of relationships and administrative support with public contributors), through the intra-institutional functions of performance/project management. This reframing elides an important difference between the two: public and patient partners, for the most part, do not have a formal contractual relationship with the institution and are not subject to performance management in the way that contracted researchers and healthcare professionals are. Indeed, framing the relationship between PPI workers and public contributors through the language of “management” fundamentally misrecognizes the kinds of relationalities produced in the interactions between PPI workers and public contributors and elides the externality of PPI to the “logic of deliverables” [ 36 ].

The inclusion of a detailed PPI section in grant applications and annual reports to funder further consolidates this misapprehension by also representing public involvement as if it is already enrolled within organizational normative procedures and therefore compels those in receipt of funding to evidence such delivery through annual reports [ 37 ]. This demand puts PPI workers under increasing pressure, since their function is to essentially present PPI objectives as not only achievable but already achieved, thus essentially bracketing out the process of organizational transformation which is a necessary prerequisite to establishing enduring partnerships with patients and the public. This bracketing out is at work in the organizational expectation to “just get on with it”, which structures the labour of delivering PPI in NIHR-funded research. Here, the demand to just get on, to do the work one is paid to do, forecloses the possibility of engaging with the structural obstacles that militate against that work being done. To the extent that both role designation and reporting expectations function to conceal the disjuncture that the establishment of public partnerships represents for regimes of performance management, they generate new invisibilities for PPI workers. These invisibilities radically constrain how such labour can be adequately undertaken, recognized and resourced.

In suggesting that much of the labour of staff in public involvement roles is institutionally invisible, and that organizational structures may obstruct or block their efforts, we concur with the arguments made by Watermeyer, Mathie and colleagues about the position of staff in public engagement and public involvement roles, respectively. However, our account diverges from theirs in our interpretation of how and why this labour is experienced as invisible and how that invisibility could be remedied. Mathie and colleagues in particular attribute this invisibility to a lack of parity and an institutional devaluation of what are perceived as “soft skills” – facilitation and relationship building in particular [ 50 ]. They therefore seek to raise PPI work to visibility by emphasizing the complexity of PPI activities and by calling for a ring-fencing of resources and a development of infrastructures capable of sustaining such work. While we concur that the invisibility of PPI labour is connected to its devaluation within research institutions, we also suggest that, in addition, this invisibility is a symptom of a radical misalignment between regimes of performance management and the establishment of sustainable public partnerships. Establishing such partnerships requires, as a number of researchers have demonstrated [ 18 , 59 , 60 ], considerable institutional transformation, yet those tasked with delivering PPI are not only not in a position to effect such transformation, they are also compelled to conceal its absence.

Recognizing and addressing the misalignment between regimes of performance management and the establishment of sustainable public partnerships becomes particularly pressing given the increasing recognition, in many countries, that public participation in health research and intervention development is an important step to effectively identifying and addressing health inequalities [ 19 , 61 , 62 ]. Calls for widening participation, for the inclusion of under-served populations and for co-designing and co-producing health research, which have been gathering force in the last 20 years, have gained renewed urgency in the wake of the coronavirus disease 2019 (COVID-19) pandemic [ 63 , 64 , 65 , 66 , 67 ]. In the United Kingdom, Best Research for Best Health: The Next Chapter, published by the NIHR in 2021 to define the direction and priorities for NHS Research for the coming decade, exemplifies this urgency. The document asserts that a radical broadening of the scope of PPI (now renamed “public partnerships”) is essential for combatting health inequalities: it explicitly amplifies the ambitions of its 2006 predecessor by setting up as a key objective “close and equitable partnerships with communities and groups, including those who have previously not had a voice in research” [ 68 ]. Here, as in other comparable policy documents, emphasis on extending partnerships to so-called underserved communities rests on the assumption that, to some degree at least, PPI has already become the norm for undertaking research. This assumption, we argue, closes down in advance any engagement with the tensions we have been discussing in this paper, and in so doing risks exacerbating them. The document does recognize that for such inclusive partnerships to be established institutions must “work differently, taking research closer to people [..] and building relationships of trust over time” – though, we would suggest, it is far from clear how ready or able institutions are really to take on what working differently might mean.

Our study engages with and emphasizes this need to “work differently” while also arguing that the demands and expectations set up through regimes of performance management and their “logic of deliverables” are not favourable to an opening of a space in which “working differently” could be explored. In health research systems organized through these regimes, “working differently” is constrained by the application of the very templates, instruments and techniques which constitute and manage “business as usual”. Any ongoing effort to transform health research systems so as better to respond to growing health inequalities, our study implies, needs to combat, both materially and procedurally, the ease with which the disjuncture between embedding public partnerships and normative ways of undertaking research comes to disappear.

Limitations

We focus on the labour of the PPI workforce and their negotiation of performance management regimes, which means that we have not discussed relationships between PPI staff and public contributors nor presented examples of good practice. While these are important domains for study if we are to understand the labour of the PPI workforce, they lie outside the scope of this article. Furthermore, our focus on the UK health research system means that our conclusions may have limited generalizability. However, both the consolidation of NPM principles in public sector institutions and the turn to public and patient participation in the design and delivery of health research are shared developments across countries in the Global North in the last 40 years. Therefore, the tensions we discuss are likely to also manifest in health systems outside the United Kingdom, even as they may take somewhat different forms, given differences in how research and grants are costed, and roles structured. Finally, this project has elements of “insider” research since both authors, while working primarily as researchers, have also had experience of embedding PPI in research studies and programmes. Insider research has specific strengths, which include familiarity with the field and a sense of shared identity with participants which may enhance trust, facilitate disclosure and generate rich data. In common with other insider research endeavours, we have sought to reflexively navigate risks of bias and of interpretative blind spots resulting from over-familiarity with the domain under research [ 69 ] by discussing our findings and interpretations with “non-insider” colleagues while writing up this research.

Our qualitative study is one of the first to investigate how the UK PPI workforce is negotiating the current health research landscape. In doing so, we have focused on the UK’s NIHR since this institution embodied the redirection of performance management regimes towards public benefit by means of public participation. If PPI is set up as both the means of enabling this redirection and an outcome of its success, then the PPI workforce, the professional cadre evolving to support PPI, becomes, we argue, the site where the tensions of attempting this alignment are most keenly experienced.

We suggest that, while such alignment would demand a wholesale transformation of organizational norms, the regimes of performance management underpinning research ecologies may also work to foreclose such transformation, thus hollowing out the promise of patient-centred research policies and systems. Recognizing and attending to this foreclosure is urgent, especially given the current policy emphasis in many countries on broadening the scope, ambition and inclusivity of public participation as a means of increasing the reach, relevance and potential positive impact of health research.

Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author.

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Acknowledgements

S.P. presented earlier versions of this paper at the 8th annual conference of the Centre for Public Engagement Kingston University, December 2021; at the Medical Sociology conference of the British Sociological Association, September 2022; and at the annual Health Services Research UK Conference, July 2023. They are grateful to the audiences of these presentations for their helpful comments. Both authors are also grateful to the generous participants and to the NIHR Applied Research Collaboration Public Involvement Community for their sustaining support and encouragement during this time. S.P. also wishes to thank Felicity Callard for her comments, advice and suggestions throughout this process: this paper would not have been completed without her.

S.P. is supported by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration (ARC) South London at King’s College Hospital NHS Foundation Trust. The views expressed are those of the author and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

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S.P. developed the original idea for this article through earlier collaborations with L.M.B. whose long-term experience as a PPI practitioner has been central to both the project and the article. L.M.B. contributed to conceptualization, wrote the first draft of the background and undertook revisions after the first draft including reconceptualization of results. S.P. contributed to conceptualization, undertook data analysis, wrote the first draft of findings and discussion and revised the first draft in its entirety in consultation with L.M.B. Both authors read and approved the final manuscript.

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Papoulias, S., Brady, LM. “I am there just to get on with it”: a qualitative study on the labour of the patient and public involvement workforce. Health Res Policy Sys 22 , 118 (2024). https://doi.org/10.1186/s12961-024-01197-5

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Lecturers’ perceptions of the influence of AI on a blended learning approach in a South African higher education institution

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Published: 02 September 2024
Volume 3 , article number 135 , ( 2024 )

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Debbie A. Sanders 1 , 2 &
Shirley S. Mukhari 1 , 3

In this study, the researchers explore lecturers’ perspectives on the impact artificial intelligence (AI) has on blended learning within the context of South African higher education. AI is transforming traditional teaching and learning by enabling academic institutions to offer computerised, effective, and objective educational processes. The research was conducted to address the growing need to understand lecturers’ viewpoints on how AI can enhance educational practices and overcome existing challenges in blended learning environments. To investigate this phenomenon, the researchers applied the Substitution, Augmentation, Modification, and Redefinition (SAMR) model as theoretical framework for the study. Their qualitative research undertaking employed a singular case study design focusing on 15 lecturers from the College of Education at a selected academic institution, to arrive at an in-depth understanding of lecturers’ experiences and perceptions of how AI is integrated in blended learning. The researchers examined both the benefits and challenges associated with a blended teaching and learning mode, in the context of AI integration. The data collection process involved semi-structured focus group interviews that allowed for in-depth discussions to be conducted. This was complemented by detailed document analysis to analyse the course materials and teaching methods used by the lecturers. Homogeneous, purposeful sampling was applied to select participating lecturers who shared specific characteristics relevant to the study. Data analysis involved coding through the induction method, which helped to reveal relevant codes that were subsequently categorised. The study also included a comprehensive literature review of recent research findings, which were correlated with the collected data. The findings underscored the critical need for supportive measures, such as management backing, enhanced training opportunities, professional development initiatives, reliable technological infrastructure, improved internet connectivity, and additional time allocation, for the successful implementation of blended learning which integrates AI. This study contributes valuable insights into, and discussions on, the implications of adopting AI in a hybrid learning environment.

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1 Introduction

Over the past few years there have been notable advances in supporting lecturers to enhance their teaching methods, and in improving students’ learning experiences through the adoption of blended learning. Defined as a combination of face-to-face (F2F) and online learning, blended learning offers more flexible learning experiences that are also deemed to be more effective. Also known as "brick-and-click" instruction, hybrid learning, dual-mode instruction, blended pedagogies, or HyFlex, targeted, multimodal or flipped learning [ 5 , 38 ], this approach is becoming increasingly popular. The approach, which combines traditional classroom F2F learning with online components, facilitates the application of asynchronous teaching and learning in educational settings [ 16 ]. In recent years, educational institutions have widely embraced blended learning as the preferred teaching method, expressing appreciation for its flexibility, timeliness, and uninterrupted learning opportunities. As hybrid learning gains popularity, so it has become increasingly important to find new ways of improving the effectiveness thereof [ 17 ]. Recent developments in artificial intelligence (AI) are one way of enhancing the efficacy of blended learning approaches. With the integration of AI into academic environments, individualised learning experiences can be provided, administrative tasks can be automated, and such systems can be adapted to student needs [ 20 , 44 ]. For these reasons, the researchers sought to understand lecturers’ views on the relationship between AI and blended learning, as those perspectives are crucial for developing effective teaching and learning practices in higher education contexts.

AI involves the study and development of computer programs that display ‘intelligent’ behaviour, mindful of the fact that machine intelligence is distinct from the natural intelligence that is inherent in humans and animals. Other definitions of AI examine efforts to enable computers to possess intelligence [ 19 ]. Ultimately, AI extends much further than just robotics, however, to include the human capacity to program computers and other technology-enabled devices, so that they comprehend the principles of intelligent thought and behaviour. As a key invention of the Fourth Industrial Revolution (4IR), AI is considered one of the most influential technologies of our time [ 19 ]. For the purposes of this research, AI will be taken to refer to the development of computer systems capable of performing tasks that typically require human intelligence, such as learning, problem solving, and decision making. From the point of view of lecturers, such integration would require them to adapt their teaching and learning approaches, to make them more efficient and effective in addressing diverse student needs [ 25 ].

It is against this background that the researchers felt the need to investigate what impact AI has on blended learning, which includes lecturers having to revisit the way in which they usually lecture (the educator teaching, and students listening and regurgitating what they have been taught), to scenarios where AI is infused into a hybrid learning approach. It is crucial to emphasise that, in the context of this paper, blended learning is deemed to comprise more than the mere incorporation of technology into an academic programme. The adoption of the term, in this instance, aligns with what Lee [ 24 ] describes as a hybrid teaching approach, integrating traditional F2F lecturing with the latest, updated technologies. This mode aims to enhance student success and promote the relevance of the course content. Interaction among students, and between lecturers and the student cohort, is accomplished through various internet-enabled learning technologies, including platforms such as online discussion forums [ 3 ]. These technologies play a crucial role in promoting communication between educational stakeholders. Consequently, the smooth integration of conventional classroom instruction with e-learning offers valuable support for students’ asynchronous and collaborative learning [ 15 ]. In addition, the use of AI supplements these interactions by providing personalised feedback, allowing for two-way discussions, and for learning resources to be adapted to individual students’ needs. This combination of traditional and e-learning environments through the adoption of AI technologies makes for a more engaging and effective educational experience. It improves educational access, and promotes inclusive and equitable education, resulting in a sustainable, efficient, and accessible system of blended learning [ 3 ].

Although blended learning is not a novel concept, its use has remained largely unchanged. Its numerous challenges require further and more in-depth research into its efficacy [ 5 , 38 ]. Various aspects, including the specific technological tools and learning approaches used, and the overall quality of the teaching and learning on offer, need examination [ 5 ]. While blended learning has long been used as an approach to enhance students’ learning experiences, much of the research has focused on countries in the Global North, such as Belgium, the United Kingdom (UK), and Italy [ 6 ]. Limited research has been conducted in the South African context in this regard [ 43 ]. Notably, a search on Google Scholar revealed that only minimal related research has been published in the past 5 years (only eight research resources), with none of them originating from South Africa. Despite the increased uptake of hybrid learning in academia, AI is often perceived as a separate technological tool with limited influence on teaching and learning approaches. To enhance the effectiveness of blended learning in higher education contexts, it is essential to identify and understand lecturers’ views on the incorporation of AI into their teaching and learning, taking into account the SAMR model [ 34 ].

The significance of the study thus lies in elucidating lecturers’ viewpoints on the impact which AI and blended learning have on teaching and learning. The researchers also set out to assist higher education institutions (HEIs) in creating, adapting or changing conditions so that they are more relevant and meaningful, and ultimately enable lecturers to ensure that students are more successful in achieving specific learning outcomes. Clearly, AI is a tool that must be embraced in this modern, ever-evolving technological world.

The main research question designed to guide the study, was:

How do lecturers perceive the influence of AI on blended learning in the context of a South African higher education institution?

Four sub-questions were also formulated in this regard:

How do lecturers in South African higher education institutions perceive and integrate AI technologies into blended learning lessons within the SAMR framework?

What challenges do lecturers identify when incorporating AI into blended learning lessons, considering the SAMR levels of substitution, augmentation, modification, and redefinition?

How does AI influence student engagement, interaction, and learning outcomes in blended learning environments?

What support mechanisms do lecturers require to ensure the successful incorporation of AI into blended learning lessons?

Following the above introductory discussion on lecturers’ perspectives on blended learning and AI integration, the sections which follow focus on a comprehensive literature review on the topic, the theoretical framework chosen for this research, an exploration of the selected research methodology, the findings, and recommendations for the successful implementation of blended learning infused with AI. Lastly, concluding remarks summarise the key findings, and outline implications for future research and educational practice.

After exploring the background and rationale for this study, it is crucial that this study examines the existing body of research related to blended learning and Artificial Intelligence integration in higher education which is the focus of the next section.

2 Literature review

2.1 blended learning as an approach to teaching and learning.

In recent years, the educational domain has experienced significant transformations, driven by the continued evolution of information technology. One notable outcome is the emergence of blended learning, a pedagogical approach that integrates diverse methods of delivering information, such as web-based software courses, coupled with the management of practical knowledge [ 33 ]. According to Damanik [ 12 ], Choi and Park [ 10 ], and Qiu et al. [ 35 ], blended learning can be implemented both on- and offline. Bozkurt [ 8 ] expands on this, emphasising that blended learning encompasses F2F interactions and online engagement through specific mediums. The positive impact of the blended learning model on students’ learning outcomes, through fostering heightened engagement, is echoed by Santosa et al. [ 39 ]. This model, as observed by Nugraha et al. [ 31 ], also enhances students’ problem-solving abilities and understanding of the module content. This ensures adaptability and flexibility that caters to individual students’ needs, preferences, and schedules [ 43 ]. While initially designed for specific modules and their content, this approach prioritises student-centred satisfaction [ 43 ], thereby supporting HEIs in pursuing their goals and ultimately achieving the successful attainment of the learning outcomes set [ 38 ]. At its core, the concept of blended learning is built on the understanding that learning is not a singular, isolated event, but rather an ongoing, continuous process [ 33 ]. This transformational shift aligns with the modification level of the SAMR model, as it goes beyond merely substituting traditional teaching methods with technology, instead modifying the entire learning experience. Admittedly, the development of efficient blended learning systems can be demanding, particularly in respect of their endurance and flexibility to adapt to modern technological developments [ 3 ].

The integration of Artificial Intelligence (AI) in blended learning environments has been the subject of increasing debate in recent years. A review of the literature reveals that while there are some global studies completed which have explored various aspects of AI in education, research originating from South Africa is notably sparse. Alshahrani [ 3 ], Ferry et al. [ 13 ], and Rahman et al. [ 37 ] have all examined the impact of AI on student engagement and learning outcomes in blended learning, highlighting the potential benefits of AI-driven feedback and personalised learning experiences. However, research from a South African context is underrepresented, which may limit the generalisability of these findings to local settings. This gap highlights the need for more region-specific studies, particularly in HEIs.

The year 2017 marked a significant milestone, with extraordinary and unique developments in our understanding of the possibilities of the merging of technology and AI. As a rapidly advancing field, AI has the potential to influence the future of information technology and, for this reason, training in that regard is imperative [ 33 ]. The study of AI is fascinating and intriguing, representing the future of information technology. AI has the potential to enhance people’s lives by ensuring that tasks are accomplished more rapidly and more accurately. Petrova [ 33 ] suggests that soon AI will be integrated into all platforms and technologies, across different spheres. This development represents a shift toward the redefinition level of Puentedura’s [ 34 ] SAMR model. It transcends the traditional roles of both lecturers and students and introduces new possibilities for teaching and learning through the use of technology. While there is still substantial work ahead, AI empowers lecturers to achieve more—and with greater efficiency—than ever before. In the past, AI was a technology that instilled fear in many. The notion that computers could think and learn like humans raised concerns about our ability to comprehend and constrain machines. However, as we move away from the pursuit of human-like AI, we can now view its progress as a tool serving to develop and enhance every industry [ 33 ].

AI stands out as a potential answer to improve the efficiency and durability of blended learning systems [ 3 , 23 ]. Through the use of AI techniques such as machine learning (ML), natural language processing (NLP), and chatbots, opportunities are created which allow for the automation of diverse features of the learning journey, including content delivery, assessment, and feedback [ 3 , 22 ]. Furthermore, AI allows for the customisation of the learning experience for individual students, ensuring increased engagement and enhancing learning outcomes [ 3 ]. The fact that AI makes it possible for lecturers to adapt and automate their teaching, represents a change in traditional teaching and learning methods, aligning with the substitution as well as modification levels of the SAMR model, as technology can be used as a direct substitute for conventional teaching and learning methods, while also accommodating or revealing new capabilities. It offers a vast range of new possibilities to help ensure the successful achievement of a module’s learning outcomes—something that was not possible with conventional approaches.

It became clear to the researchers that while relevant, limited studies on this theme exist in South Africa, Mhlanga [ 27 ] and Mokoena [ 28 ] explored the challenges and opportunities of implementing AI in South African HEIs. These studies highlight the need for more specific approaches that consider the unique socio-economic and technological constraints, such as limited access to high-speed internet and the variability in digital literacy among both students and staff. These insights are crucial for understanding how AI can be effectively integrated into blended learning environments in South Africa, ensuring that such integration is successful, equitable and sustainable.

Moreover, there is a critical need for research that addresses the localised implementation of AI-driven blended learning solutions, particularly in rural and under-resourced areas where access to technology is inconsistent [ 27 ]. Such studies would provide valuable insights into how AI can be utilised not only to enhance learning outcomes but also to bridge the educational disparities that persist across different regions of the country. While limited, some relevant studies do exist. Mhlanga [ 27 ] and Mokoena [ 28 ] explored the challenges and opportunities of implementing AI in South African HEIs. These studies highlight the need for more specific approaches that consider the unique socio-economic and technological constraints, such as limited access to high-speed internet and the variability in digital literacy among both students and staff. These insights are crucial for understanding how AI can be effectively integrated into blended learning environments in South Africa, ensuring that such integration is successful, equitable and sustainable.

Integrating AI into blended learning systems offers the potential to establish an education system that is not only efficient, but also sustainable. The use of AI in education, particularly in blended learning, revolves around delivering personalised learning experiences, and optimising course delivery [ 3 , 2 , 24 ]. Through the adoption of AI technology in hybrid learning systems, it is easier for lecturers to analyse student performance data for a personalised learning experience which aligns with individual strengths, weaknesses, and interests [ 3 , 25 ]. The implementation of AI in blended learning streamlines tailored assistance for students. Alshahrani [ 3 ] concurs that AI allows for responsive interaction. This corresponds to the augmentation (A) level of the SAMR [ 34 ] model, where technology is used to improve the learning experience, exceeding what was achievable with traditional methods. Personalised support can easily be based on individual student needs. The personalised approach assists students in navigating complex concepts, thus helping to ensure the achievement of learning outcomes, and ultimate success.

AI is also conducive to enhancing teaching and learning methods, increasing efficiency through automated administrative tasks, and refining content delivery. Introducing AI tools to ensure a sustainable and efficient blended learning system allows lecturers to lessen the strain on the environment, by reducing paper usage and minimising the carbon dioxide emissions associated with physical (F2F) lectures or meetings. This not only improves educational effectiveness and accessibility, but also empowers students to acquire the essential knowledge and skills for building a sustainable future [ 3 , 36 ].

Viktorivna et al. [ 40 ] point out that AI serves to enhance student engagement, and the effectiveness of their learning. AI also facilitates a more straightforward explanation of subject matter [ 32 ], thereby encouraging students to develop and enhance skills required in the twenty-first century [ 11 , 42 ]. AI is a valuable educational resource for blended learning, as it grants access to an ever-expanding range of learning materials. Furthermore, AI helps in the creation of lessons, quizzes, and rubrics which allow lecturers to reorganise the curriculum and content of a module. AI-generated resources can even be customised to align with students’ instructional preferences, thereby fostering a flexible and inclusive learning experience [ 3 ].

Various studies have shown that the infusion of AI in a blended learning module enriches the learning process for students, helping them attain specific learning outcomes [ 13 , 14 , 37 , 39 ]. The collaborative and conversational capabilities of AI enhance the overall learning experience, resulting in an enjoyment of the course and heightening active participation among the student cohort. Concerted engagement delivers improved learning outcomes, and a more profound understanding of the subject matter [ 3 ]. This aligns with the Augmentation (A) level of the SAMR model, as technology (AI in this instance) goes beyond merely enhancing traditional methods, to develop a more interactive and engaging learning environment, thus fostering increased student participation and leading to a better grasp of the subject matter.

The AI-based blended learning model boosts students’ digital literacy levels as well as their 21st-century thinking skills [ 37 ]. This innovative approach helps to improve their critical thinking skills, for use in the learning process [ 18 ]. Ultimately, models can be created using a variety of AI-based technologies, thereby saving lecturers time and enhancing students’ learning opportunities [ 37 ].

In higher education, large class sizes make it difficult for lecturers to offer individualised teaching and can impede swift and direct student support. AI negates this challenge by rendering personalised support. As such, AI delivers real-time answers and support, easing the workload on lecturers and enriching the learning experience. The rise in popularity of AI has initiated extensive discourse and research regarding its potential influence in the education sector, particularly in higher education where limited lecturer–student ratios present unique challenges [ 3 ]. Thus, it is clear that AI serves as a valuable asset in blended learning.

AI also enables the delivery of customised support, feedback, and motivation to students. Investigating these aspects will further our understanding of AI’s integration in blended learning, unveiling fresh insights to guide the design, implementation, and ethical use of related technologies in educational environments that adopt a hybrid learning approach [ 3 ]. Since this is a relatively new technological development and thus a relatively novel approach to teaching and learning, further research is needed, especially at HEIs, to analyse the exact impact on students’ performance. As Rahman et al. [ 37 ] concur, this approach needs further development. This research paper extends the current knowledge base in the field of blended learning, particularly in higher education, by providing insights into the integration of AI for enhanced student literacy, thereby filling a significant gap in the existing literature. Closing this gap will not only expand our understanding of emerging educational practices, but also provide valuable insights for educators, institutions, and policymakers aiming to optimise the student learning experience.

The follow section reviews the theoretical framework that guided this research.

3 Theoretical framework

For this research, the Substitution, Augmentation, Modification, and Redefinition (SAMR) theoretical model was selected, to establish a solid foundation for investigating intricate aspects of AI’s influence on blended learning in HEIs. The model was chosen for its applicability to an understanding of the transformative impact of AI on blended learning, within the South African higher education milieu.

As per Puentedura’s [ 34 ] SAMR model, digital technologies can either enhance or transform educational practice. Enhancement involves substitution without functional change, or augmentation with functional improvement. Transformation, by contrast, requires significant task redesign or redefinition, leading to the creation of new tasks that were previously inconceivable. The model, which explores the creative application of technology to enhance the learning experience, serves as a useful guide for lecturers facing pedagogical changes as a result of using new learning technologies in their courses [ 30 ].

The SAMR model comprises four hierarchical levels. Firstly, the substitution level in which technology is used as a direct substitute for a traditional tool, with no functional change. At this level, the lecturer is tasked with substituting an older technology to perform the same activities as previously. While this may set the stage for future development, it is unlikely to have a significant impact on student outcomes at this stage [ 30 ]. The second level, augmentation, prompts lecturers to consider whether or not the available technology improves their teaching and learning. Instead of merely observing how students performed a given task before, lecturers must now focus on specific features of the technology, to accomplish the task more effectively, informatively, and swiftly. This approach aims to enhance students’ performance in completing assigned tasks [ 30 ]. Thus, technology acts as a direct substitute, with some functional improvement. The third level is the modification level in which technology allows for significant task redesign and, during modification, the lecturers’ objectives are to successfully achieve lesson outcomes with technological assistance. Teaching methods are thus adapted to ensure the incorporation of technology. While the syllabus remains unchanged, teaching approaches are modified to enable students to attain new goals that were previously deemed challenging [ 30 ]. The final level of redefinition empowers lecturers to replace older teaching techniques with newer, more effective teaching ideas. This is achieved through the use of technology, which allows for the creation of tasks once deemed inconceivable [ 7 ]. These teaching methods mainly seek to capture and retain students’ attention [ 30 ].

The SAMR framework enhances the value of the accumulated data, by offering a decision-making model for assessing the design of research interventions. The lowest levels—substitution and augmentation—encourage participants to actively engage, thus overcoming challenges related to technology, pedagogy, and their consequences. At the higher levels—modification and redefinition—the design of research questions becomes crucial for considering potential challenges in participants’ understanding of increasingly complex topics. This approach aims to purposefully overcome obstacles associated with the evolving nature of the scheduled tasks [ 4 ].

The application of the SAMR model in the context of this research involved a comprehensive examination of how AI influences blended learning practices. At the Substitution level, the study explored how AI replaces or replicates traditional teaching methods, offering insights into its role in directly substituting conventional approaches. Moving to the Augmentation level, the research assessed how AI enhances or improves existing educational practices, particularly in terms of providing additional features or functionalities that support teaching and learning. The Modification level focused on analysing how AI introduces significant changes in the execution of educational tasks, transforming traditional methods into more dynamic and effective practices. Finally, at the Redefinition level, the study evaluated how AI facilitates entirely new and transformative educational practices that were previously unattainable, showcasing its potential to revolutionise blended learning environments in ways that were not possible before.

Using this framework ensured that the research could follow a systematic approach to assessing the influence AI exerts on blended learning. It allowed the research to progress from simple enhancements to transformative changes. By offering a structured method for assessing the extent of AI integration in various facets of teaching and learning, the researchers gained valuable insights into the evolving landscape of educational technologies.

It is against this background that the chosen methodology is discussed next.

4 Methodology

4.1 research approach.

This study applied a qualitative methodology to investigate lecturers’ views on the influence AI has on blended learning. A qualitative approach involves a thorough exploration and grasp of phenomena, using non-numerical data and highlighting context, meanings, and subjective experiences [ 21 ]. The researchers deemed this method best suited for its exploratory nature of extracting relevant information. Focus group interviews were conducted, as Islam and Aldaihani [ 21 ] suggest, to allow for the coordination of discussions among a small group of participants, to mine and gather their views on a specific topic or phenomenon. For this research the focus was on the modules, lessons, and assessments of the participating lecturers. In addition, the researchers employed document analysis, which enabled them to explore the actual course content, lesson plans, and discussion forums.

In this way, the researchers arrived at an in-depth understanding of the specific phenomenon under investigation, as proposed by Morgan [ 29 ]. This approach enabled the researchers to scrutinise the lecturers’ experiences and opinions, focusing on their knowledge of, and encounters with, AI and blended learning.

The researchers applied a singular case study research design. This involved focusing on a single participant or unit of analysis, for an in-depth exploration of the intricacies and dynamics of a specific case [ 1 ]. This approach made it possible to conduct a thorough examination of the perceptions of lecturers employed in the College of Education at the HEI in question. The choice of design was prompted by ongoing developments in both AI and blended learning, which enabled the researchers to gain insights from lecturers actively engaged in related emerging educational practices.

4.2 Population and sample

Identifying a population for a particular research study enables the researchers to gather pertinent information from a smaller representative sample. This ensures that each distinct element of the collected information with similar characteristics is given the opportunity to be part of the sample. The researchers opted to employ a homogeneous purposeful sampling technique, intentionally selecting a group of participants who shared specific characteristics or traits deemed relevant to the research objectives. Participants were thus chosen based on shared traits, including gender, age, years of experience, the college in which they lectured, and their use of AI and blended learning, in order to align with the study’s purpose and objectives (Table 1 ).

Here, the group of participants selected were part of the same college at the specific HEI. The criteria for selection encompassed their approachability, availability to actively participate in the study, responsiveness to the interview questions, and willingness to share the content of their modules, lessons, and assessments. For this study, 15 lecturers agreed to participate: two males and 13 females, ranging in age between 32 and 63. Importantly, age has an impact on a user’s acceptance and embrace of AI in teaching and learning. Older lecturers often express discomfort with new technology adoption, and tend to be resistant to change. They are usually more comfortable with traditional ways of teaching and are fearful of using cutting-edge technological innovations. The participants’ readiness to openly share their course content, lessons and assessments, assisted the researchers in effectively analysing the collected data through the chosen document analysis data-collection technique. Consequently, the participants contributed valuable information that enhanced the depth of the study. Their active involvement in university affairs (especially the teaching and learning programmes) provided information that was highly relevant to this research .

4.3 Data collection

For this study, data were acquired by conducting interviews with the participating lecturers, enhanced by document analysis (see appendices A and B). The application of these data-collection techniques enabled the researchers to gather pertinent insights into the lecturers’ practical encounters with AI and blended learning in their teaching and learning. The use of open-ended, semi-structured interviews, along with document analysis, facilitated the analysis of the data, thus ensuring a thorough and precise in-depth study of the subject matter. The thematic approach adopted in this research aimed to pinpoint repeated topics identified in the data gathered. This enabled the researchers to concentrate on emerging themes specific to the realm of AI and blended learning, rather than providing mere synopses of the data [ 9 ].

4.4 Data analysis

To gain valuable insights from the participants’ answers to the interview questions, and information derived from the document analysis, a thorough study and interpretation of the collected data was imperative—an analytical process which is crucial for answering the research questions effectively. The researchers actively engaged in interpreting, consolidating, and synthesising the lecturer participants’ statements, to assign meaning to the data. This involved transcribing, comparing, and scrutinising the interview responses, along with the content of the modules, lessons, and assessments. The participant responses were coded manually, using letters of the alphabet, to ensure anonymity. Each response was tagged with a corresponding letter, making it possible to trace every piece of data back to the specific participant who supplied it. Each statement was carefully linked to specific codes and themes, especially given the fact that AI does not replace F2F lecturing, but rather augments teaching and learning. The coding process involved categorising data into the SAMR [ 34 ] levels, to reach conclusions about how lecturers perceive AI's influence on different aspects of blended learning.

The thematic approach was used to identify patterns and themes in the data, which were then related back to Puentedura’s [ 34 ] SAMR model. This allowed for a comprehensive review of how AI is being used at different levels of integration in a specific hybrid learning environment. An inductive approach, specifically axial coding, was followed to analyse the data collected. This involved a systematic comparison of the gathered data to identify codes, categories, and subcategories. A natural analysis of the data, without preconceived notions, was achieved by using an inductive approach, which enabled an unbiased analysis of the lecturers’ actual experiences. Through this comparative analysis, the researchers aligned the collected data with information derived from the literature review. The adoption of these methodologies facilitated the analysis of findings, reinforcing the credibility and reliability of the data. The theoretical justifications for this approach included grounding the findings within the SAMR framework, to enable the data-analysis process to align with the study objectives and research questions throughout.

4.5 Trustworthiness in data collection and analysis

Ensuring the credibility and trustworthiness of research findings is the prerogative of every qualitative researcher. In this study, the researchers developed a lasting, reliable, and open relationship with the participants. This approach guaranteed the latter’s willingness to actively participate in the study, and to share their personal experiences of the impact which AI has on blended learning. Moreover, the lecturers were encouraged to review and offer feedback on the researchers’ summary of the interview responses, further confirming the accurate representation of all data, and strengthening the trustworthiness of the research.

The coding process for this study was primarily conducted by Researcher A who began the initial coding of the qualitative data, identifying preliminary themes and patterns. To enhance the reliability of the analysis, researcher B participated in the second phase, where both researchers reviewed and validated the initial codes and themes. This collaborative approach involved both open coding and axial coding and ensured a thorough and unbiased interpretation of the data. A critical reader provided feedback and suggestions, which helped refine the coding framework and resolve any discrepancies. This process promoted credibility by introducing different perspectives, which prevented individual prejudice and improved the accuracy of the data interpretation. Transparency was achieved by clearly documenting each researcher’s role and contributions, making the process open to scrutiny and validation by other future researchers. The method ensured the reliability and comprehensiveness of the data analysis and actual results.

The researchers adhered strictly to qualitative research principles, ensuring transparency in their data-collection methods and meticulousness in their data-analysis techniques. Participants were continually asked to check the researchers’ notes, interpretation of the interviews, and transcriptions (member checking). Detailed descriptions of the participants’ experiences were provided to enable the transferability of the findings. This precise approach guaranteed the reliability and validity of the findings. By integrating the findings from the interviews, document analysis, and literature review, the validity and trustworthiness of the conclusions were further enhanced. Through this methodological approach, the researchers ensured the trustworthiness of the research findings and were able to make informed recommendations based on the results reported on here.

4.6 Ethical issues

The chair of the department in which the research was undertaken, obtained comprehensive ethical clearance covering the entire department from the Research Ethics Review Committee of the College of Education of the particular HEI. This clearance authorises all researchers in the department to conduct research within the institution, under ethics clearance number 90060059MC.

In ensuring that the highest ethical standards were maintained, the researchers pledged to use codes to protect the identity and privacy of the participating lecturers. The lecturers were also required to give the researchers permission to record the interviews, and to analyse their module content, lessons, and assignments. They were explicitly informed that their participation was voluntary, and that they were free to withdraw from the study at any stage without fear of penalty.

4.7 Research findings

Here, the researchers summarise the outcomes of the research based on insights derived from the responses provided during the interviews with the participating lecturers, and the document analysis. The findings are organised to address the main research question and sub-questions.

4.8 Lecturers’ perceptions of incorporating AI technologies in their blended learning and teaching approach

Of the 15 participants interviewed, 12 reported using AI to ensure that student queries were answered, and that they could find additional information as required, thus personalising the entire academic journey. In the words of Lecturer H:

I use AI in my modules to ensure that students can easily obtain answers to their questions. It is an amazing tool which helps suggest supplementary resources based on students' progress. This ensures a learning experience which is better, as it is adapted to my students’ progress.

Lecturer C corroborated this:

These systems can answer questions, provide information, and simulate conversation, creating an amazing and enjoyable interactive environment.

The same 12 lecturers deemed AI very useful for facilitating discussions between lecturers and students, and students amongst themselves. This was achieved because AI streamlined communication, enhanced interaction, and provided valuable support. Lecturer F said:

AI has significantly improved communication channels; it allows me to develop interactive and engaging discussions between students and between students and myself, and even encourages students to discuss the course content amongst themselves.

Lecturer H concurred:

The use of AI chatbots has created a space for students to collaborate effectively. This offers immediate assistance and helps develop a sense of collaboration in our blended learning environment.

All the interviewees maintained that the use of AI to generate relevant and customised learning materials and assessments was a very useful feature that could easily be adopted in blended learning modules. In this regard, Lecturer C said:

I use AI to create customised learning materials, quizzes and even games that align with the specific learning outcomes of my modules.

Lecturer G stated:

I find that the fact that AI can create adaptive assessments that adjust difficulty levels based on the individual performance of my students, is very useful.

Five participants highlighted the value of AI for translation. This was considered extremely useful, particularly in the South African context with 11 official languages. Lecturer M explained:

The ability of AI to facilitate translation greatly benefits our students from diverse backgrounds. It is so easy for any of us [lecturers and students] to quickly translate a word or even a whole paragraph, which makes the understanding of the module so much easier.

Lecturer H added:

I find that it helps students who are more comfortable in their home language to participate in the course content. This ensures that learning materials are accessible to everyone, regardless of their language preference.

The researchers’ document analysis showed that lecturers who mentioned the benefits of AI for creating customised learning materials and adaptive assessments had indeed merged these elements into their module sites. This correlated with the findings obtained from the interviews, where 12 of the 15 participating lecturers highlighted the positive impact AI had on facilitating communication, enhancing interaction, and offering support in hybrid learning environments. In addition, the analysis revealed instances where AI tools were used to support F2F classes by providing real-time feedback and interactive activities, thus enriching the blended learning experience. Using technology to individualise learning experiences and adapt teaching strategies in real-time helps students adapt to such approaches, thereby supporting traditional teaching methods and enriches learning environments.

Lecturer C had integrated AI-generated, scenario-based case studies into the course material. The document analysis revealed a scenario related to cultural integration through language teaching and learning. Students were presented with a case study involving a classroom with learners from diverse linguistic and cultural backgrounds. They were tasked with designing a language lesson that not only focused on language acquisition, but also promoted cultural understanding and integration. AI was used to evaluate the students’ answers to the case study. Based on individual performance, the system provided feedback to each individual student, and suggested additional resources or challenges to focus on specific areas of improvement in designing the language lesson.

The document analysis (as outlined in the second criterion, which aimed to “examine evidence of how assessments reflect the unique contributions of AI to student learning outcomes”) also ascertained the presence of adaptive assessments that were able to adjust complexity levels based on individual student performance. Lecturer G, who felt that AI was beneficial for creating such assessments, had incorporated quizzes with dynamic difficulty levels into the module site. Students were able to complete personalised assessments, with questions based on their previous performance.

The researchers noted the integration of AI-based translation services. Lecturer M, who highlighted the value of AI for translation, had implemented an AI-driven language translation tool on the module site. The researchers noted that some students had translated sections of the course content into their preferred language, promoting inclusivity and ensuring that the specific learning materials were clear to everyone, regardless of their language preference.

4.9 Lecturers’ perspectives on the challenges of incorporating AI into blended learning

Four of the lecturers interviewed, described the adaptation of new methods of teaching and learning, when using AI in their blended learning modules, as a challenge. In response to interview question 5 (What challenges have you encountered when incorporating AI into blended learning, and how did you overcome them?), Lecturer H commented:

Incorporating AI into my modules requires a delicate balance. I found that at times AI tends to minimise the importance of traditional teaching and learning methods, and not actually enhance them.

Lecturer B said:

Finding the right blend is crucial, so students benefit from the best of both worlds. AI must enrich my module and definitely not disrupt it … [We have to find] a balance between the technology and the personalised touch.

Lecturer C indicated:

… it can be a challenge to decide exactly where AI should be incorporated into the actual content of the course. Determining this often requires me to rethink my learning outcomes and approaches to teaching the content of my modules.

Twelve participants expressed the view that resistance to change was a major impediment to the successful adaptation of AI in blended learning modules. This aligns with responses to Interview Question 9 (In your experience, what support or resources do lecturers currently require when implementing AI in blended learning?) where Lecturer G noted:

Change is always met with resistance, especially when it comes to technology, particularly amongst us older lecturers. Some may see AI as a threat to the traditional way of teaching.

Lecturer H stated:

There's a comfort in the familiar, and AI represents a significant shift. Overcoming resistance requires effective communication. It also requires practically exploring the uses and benefits of AI.

All the participants mentioned that, although AI definitely saved time, problems were experienced with finding additional time to investigate new technologies and adapt their modules accordingly. In the words of Lecturer A:

While AI streamlines certain processes, the challenge lies in actually finding dedicated time for exploring its full potential to ensure that AI helps both me and my students successfully achieve the outcomes of the specific module.

Lecturer G mentioned:

Despite the efficiency AI brings, we must confront the reality of time constraints. It is essential to find a balance between adopting new technologies and meeting existing teaching demands.

Eight participants mentioned that it was becoming increasingly challenging to cope with the problem of the “digital divide”, which pertains to the technological proficiency of the students. Lecturer E noted:

There's a noticeable difference in access to technology among our students, and it's becoming increasingly challenging for us lecturers to bridge this gap as a result of the fast pace of new technological developments.

Lecturer F concurred, adding:

The issue of unequal access is growing. We need effective strategies to ensure all students are given equal learning experiences, regardless of their experience using computers for actual learning.

Several lecturers discussed ethical and privacy-related challenges with regard to the integration of AI in their blended learning module. As Lecturer F indicated:

I find that a huge challenge is that of ethical considerations, especially with regard to the privacy of student data. Finding the correct balance between using AI and protecting our students' privacy is an ongoing challenge. Additionally, there's a need for clear guidelines from management on how AI should be used ethically in our teaching, to avoid unintended consequences.

Lecturer G opined:

The challenge lies in providing the benefits of using AI to achieve the outcomes of our modules without compromising the privacy rights of our students. Open discussions on ethical guidelines and continuous awareness among lecturers and management as well as lecturers and students [are] essential to overcoming these challenges successfully.

4.10 Lecturers’ perspectives on AI's impact on student engagement

All 15 participating lecturers noted that using AI in their blended learning modules was beneficial, but not all believed they were using AI to its full potential, admitting there was room for improvement. Lecturer F stated:

While AI has enhanced certain aspects of my lecturing and interaction with my students, I really feel that there's much further potential for the use of AI in my modules, especially with regard to the advanced AI functionalities and typing in the correct prompts.

Lecturer O opined:

Integrating AI into blended learning helps me improve the actual teaching of the content of my modules. This allows me to individualise the learning experiences of each of my students, to ensure that their needs and preferences are met.

Lecturer A agreed:

Using AI in my blended learning course helps me adapt to my students' needs. This makes the teaching and learning much more flexible and meaningful, as it allows me to develop an individualised teaching approach to each student's strengths and weaknesses.

Nine of the participants highlighted the significance of AI’s prompt feedback to the inputs provided and queries posted on the AI system. In response to interview question 6 ("Have you received any feedback from students regarding their experiences with AI-infused blended learning?"), Lecturer B mentioned,

The quick feedback of AI has really changed the learning experience. Students receive real-time feedback [on] their progress, allowing them to make [the] necessary changes immediately.

Lecturer K echoed this:

I see AI as a game changer. Its ability to offer instant, personalised feedback has been a real […] eye-opener. It helps students understand their strengths and weaknesses without delay. This helps ensure a more integrated and authentic learning environment. It helps in identifying gaps in understanding and adapting teaching strategies.

Lecturer N concurred, adding:

From where I stand, AI's ability to analyse student data can provide valuable insights for personalised teaching and learning, and allows for instantaneous feedback. As a result, students' entire learning process is enhanced, resulting in an improved ability to achieve their learning goals.

Lecturer B, who viewed the instant feedback of AI as beneficial for enhancing teaching and learning, had used AI to create scenario-based feedback activities. The document analysis identified instances where students were presented with virtual scenarios representing diverse language teaching situations, such as classroom settings, one-on-one tutoring sessions, and language immersion programmes. AI was able to instantaneously analyse students' responses and actions in each scenario, providing immediate, real-time personalised feedback on their answers. This integration of AI thus enhanced both asynchronous learning and synchronous F2F interactions, by offering immediate feedback during live sessions.

The interview responses of seven of the participants revealed that AI is able to easily automate administrative tasks, through machine learning algorithms and natural language processing. This analytical capability allows instructional approaches to be adapted to individual student needs, ensuring that they successfully attain the learning outcomes of the module. Lecturer C said:

AI tools can streamline administrative tasks, allowing me to devote more time to my students and support them, especially where they are encountering challenges.

Lecturer F added:

I've used AI to analyse student performance data, which helps me adapt the content of my modules and teaching methods to make them more interactive. This can easily be based on my individual students’ needs.

The document analysis, which aimed to examine evidence of how assessments reflect the unique contributions of AI to student learning outcomes (the fourth criterion on the document analysis) also showed that modules where AI was integrated into feedback mechanisms saw improved student engagement. Studying the module site of Lecturer F, the researchers discovered that s/he used AI to automatically grade assignments (multiple-choice and written) and give immediate feedback. The reports generated were instantaneous and showed specific trends which helped the lecturer adapt the teaching and learning of this particular module.

It is indeed important to note how AI supports F2F teaching in class. As a result of this approach, learning during live lectures is made more dynamic and responsive to student needs. This point was highlighted by Lecturer M, who said:

The use of AI tools allows for instantaneous feedback to my students’ questions during lectures. It can give them various suggestions for additional materials and let them engage in interactive activities during face-to-face classes that will allow them to engage more deeply with the material.

4.11 Lecturers’ perspectives on the support they require to successfully implement AI in blended learning

All the participating lecturers confirmed the importance of comprehensive training and professional development. The need for comprehensive training and institutional support emerged as a critical theme. Interview Question 8 ("What kind of training or professional development opportunities do you believe are necessary for lecturers to effectively integrate AI into their blended teaching methods?") prompted responses highlighting the importance of ongoing professional development. In the words of Lecturer G:

Access to ongoing professional development courses focused on AI is essential for us lecturers to keep up to date with the latest developments in this field.

Lecturer M noted:

Professional development should include […] theoretical knowledge of AI as well as, specifically for us, its practical application in blended learning contexts.

Four participants stated that technological support was imperative if AI was to be instituted successfully. Lecturer O suggested:

Dedicated support teams must be specifically set up to assist with any technical challenges we may come across during the implementation of AI into our teaching and learning. This includes prompt responses to technical glitches and troubleshooting, to ensure that everything works properly for both me and my students.

We need assistance with initial setup and implementation, and with ongoing technical issues that may arise. This could be problematic as our IT help desk is already so overburdened. More IT staff definitely need to be employed.

Having institutional support for incorporating AI into the curriculum, is crucial. This involves not only providing resources, but also creating a culture that values and encourages the integration of AI technologies into teaching practices. This was echoed by all the lecturers interviewed. In the words of Lecturer A:

Having institutional support for incorporating AI into the curriculum is crucial. This involves providing resources as well as creating an institution that values and encourages the integration of AI into our teaching and learning.

Lecturer O echoed this:

Institutional commitment is key to the successful integration of AI. This should also include dedicated policies, so that we lecturers know exactly the correct process of AI.

Additionally, setting aside dedicated time for lecturers to adopt AI technologies was deemed imperative, as mentioned by ten of the participants. Lecturer H opined:

Allocating specific time for training and hands-on experience with AI tools is crucial. We need the opportunity to explore and familiarise ourselves with this new, exciting technology. This will definitely help us.

Lecturer E noted:

Having dedicated time for learning and experimentation is essential. This would give us more confidence in the actual implementation. But our schedules are already so busy that I have to wonder if this is at all possible.

Next, we examine the findings of the research.

5 Discussion of research findings

Using the research findings as a starting point for drawing meaningful conclusions and contributing to scholarly discourse on the subject, this section provides a summary of the findings that correlate with the literature review. From the utterances of many of the participants it became clear that there is a positive attitude towards AI, its significance for blended learning, and the benefits for tertiary students, as long as HEIs make certain adaptations. This aligns with the Redefinition and Modification aspects of the SAMR [ 34 ] model used for this study.

The research questions sought to explore how AI influences student engagement, interaction, and learning outcomes in blended learning environments. Lecturer N’s opinion, that AI boosts the learning process as a whole, resulting in an improved ability to successfully complete the course , is consistent with the findings of Alshahrani [ 3 ], Ferry et al. [ 13 ], Fradila et al. [ 14 ], Rahman et al. [ 37 ] and Santosa et al. [ 39 ], who found that infusing AI into a blended learning module enriches the learning process for students, helping them to achieve the specified learning outcomes. The collaborative and conversational capabilities of AI enhance the overall learning experience, leading to an enjoyment of the course, and active participation by students. These findings support the SAMR [ 34 ] model’s Redefinition level, where AI transforms the learning experience. Accordingly, the researchers of this study recognised that while AI does enhance learning experiences, its integration must be carefully managed to avoid over-reliance on technology at the expense of fundamental pedagogical principles.

The research findings corroborate the potential benefits AI holds for blended learning, as identified by the interviewees. Lecturer H's use of AI for immediate student support aligns with the views of Alsaleem and Alghalith [ 2 ], Alshahrani [ 3 ] and Lee [ 24 ], who emphasise AI’s capacity for personalising learning experiences. Moreover, Lecturer B's opinion on the importance of using AI for the prompt integration of AI-driven feedback, is consistent with the findings of Alshahrani [ 3 ] and Khosravi and Heidari [ 22 ], which emphasise AI’s functionality of supplying instantaneous feedback to enhance the learning experience. This aligns with the Augmentation level of the SAMR [ 34 ] model, where AI enhances existing teaching and learning practices. This made it clear to the researchers that while AI-driven feedback can significantly improve learning efficiency, it also raises concerns about data privacy and the need for transparent feedback mechanisms.

The views of Weber et al. [ 41 ]—that resistance to change may be an obstacle to the effective implementation of AI—are consistent with the opinions of 12 of the study participants. Specifically, Lecturer G noted that transformation is often met with resistance, especially when it comes to technology, and AI may be perceived as a risk to the conventional mode of teaching. Addressing this resistance requires policy interventions and professional development programs to ease the transition and encourage AI adoption. This indicated to the researchers that creating a culture of continuous improvement and gradually embracing this new approach may prevent resistance to adopting AI by lecturers and their higher education institutions.

The perspectives of all the participants, as regards the significance of tailored training and professional development which are customised to their specific needs, align with the findings of Luckin et al. [ 26 ]. According to that study, training should be more specific, and be contextually relevant to the unique demands and settings of the educational environment. This approach encourages active engagement and participation. Lecturer M specifically noted that any related training should focus mainly on its application to blended learning, to be successful. This highlights the importance of ongoing professional development to keep pace with technological advances. Clearly, HEIs need to adapt their policies to integrate AI tools that support personalised and interactive learning experiences. This suggested to the researchers that for AI technologies to be successful in higher education, professional development programmes must be made easily accessible for lecturers.

Finally, as featured in Alshahrani’s [ 3 ] study, the ethical use of AI in educational environments that adopt a blended learning approach, must be considered. Two participants (F and G) expressed the same sentiment, stating that open discussions on ethical guidelines and continuous dialogue among lecturers, management, and students are essential for navigating these issues. This suggests that policy should include ethical guidelines for AI use in education, ensuring that such integration supports not only academic integrity, but also responsible teaching and learning practices. In view of these findings, the researchers concluded that there was a distinct need for the creation of specific ethical frameworks that would assist all stakeholders to address the emerging ethical concerns associated with AI use in higher education institutions.

5.1 Limitations of the research

It is important to note the limitations of this study, which affect the generalisability of the findings. First, the study was restricted to a single South African higher HEI and one specific college, which may limit the applicability of the results to other contexts or institutions. Additionally, the full impact of AI on the blended learning approach may only become apparent in the future, as the students from this cohort progress in their careers and enter their respective professions. Furthermore, AI is a rapidly evolving field, and its continual advancements could mean that the study’s findings might become outdated relatively quickly. Finally, the successful implementation of AI in blended learning modules may be hindered by the lack of requisite technological resources and infrastructure in some educational institutions, potentially affecting the feasibility and effectiveness of AI integration.

6 Conclusion and recommendations

This paper discussed the impact of AI on a blended approach to teaching and learning in a particular HEI. It was based on the perceptions of 15 participating lecturers who lecture in the same college, albeit in different departments. The insights were based on the lecturers’ familiarity, experiences of, and involvement with, AI, and its impact on their teaching and learning. This positioned them to discuss the perceived advantages, disadvantages and supportive measures needed for such an approach to be successful. The use of focus group interviews and document analysis enabled the researchers to correlate what was actually taking place in this field of research, with the literature review undertaken.

Puentedura’s [ 34 ] SAMR model was chosen as theoretical framework to guide this undertaking, since it enabled the researchers to investigate how AI could bring about transformative changes in blended learning within the domain of higher education. The results highlight the significance of using AI in hybrid learning contexts, which has great potential for transforming traditional teaching methods. The study highlighted the implications of adopting AI to enhance the effectiveness of blended learning which offers personalised feedback, interactive discussions, and adaptive resources to cater to individual student needs. The findings draw attention to the crucial role of supportive measures such as management backing, improved training and professional development opportunities, reliable technological infrastructure, and improved internet connectivity, in ensuring the successful use of AI for blended learning modules. The findings thus enhance the knowledge base of this emerging field of study, by clarifying the perspectives of the lecturer participants at a particular HEI. Moreover, the findings can support future research on this topic, and may be used by other educational institutions—even those catering for different age groups.

6.1 Recommendations for further research

Recommendations for further research include several key areas to enhance the understanding and implementation of AI in blended learning environments. First, investigating AI and blended learning across various HEIs, both within South Africa and internationally, would provide a more comprehensive understanding of lecturers' perceptions of AI's impact. Additionally, research should focus on the effect of AI on students’ achievement of learning outcomes, their engagement with modules, and their overall enjoyment of learning within hybrid environments. Examining specific support measures, particularly relevant training, could further assist lecturers in effectively integrating AI into their modules. Longitudinal studies are also recommended to track changes in lecturers’ perceptions as they adapt to and integrate AI over time. A thorough exploration of the challenges HEIs face during the implementation process should be considered to address potential barriers. Furthermore, research into the ethical implications of AI in education, including the development of necessary guidelines, is essential. Finally, future studies should aim to validate and expand upon these findings using quantitative methods, as this study was purely qualitative.

Data availability

The data that support the findings of this study are not openly available due to the privacy and confidentiality agreements with the participants. However, the data will be made available by the corresponding author upon reasonable request, subject to review and approval by the research ethics committee of the involved institution. Requests for data access can be made by contacting the corresponding author at [email protected].

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Acknowledgements

The authors acknowledge the cooperation of the lecturers who participated in the data-collection process, and the HEI under study, for allowing the research to be conducted.

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1.1 Appendix A: Interview question

The following is the set of open-ended interview questions the researchers used by the researchers to assess the lecturer’s view of the impact of AI on blended learning:

Can you describe your experience incorporating AI technologies into your blended learning lessons?

What specific AI technologies or tools have you used in your blended learning approach?

Can you share examples of instances where AI enhanced the effectiveness of your blended learning lessons?

In your opinion, what are the key advantages of integrating AI into blended learning?

What challenges have you encountered when incorporating AI into blended learning, and how did you overcome them?

Have you received any feedback from students regarding their experiences with AI-infused blended learning?

Have you noticed any differences in student performance or understanding between traditional and AI-infused blended learning?

What kind of training or professional development opportunities do you believe are necessary for lecturers to effectively integrate AI into their blended teaching methods?

In your experience, what support or resources do lecturers currently require when implementing AI in blended learning?

1.2 Appendix B: document analysis guide

The researchers used the following guidelines when analysing the module contents, lessons and assessments:

Assess whether the content and learning objectives of the module feature the integration of AI technologies ─ look for objectives that explicitly mention the use of AI to enhance specific skills or competencies.

Identify specific occurrences where AI enhances interactivity within lessons.

Look for evidence that assessments capture the unique contributions of AI to student learning outcomes.

Search for features that assist in the immediacy and effectiveness of feedback mechanisms through AI.

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Sanders, D.A., Mukhari, S.S. Lecturers’ perceptions of the influence of AI on a blended learning approach in a South African higher education institution. Discov Educ 3 , 135 (2024). https://doi.org/10.1007/s44217-024-00235-2

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Artificial Intelligence (AI)
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Substitution, Augmentation, Modification, and Redefinition (SAMR) theoretical model

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http://orcid.org/0000-0003-4078-0657 Ross Thomson 1 ,
Lucy Phillips 1 ,
http://orcid.org/0000-0002-8577-216X Sophie Orton 1 ,
http://orcid.org/0000-0001-9790-2796 Felix Naughton 2 ,
Tim Coleman 1
1 Lifespan and Population Health , University of Nottingham School of Medicine , Nottingham , UK
2 School of Health Sciences , University of East Anglia Faculty of Medicine and Health Sciences , Norwich , UK
Correspondence to Dr Ross Thomson; Ross.Thomson1{at}nottingham.ac.uk

Objectives To explore the acceptability and perceived motivations and barriers of using nicotine replacement therapy (NRT) to reduce the number of daily cigarettes smoked in pregnancy, rather than for stopping completely.

Design Telephone, semi-structured interviews, audio-recorded and transcribed verbatim. Transcripts were analysed using an inductive thematic analysis.

Participants Eighteen pregnant women in the UK, who were smoking or had recently stopped smoking, were recruited.

Results Half of interviewees reported having used NRT to reduce smoking during their current pregnancy, and there was overwhelming support for the UK National Health Service to recognise this as a potentially useful way to use these products. The cost and stigma associated with purchasing NRT products when pregnant were seen as barriers to using NRT in this way. The early offer of NRT for reduction along with a tailored, structured approach to support was seen as important.

Conclusions Using NRT to help women, who are unable to stop smoking, to reduce their smoking may be acceptable to pregnant women. This study found women were already using NRT alongside ad hoc strategies to reduce their smoking. Further research evaluating structured smoking reduction support, alongside concurrent NRT use is needed.

pregnant women
primary care
public health
qualitative research

Data availability statement

Data are available upon reasonable request. The data analysed during the current study are not publicly available but anonymised transcripts are available from the corresponding author upon reasonable request.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjopen-2024-085945

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STRENGTHS AND LIMITATIONS OF THIS STUDY

This study used online recruitment using Facebook adverts and social media posts that allowed researchers to identify and interview women from across the UK.

The use of social media recruitment will have excluded those without internet access or who use Facebook as a social media platform.

Using telephone, rather than face-to-face interviews, while more difficult to develop rapport with interviewees, is known to have advantages when discussing topics of a potentially sensitive nature.

Smoking in pregnancy is a major public health problem; it is the biggest preventable cause of adverse pregnancy and perinatal outcomes. 1–3 Globally, large numbers of pregnant women smoke and while slowly declining in high-income countries, rates are highest in Europe (8.1%) and the USA (5.9%). 4 In England, in 2020/2021, 9.5% of women were smoking during childbirth, with rates highest in economically deprived areas (Blackpool 21.4%). 5 However, an estimated 23.3% of women in the UK smoked at some point during pregnancy, 4 resulting in approximately 160 824 fetuses being exposed to smoking in pregnancy annually, 6 7 causing up to 5000 miscarriages, 300 perinatal deaths and 2200 premature births in the UK. 8

In Europe, for non-pregnant smokers, nicotine replacement therapy (NRT) products are licenced for reduction as well as cessation of smoking, 9 and evidence suggests that NRT used to cut down can induce successful quit attempts resulting in stopping smoking (RR for stopping smoking after using NRT to cut down, 1.87, 95% CI 1.43 to 2.44). 10 However, following the WHO’s recommendation that there is no safe level of smoking in pregnancy, 11 most countries’ guidelines urge abrupt cessation of smoking in pregnancy and jurisdictions, which recommend using NRT in pregnancy, only do so for cessation attempts. 12 In the UK, the National Health Service (NHS) only offers NRT to pregnant women if they are ready to quit smoking and offers no alternative support to the 45% of women who smoke during pregnancy, but who do not make quit attempts. 13 14 However, there is strong evidence that when pregnant women cannot achieve abstinence, reducing smoking is very likely to be better for theirs’ and their babies’ health than ‘smoking as usual’. There are dose-dependent associations between heaviness of smoking and birthweight, 15 low birth weight, 15–17 increased risks of adverse pregnancy and adverse neonatal outcomes 18 and babies born to women smoking fewer than 10 cigarettes daily are heavier than babies born to women smoking >10 cigarettes daily. 15 Helping pregnant women who cannot stop to instead reduce their smoking would substantially improve the health of up to 72 370 UK fetuses annually. 6 7 19

Pregnant women are not recommended NRT for reducing smoking due to safety concerns. Many animal studies demonstrate that nicotine could be harmful to the developing fetus, 20 and, in the USA and Australia, nicotine is classified as potentially a risk for use in pregnancy. 21 While it would not be logical to advocate nicotine use in pregnant women who do not smoke, systematic reviews suggest using NRT instead of smoking is protective not harmful to the fetus, 22 23 and pregnant women are exposed to far less cotinine (primary nicotine metabolite) from NRT than when smoking. 24 Compared with when only smoking, pregnant women on NRT patches who also used cigarettes smoked less each week, exhaled less CO but had similar cotinine concentrations. 25 Those offered ‘dual’ NRT for quitting (ie, patch and fast-acting NRT [eg, lozenge, spray, etc]) but who did not stop smoking and reported some cigarette use, smoked fewer each day, exhaled less CO and had lower saliva cotinine concentrations than when smoking only. 26

Qualitative work suggests that some pregnant women who are trying to stop smoking already use NRT to reduce their smoking to assist this. 27 However, other women are anxious about potential fetal harm from smoking and using NRT together, and some have reported viewing not quitting as a ‘failure’. 28 Previous studies, however, have only reported the views of women who use NRT to help them stop smoking. We know little about the acceptability of offering NRT to pregnant women who feel unable to stop smoking to help them cut down their daily smoking instead. As this would be a substantial change to current clinical practice, if it were to be considered as a treatment option, it would be very important to fully understand women’s views on this use of NRT.

We conducted a qualitative exploration of the acceptability of pregnant women, who were not necessarily receiving stop smoking support (SSS), of using NRT in pregnancy to reduce the number of daily cigarettes smoked, and the barriers to and facilitators for them using NRT in this way, rather than for stopping smoking completely.

We conducted a qualitative study using semi-structured telephone interviews. Ethical approval was granted by the Faculty of Medicine and Health Science Research Ethics Committee, University of Nottingham (reference number FMHS 442–0122). This paper follows the consolidated criteria for reporting qualitative research checklist for reporting qualitative research 29

Inclusion criteria

We included women who were aged ≥16, living in the UK, and who self-reported being currently pregnant and smoking or having quit smoking cigarettes during pregnancy. We also included women who either combined or replaced cigarette smoking during this pregnancy with other nicotine-containing products. Those who were unable to understand the study procedure sufficiently to provide consent and were unable to read or understand the study procedures in English or participate in an interview in English were excluded.

Recruitment

Recruitment took place between March 2022 and July 2023 using these methods.

Facebook banner adverts

We posted short advertisements on Facebook using algorithms to target specified demographics (eg, age, gender, location and interests).

The adverts displayed a link to an external webpage hosted by Jisc Online Surveys 30 containing a short screening questionnaire that determined eligibility. The screening questionnaire collected women’s name, age, smoking status, weeks’ gestation, email address and telephone number.

Social media posts

We set up accounts on different social media sites and forums (eg, Reddit, Mumsnet, Twitter) and posted links to the short screening questionnaire.

Participants from other studies

We also contacted participants from other studies conducted by the research group that had given consent to, and shown interest in, being involved in other research projects. Only participants where it was deemed that there was little possibility of cross contamination between ongoing projects, for example, if they were screened and found to be ineligible for an alternate study, were invited to complete the screening questionnaire for this study.

All women who completed the screening questionnaire and fulfilled the eligibility criteria were emailed a Participant Information Sheet. After 24 hours, a member of the research team made three attempts at contact to explain more about the study and offer the option of taking part in a telephone interview at a mutually convenient time.

Three researchers conducted the interviews (LP: female, MSc, health psychology background, non-smoker, RT: male, PhD, health psychology background, ex-smoker, SO: female, PhD, health psychology background, non-smoker). The interviewers introduced themselves as researchers from the University of Nottingham, obtained informed consent and recorded pregnancy and smoking information before commencing the interview. Interviews were audio recorded and transcribed verbatim by an external transcription service. Interviewees received a £20 shopping voucher as compensation for their time.

Interview topic guides were semi-structured and informed by the Theoretical Domains Framework 31 and COM-B model 32 covering the following topics: healthcare support for smoking during pregnancy, views on reducing smoking in pregnancy rather than stopping, knowledge and experience of NRT, barriers and facilitators to using NRT to reduce smoking and support/strategies for using NRT to reduce smoking in pregnancy (see online supplemental file 1 ). Interviews lasted 30–40 min.

Supplemental material

The data were analysed using inductive thematic analysis. This approach allowed themes and patterns within data to be identified, interpreted, organised and described. 33 Analysis was led by RT with the coding checked by a second researcher (LP) and was facilitated using NVivo 12 software. 34 Using the method outlined by Braun and Clarke, 33 35 the researcher who led the analysis familiarised himself with the data by reading and re-reading transcripts, systematically noting initial codes and patterns across the data. These were then collated into potential themes and subthemes, with all examples of the themes within the data gathered. Next, these themes were reviewed by RT and LP, ensuring they reflect the coded extracts and the entire data set. The themes were then further refined, with clear definitions and names for each theme given. All members of the research team provided input regarding reviewing and refining the final themes.

Patient and public involvement

Three women from our public involvement advisory panel, all of whom had lived experience of smoking during pregnancy, were involved in the funding application and the development of participant materials (eg, recruitment adverts topic guides for interviews, participant information sheets). They were also involved in the interpretation of the data. This involved asking them to read and comment on a selection of anonymised transcripts which provided valuable insights into the importance and inclusion of potential themes and allowed us to check how the data related to their own lived experience. 36

48 eligible women expressed an interest in taking part, from whom, we recruited 18 interviewees (10 via Facebook adverts, two from other social media posts and six from other studies). From the six women recruited from other studies, three did not meet the eligibility criteria for an NRT cessation study and three were from a carbon monoxide monitor study. We were unable to contact 30 women.

Of the 18 interviewees (mean age: 30 years), 15 reported having reduced their smoking since finding out they were pregnant, while three interviewees reported having stopped smoking. Pregnancy gestation ranged from 8 weeks to 36 weeks (mean: 20 weeks) with six interviewees reporting having smoked in a previous pregnancy. Nine interviewees had other children with eight being married, seven cohabiting and three reporting being single. Of the 18 interviewees, 10 were not actively engaged with stop smoking services, 12 had used NRT previously and nine had used NRT during their current pregnancy to assist in reducing their smoking. See table 1 for full interviewee characteristics. During analysis, we considered 18 participants provided us with adequate information power, 37 in terms of the quality of the interview dialogue, to offer sufficient new knowledge and insights in line with the aim of the study.

View inline

Interviewee demographics

Interviewees expressed varied views on smoking reduction in general and specifically on using NRT to reduce rather than stop smoking. These views are organised into three themes: (1) ‘views on smoking reduction’, (2) ‘views on using NRT for smoking reduction’ and (3) ‘advice and support needs’.

The findings are illustrated by extracts from participant interviews to bring transparency to the qualitative analysis. Interviewee identification numbers and whether, at the time of the interview, they had quit (Q) or reduced (R) their smoking or had used NRT to help them reduce their smoking in this pregnancy (NRT) are reported in parenthesis.

Theme 1. Views on smoking reduction

All the women we spoke to describe making efforts to reduce or stop smoking since finding out they were pregnant. Some were cut down as an alternative to abstinence:

Well I think cutting down, if you can’t quit then cutting down would obviously be, you need to do one or the other really. I mean I cut down from 50 to 20… I don’t know if I could give up the full lot. (Int 7,R)

Others said they were cutting down with a view to quitting in the future:

…I don't feel like I physically need one every day now, I can go a day or two without one,…, hopefully I’ll completely stop and then it’s like gone forever! (Int 10, R, NRT)

Stopping smoking was seen as being particularly difficult. Most of the women had tried to stop in the past but found it either too difficult or had stopped for a while but then relapsed back into smoking:

It’s not as daunting as just completely stopping. It does feel like I’m making a better decision (cutting down) rather than just stopping completely, because before I’d literally, I would just stop buying cigarettes and then it would become a problem because I’d become agitated, so it was just becoming a massive issue. (Int 5, R)

All women spoke about the difficulties of having to cope with the symptoms of tobacco withdrawal and the feeling of having to give something up was a common theme that was discussed as to why cutting down was considered easier than complete cessation:

I know giving up is quite hard but cutting down I can kind of live with that. I think the main reason is I’m kind of you telling myself it’s OK, I can still smoke but it’s just less. So, I think the fact that I’m still smoking has given me that peace of mind (from the stress of having to quit). (Int 2, R)

However, one woman, who had stopped smoking using NRT, was very much against using a reduction approach as they felt it would be too easy for relapse to pre-pregnancy levels of smoking:

…if anything, I think it opens the pathway to temptation a bit more… You have a bad day and you're like oh sure what’s another one, what’s another one, what’s one evening of a couple more, you know? Because you're still buying them and you've still got access to them, like, one of the things that we did when we found out we were pregnant I had cigarettes in the house is I made sure that they were binned straightaway with no access for me to get to. (Int 16, Q)

Views on cutting down and harm reduction

Half of those interviewees who believed they had successfully reduced their smoking expressed the view that reducing the amount of cigarettes they smoked would reduce the risk of harming their unborn child:

Well obviously, if you’re cutting down, you’ve got less toxins and less carcinogens going into your body and less of it going into the baby. (Int 7, R)

However, this view was not necessarily based on any advice, it was more of an intuitive view of how to reduce the risk to the fetus, and there was some uncertainty as to the efficacy of this approach in the context of the interview:

…think I’m not exactly sure because obviously I’m not a medical professional or anything but personally I would think your baby would be at less risk… but I’m not sure whether that is true or not, if that makes sense? (Int 1, R)

There was an acknowledgement that cutting down was a compromise, and women believed that although harm was reduced, it was not eliminated:

…yeah maybe the less of the substances are going into the bloodstream and the baby maybe… (but) you still smoke, so you still poison the baby, yeah. (Int 15, R, NRT)

Stigma around cutting down but continuing to smoke

The stigma associated with continued smoking in pregnancy, even at a reduced amount, was identified as an important barrier for cutting down smoking rather than complete cessation. Over half of interviewees spoke about still being seen by others as a ‘pregnant smoker’ who feels they are doing something wrong. The idea of people on the outside not understanding their individual situation was expressed in descriptions of feeling judged by people who would not be aware of how difficult they were finding stopping smoking and the lack of recognition for the progress they had made in reducing their smoking:

I’ve been pregnant and smoking and I’ll still go – doesn’t look really good, does it?… Well, no one looks at how many you’re smoking a day. They just see what’s currently there which is a pregnant woman smoking. (Int 6, R, NRT)

Two thirds of interviewees reported hiding their continued, although reduced, smoking from friends and family, fearing disapproval.

My nan hates it. She absolutely hates it. She’s getting very broody obviously because first great-grandchild so she’s very strict with me and I don’t smoke when I’m at her house but as soon as I get home, I do. (Int 7, R)

They reported feeling embarrassed and guilty and did not want to be judged as possibly harming the unborn baby. Some women also reported feeling disapproval or judgement from their healthcare professionals and so were reluctant to disclose their smoking status or discuss with them that they had been unable to quit completely and so had instead reduced their smoking:

Yeah, I told my midwife I don’t smoke anymore because she’s quite judgemental. So she doesn’t actually know I still smoke. (Int 4, R, NRT)

Theme 2. Views on using NRT for smoking reduction

Despite not having been advised to do so by any health professionals, during this pregnancy, half of interviewees had already used NRT to help reduce their smoking. There was overwhelming support among interviewees for the idea of having a recognised approach to using NRT to help women reduce their smoking when stopping completely was unobtainable:

I mean it, well hopefully it will help. I feel like if you can’t quit, and you can cut down and there’s things available to help, then why not help?… So, I’m sure if they give an option “OK we know you can’t quit right now, we know that’s not something that you can do right now, so here is something to help you cut down” I feel like that’s amazing. (Int 5, R)

Women improvised different, ad hoc, strategies to reducing their smoking such as lengthening the time between cigarettes or trying to only smoke at certain times, without any clear goal setting but could also see a way of integrating NRT to replace some of the cigarettes they smoked:

…yeah it might work to have a normal cigarette let’s say in the morning and in the evening and then during the day for example use the replacements. (Int 9, R, NRT)

One interviewee pointed out that the type of NRT most appropriate to help reduce smoking might need to be determined on an individual basis:

Mine is so habitual, it’s all about that. That’s why the inhalator works best for me. But for other people if it is purely a chemical then, you know, probably the patches would help brilliantly for them. (Int 3, R, NRT)

And that changes due to pregnancy may influence what type of NRT may be tolerated:

Personally, I have tried the gum that you can buy, but it’s the taste for me so I couldn't really have it because I was quite – early on in pregnancy I was quite sicky – so I couldn’t have the texture or the taste of it, so that went out the window. (Int 11, R, NRT)

Embarrassment associated with NRT

As many of the women were not actively engaged with a stop smoking service, they were having to source their own NRT and were conscious that by buying NRT in public they may be perceived by others as continuing to smoke while pregnant, even though NRT could equally be a sign of them having stopped smoking. Similar to the stigma associated with reduced smoking in pregnancy, over a quarter of interviewees described embarrassment when purchasing or using NRT while pregnant.

Being seen going to a pharmacist to purchase NRT while visibly pregnant was embarrassing for one woman, who described making her husband buy it on her behalf:

…like I made my husband carry the gum yesterday in Boots. Like I told him “I need to go and get some more gum” and like I wasn’t going to hold it. I’ll pay for it. We paid at the counter together but I don’t want to be seen even taking NRT when I’m quite clearly pregnant. (Int 4, R, NRT)

Women indicated that they would consider their choice of NRT based on how obvious it would be to others that they were using certain products:

I think if I was to open a patch out in public people would stare at me but if I had some maybe chewing gum I could kind of open the packet in my bag and kind of sneak it into my mouth because people would think it’s just normal chewing gum. (Int 2, R)

As in England, NRT is only provided at no cost to pregnant women who are in quit attempts, the expense of buying NRT for smoking reduction was seen as a barrier to this use of the treatment.

It is very expensive to do it self-funded… you don’t realise obviously say 7 or 14 day patches and you think right that’ll do me for the next 2 weeks, but you blink and then you need a new packet and then you run the risk (of running out) if you don’t have the money mid-month and especially with the cost of living happening at the moment, it’s just expensive. (Int 16, Q)

One interviewee had problems getting to the pharmacist on her day off at the weekend so had to absorb a higher cost to buy NRT from the internet:

…but I eventually gave up and just buy them on Amazon and we’ll just eat the cost, because the practicality of, like I can get Prime 1 day delivery, it’ll come out of the budget somewhere …. (Int 11, R, NRT)

Expectations around NRT

Over half the interviewees had previous experience of using NRT, both inside and outside of pregnancy, with varying success that may have influenced their enthusiasm for using NRT in the future. Some had previously tried different types of NRT products without successfully quitting smoking:

So, I’ve used nicotine patches. I was given them by a health professional to help – didn’t seem to work. I’ve tried the gum, that didn’t seem to work. I’ve tried to go cold turkey and then I ended up just smoking more than what I was smoking in the first place. (Int 5, R)

While others had used NRT in a successful quit attempt:

Yeah, when I quit before, probably last year, I used patches and gum. (Int 4, R, NRT)

Friends’ mixed experiences with NRT also appeared to have contributed to some participant’s mixed views about NRT. These experiences related to both NRT efficacy and product side effects:

And I know that from my friends’ experiences with like the patches for example, they haven’t really helped them cut down at all… I get like mixed responses about the patches… Some say that they’re good, and some say that they don’t really do anything. (Int 14, Q) I mean I’ve got a certain friend and she’s got like really sensitive skin, and she told me when she put the patches on she reacted really horribly to them. (Int 2, R)

Safety concerns of using NRT and smoking

There were some concerns expressed about the safety of using NRT and smoking at the same time and the possibility of getting too much nicotine:

I've got mixed feelings about it because I feel like when you smoke and when you use the therapy as well you might give your body more substances, more nicotine… because you're topping it up with the patches. (Int 9, R, NRT)

One interviewee thought that there should be a way of controlling the amount of nicotine that women take in and that some NRT products may be more useful at achieving this:

The only one I could see working is maybe you know the inhalers, where you can control the intake, yeah, you can control the intake of the nicotine going into your system so if you are going to have that fag, you’re not overloading but the patches wouldn’t work… There’s no point putting on a twenty a day patch and then smoking almost twenty a day. You’ve doubled your intake. At least with you know the gum, you can spit the gum out. You can remove gum, you can remove, everything else is removable. The patches aren’t! (Int 6, R, NRT)

Two interviewees reporting feeling sick while using NRT gum and continuing to smoke and worried they may have ‘overdosed’ on nicotine:

I’ve made myself feel very sick with the gum and with the patches by trying to have a cigarette on them. And I don’t know, that would be something I’d have to actually ask a doctor as to whether I’ve just made it up – coincidence or if it is that you can have too much nicotine. (Int 3, R, NRT) The gum tastes absolutely repulsive! And I always after having it, I felt like I needed to have a cigarette and then I’d actually often find that I almost – I don’t know if you could call it this, but I overdosed on nicotine and made myself feel very sick. (Int 15, R, NRT)

Theme 3. Advice and support needs

When considering what advice or support from health professionals would be helpful if NRT for smoking reduction in pregnancy were presented as a treatment option, women felt a tailored, encouraging approach that was offered early in pregnancy would be helpful.

Women reported the importance of adopting a tailored approach to supporting pregnant women use NRT to help reduce their smoking when abstinence was unachievable:

I would say every woman is different and everybody smokes different ways. Some smoke more than each other because all women are different… (how) slowly you cut down– depends on the person or the lady, how long it takes them. (Int 1, R)

It was suggested that women should have individualised reduction targets that could be monitored using an app to record the number of cigarettes smoked or by expired carbon monoxide measurements:

I don’t know if (stop smoking services) could come to a compromise in terms of if you're cutting down but you're still going to be smoking only have ‘x’ amount of cigarettes or however they want to measure it in terms of, I don’t know, like carbon monoxide detects or anything like that be helpful, you have to be under a certain threshold by sort of that midway point. (Int 16, Q)

There currently seemed little encouragement for women that had managed to reduce their smoking with NRT. This was highlighted by interviewees as an area that would need to be improved:

[A] bit of validation that actually (motivates you). it’s a hard thing you’re trying to do. I think at the moment it’s very much focused on “You must quit and if you don’t, this is what you’re doing to the baby”. Rather than that, flip it so you get a bit more validation of actually ’This is a really difficult thing you’re doing, and you have actually cut down by quite a considerable amount’. (Int 18, R, NRT)

And that if women were able to receive support cutting down their smoking with NRT, it should be seen as an equally acceptable ‘treatment choice’, and they should receive equal support and encouragement:

…if somebody says “Look, I don’t think I’m going to be able to stop but I am more than happy and willing to try and cut down with the right support and resources and things” then I feel like that should be encouraged just as much as being encouraged to stop. (Int 14, Q)

When women were asked when the best time would be to offer NRT to reduce their smoking, most recommended that it should be offered early on in pregnancy to limit the harm to the fetus:

I think early, it’s best to start early in pregnancy so you can, sort of, give your baby the best chance, yeah. And to give yourself the time because if you start late in pregnancy, I find there is no point in starting. (Int 13, Q)

This study reports the exploration of the acceptability, in reducing the number of daily cigarettes smoked in pregnancy, rather than for stopping smoking completely and the potential role NRT may have in this process. The analysis identified an appreciation for a ‘cutting down’ approach to smoking harm reduction while highlighting associated difficulties. Half of interviewees reported using NRT to help them reduce their smoking in this current pregnancy, while the perceived stigma associated with purchasing or using NRT were seen as problematic. When presented with the suggestion that NRT could be used to help women reduce their smoking, issues associated with using NRT in this way were discussed.

A key strength of our study is that we are unaware of any others that have qualitatively explored the views of pregnant women on whether NRT should, or could, be used to help reduce the number of cigarettes smoked when complete cessation was not likely, and the kind of support needed for this approach. Online recruitment using Facebook adverts and social media posts allowed researchers to identify and interview women from across the UK who might be disengaged from SSS (ie, those who were unable or unwilling to stop smoking). 38

The main study limitation was the reliance on telephone, rather than face-to-face, interviews. While it is more difficult to develop rapport with interviewees over the phone, this approach is known to have advantages when discussing topics of a potentially sensitive nature. 39 Furthermore, the use of social media recruitment, while advantageous in terms of geographical reach, may have excluded representation from those who do not use social media regularly or use platforms other than Facebook. 40

For interviewees in this study reducing their smoking seemed to be an intuitively adopted behaviour and is congruent with the idea that particular health behaviours such as smoking, drinking alcohol and healthy eating may change as a result of pregnancy and without intervention from health professionals. 41 Interviewees in this study understood the opportunities afforded by adopting a cutting down approach, not only because abrupt stopping was seen as particularly difficult but also because smoking reduction was seen as a way of reducing the harms related to continuing to smoke at pre-pregnancy levels. While most pregnant smokers recognise the risks to their unborn child, 42 the findings of this study echo the sentiments expressed in other qualitative work, in that, while quitting smoking was judged to be the ideal, cutting down was seen as an important strategy in reducing the harm to the unborn fetus that should be considered. 28

This study identified different strategies employed by women to reduce their smoking such as increasing the time between cigarettes or only smoking at certain times of the day which have been shown to be effective means of smoking reduction 43 44 ; however, there seemed to be a lack of clear goal setting, which is an important aspect in behavioural changes. 45 Having access to professional support that could assist women in distinct, realistic, goal setting and clear feedback may increase successful reduction. 46 The lack of praise women received from healthcare professionals for any cigarette reduction was a concern raised by our Patient and Public Involvement and Engagement (PPIE) group when they reviewed interview transcripts and assisted in initial coding. They felt that in general these women were not receiving adequate support which could be a reflection of the current focus on cessation above harm reduction.

One difficulty with this approach was the stigma surrounding continued smoking. Women seemed concerned that other people would judge them negatively if they were seen buying or using NRT without understanding that they had worked hard to reduce the amount they smoked. Stigma around smoking during pregnancy has long been recognised and is considered to be less to do with the level of risk to the fetus and more of a moral judgement. 47 The stigmatisation of pregnant women who smoke may lead to women hiding their smoking and a hesitancy to engage with professional smoking cessation support. 48 49

We sought women who had experience of smoking in pregnancy but did not seek those who were trying to stop or to cut down smoking at all. Nevertheless, half of the women enrolled in this study were using NRT to cut down their smoking, and over half were not actively engaged with stop smoking services. This suggests that using NRT to cut down daily smoking may already be a widely adopted practice. It was unclear why the women were not using stop smoking services. A possible explanation could be that cessation services only work with women who are committed to trying to quit, and it has been shown that stop smoking practitioners (SSPs) hold particularly negative views towards using NRT for cutting down smoking in pregnancy and would only ever advise NRT use and concurrent smoking for anything but the briefest of smoking lapses during a quit attempt. 50 Women who felt that they were unable or not willing to commit to a stop smoking attempt would find their access to free NRT products from stop smoking services restricted and may have resulted in women buying their own NRT or nicotine products themselves. Buying their own NRT without the associated support and education provided by healthcare professionals may lead to women being less informed around what to expect in terms of possible side effects, such as nausea, which may impact better tolerance and adherence to different NRT products. 51 The cost of buying NRT to support cutting down their smoking alongside the potential for embarrassment reported by some women by buying NRT in public was seen as a barrier to this approach. Only organisational changes that would allow the prescription of NRT to support smoking reduction in pregnancy, as it is in the general population of smokers who are unable to quit, would reduce the burden on an already socially and economically disadvantaged population. 52

Stigma was also cited as a barrier by the interviewees in our study when purchasing or using NRT to help them reduce their smoking. This finding emphasises other work that pregnant smokers who feel stigmatised may be particularly attracted to products that can be used discreetly 53 such as patches that can easily be hidden under clothing or nicotine gum that looks like an ordinary product. It was reported that purchasing nicotine products over the counter offered more opportunities for them to feel judged by others. Unless women were able to procure NRT to cut down their smoking on prescription or directly from a stop smoking service, perhaps using an online delivery service may help alleviate these concerns; however, this may add an additional cost to these products.

Some women reported being concerned about getting too much nicotine from using NRT and smoking at the same time; however, a recent review and meta-analysis should provide reassurance that using NRT alongside smoking in the context of smoking reduction is unlikely to result in dangerous levels of nicotine exposure. 54 This may be because most smokers are able to self-titrate their nicotine intake, through smoking and NRT, to maintain plasma nicotine levels without adverse physical or subjective effects. 55

Similar qualitative work is needed with health professionals to assess their views on this topic. Given that stop smoking practitioners have previously reported not feeling comfortable promoting a harm reduction approach to pregnant women, it was not compatible with their aims of promoting a smoke-free pregnancy and healthy baby 50 and that stopping smoking completely is the only way of ensuring that the unborn baby is not at risk from smoking harms. 13 Health professionals might need to consider how they balance communicating the risks of reduced but continued exposure to tobacco smoke and the use of NRT to promote smoking reduction as a possible treatment option without undermining cessation as the aim of a stop smoking service. This is particularly important when pregnant women are often confused about the need for the safety of NRT and clear, consistent messages from healthcare professionals. 51

There was a distinct preference for offering NRT to support smoking reduction as early as possible in pregnancy in an attempt to minimise the effects of smoking. There is a suggestion that women may be more motivated to quit earlier on in pregnancy, 14 so it seems reasonable to suggest that women may also be more motivated to reduce their smoking during this time frame. As midwives play a particularly pivotal role in providing SSS to pregnant women, 56 any steps to reduce stigma around reduced smoking, procurement of NRT and the provision of empathetic support would require meaningful co-development involving both midwives and pregnant women. 47

Using NRT to help women who are unable to stop smoking, reduce their smoking, is potentially acceptable to pregnant women. This study found women were already using NRT alongside ad hoc strategies to reduce their smoking. There are barriers to adopting this approach, associated with access to NRT and current attitudes towards smoking during pregnancy. Further research involving a structured smoking reduction plan, alongside the concurrent use of NRT and proper follow-up care, is needed to evaluate this approach.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by Faculty of Medicine and Health Science Research Ethics Committee, University of Nottingham (Reference number FMHS 442-0122). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

The authors would like to thank the interviewees for their help with this study. We would also like to acknowledge the work of our Patient and Public Involvement and Engagement (PPIE) group who helped with study design, recruitment materials, interview schedules and analysis.

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X @FelixNaughton

Contributors TC contributed to the conception of the study, contributed to the design of the study, revised the manuscript and approved the final version. SO contributed to the conception of the study, contributed to the design of the study, conducted interviews, contributed to data analysis, revised the manuscript and approved the final version. FN contributed to the conception of the study, contributed to the design of the study, revised the manuscript and approved the final version. RT contributed to the design of the study, conducted interviews, contributed to data analysis, wrote the initial draft of the manuscript, revised the manuscript and approved the final version. LP contributed to the design of the study, conducted interviews, contributed to data analysis, revised the manuscript and approved the final version. SO is the guarantor.

Funding This study/project is funded by the National Institute for Health and Care Research (NIHR) School for Primary Care Research (project reference 524). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Prof Coleman is an NIHR Senior Investigator.

Competing interests None declared.

Patient and public involvement Patients and/or the public were involved in the design, conduct, reporting or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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STRENGTHS OF ADOPTING A QUALITATIVE INTERVIEW DESIGN. Qualitative research, including that which adopts an interview design, plays an important role in knowledge generation as it allows researchers to question the underlying assumptions of existing theorising, and engage in inductive theory generation (Sandberg & Alvesson, 2011).
The SAGE Handbook of Qualitative Research Design
Part I: Concepts of Designing Designs in Qualitative Research. Chapter 2: Reflexive Design in Qualitative Research. Chapter 3: Interactive Approaches to Qualitative Research Design. Chapter 4: Emergent Design. Chapter 5: Choosing a Research Design for Qualitative Research - A Ferris Wheel of Approaches.
What is Qualitative Research Design? Definition, Types, Methods and
Qualitative research design typically involves gathering data through methods such as interviews, observations, focus groups, and analysis of documents or artifacts. These methods allow researchers to collect detailed, descriptive information about participants' perspectives, experiences, and contexts. Key characteristics of qualitative ...
PDF Asking the Right Question: Qualitative Research Design and Analysis
Limitations of Qualitative Research. Lengthy and complicated designs, which do not draw large samples. Validity of reliability of subjective data. Difficult to replicate study because of central role of the researcher and context. Data analysis and interpretation is time consuming. Subjective - open to misinterpretation.
Characteristics of Qualitative Research
Qualitative research is a method of inquiry used in various disciplines, including social sciences, education, and health, to explore and understand human behavior, experiences, and social phenomena. It focuses on collecting non-numerical data, such as words, images, or objects, to gain in-depth insights into people's thoughts, feelings, motivations, and perspectives.
Unpacking complexity in addressing the contribution of trauma to women
Design and setting Qualitative study in the general practice setting of England, with reflections from representatives of a group with lived experience of trauma. ... Method This was a secondary thematic analysis of 46 qualitative interviews conducted online/by telephone to explore primary care practitioners' experiences of supporting women ...
"I am there just to get on with it": a qualitative study on the labour
Our interview study formed the first phase of a multi-method qualitative inquiry into the working practices of NIHR-funded PPI leads. While PPI lead posts are in evidence in most NIHR-funded research, we decided to focus on NIHR infrastructure funding specifically: these are 5-year grants absorbing a major tranche of NIHR funds (over £600 million annually in 2024).
Understanding how and why travel mode changes: analysis of longitudinal
Study design. This was a prospective longitudinal qualitative cohort study nested within a randomised controlled trial (RCT) to assess the feasibility of financial incentives for promoting alternatives to the car, implemented in Northstowe, a new town in Cambridgeshire, United Kingdom [].Eligible individuals for the RCT were those aged over 16 years and living in a household in Northstowe that ...
Identifying motivational interviewing techniques in Quitline smoking
Motivational interviewing (MI) is a common approach for smoking cessation counselling, yet little is known about the use of MI techniques in practice. This qualitative content analysis applied a published classification of content and relational MI techniques to a sample of 30 Quitline transcripts (January-March 2019) from Queensland, Australia.
Lecturers' perceptions of the influence of AI on a blended learning
The data collection process involved semi-structured focus group interviews that allowed for in-depth discussions to be conducted. ... Their qualitative research undertaking employed a singular case study design focusing on 15 lecturers from the College of Education at a selected academic institution, to arrive at an in-depth understanding of ...
Using nicotine replacement therapy for smoking reduction in pregnancy
Objectives To explore the acceptability and perceived motivations and barriers of using nicotine replacement therapy (NRT) to reduce the number of daily cigarettes smoked in pregnancy, rather than for stopping completely. Design Telephone, semi-structured interviews, audio-recorded and transcribed verbatim. Transcripts were analysed using an inductive thematic analysis. Participants Eighteen ...

Qualitative research method-interviewing and observation

Interviewing

Observation

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Types of Interviews in Research | Guide & Examples

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Tegan George

Chapter 11. Interviewing

Types of Interviews

Creating an Interview Guide

Type of Questions

Background/Demographic Questions

Temporality

Question Sequence

Probes and Flexibility

The Final Question

Conducting the Interview

How Many Interviews?

Your Final Product: The Ideal Interview Transcript

Final Tips from an Interview Master

Sample: Demographic Questionnaire

Example: Interview Guide

Further Readings

Qualitative Research Design: Start

Events Relating to the Qualitative Research and Community Engagement Workshops @ CMU Libraries

Workshop Organizer

Qualitative Research Methods

Library Support for Qualitative Research

General Handbooks and Overviews

Qualitative Interview Questions: Guidance for Novice Researchers

Qualitative study design: Interviews

Limitations

Example questions

Example studies

Appendix: Qualitative Interview Design

Qualitative Interview Design: A Practical Guide for Novice Investigators

Categories of Qualitative Interview Design

Informal Conversational Interview

General Interview Guide Approach

Standardized Open-Ended Interviews

Suggestions for Conducting Qualitative Interviews

Preparation for the Interview

Selecting Participants

Pilot Testing

Constructing Effective Research Questions

Examples of Useful and Not-So Useful Research Questions

Follow-Up Questions

Implementation of Interviews

Interpreting Data

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Qualitative Research 101: Interviewing

Overview: 5 Interviewing Mistakes

1. Not having a clear interview strategy

2. Not having good interview technique

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3. Not securing a suitable location and quality equipment

4. Not having a basic risk management plan

5. Not keeping your golden thread front of mind

Recap: Qualitative Interview Mistakes

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Interviews and focus groups in qualitative research: an update for the digital age

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Overview of qualitative research

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Conducting an interview

Box 1: Example of a topic guide

Focus groups

Focus group considerations

Focus group management

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