what is quantitative research design in nursing

Henry Ford Hospital Nursing Research Toolkit

• Step 1: Getting Started
• Step 2: Project Assistance
• Step 3: Research Question
• Step 4: Review Literature
• Step 5: Use Theory
• Step 6: Apply with IRB
• Quantitative Research Steps
• Additional Reading
• About This Guide

Quantitative Method - Identifying and Refining the Research Question

• Identify the problem and decide on a research question
• Initiate a literature search and review the literature
• Identify a theoretical framework to guide the study
• Formulate a hypothesis (a predicted statement of researcher's expectations or predictions about relationships among variables). Non-intervention studies don't have a hypothesis  because introducing a testable intervention/treatment is not part of the research)

Design and Planning

• Where will data be collected?
• How often will data be collected?
• What outcomes will be measured?
• What strategies will be used to minimize bias?
• Decide exactly what the treatment or intervention will involve
• Who will administer it?
• How frequently?
• Over what time frame?
• Identify what the alternative (control group) condition is
• Identify the population to be studied
• Design the sampling plan. The sample is a representative subset of the population: how will the sample be selected, recruited, and how many participants will there be?
• Specify methods to measure research variables: Will data be collected using self-reports, observations or biophysiological measures?
• It's always, always better to use a data collection instrument that's reliable and valid instead of creating your own instrument. Valid, reliable tools are precise instruments that have been tested for reliability (results are consistent) and validity (concept is accurately measured).
• Safeguarding subjects: protecting the rights of participants begins with submitting an Internal Review Board (IRB) application to ensure that human rights are protected.
• Finalizing the research plan. Ask other researchers to review the study protocol. It's also helpful to pretest measuring instruments with a small pilot group to identify problems that may occur. 

Data Collection / Preparing for Analysis

• Who will collect the data?
• When and where will data be collected?
• How will the study be described to participants?
• How will the information be recorded? 
• Preparing the data for analysis: who will code/prepare and enter data for analysis?
• Analyzing the data

Consult with a statistician: If you have decided on a research question and are planning a research study, the next step is to meet with a Biostatistician.  A Biostatistician can assist with turning your research question into a statistical question that is focused on outcomes that can be tested and measured. They will also determine the appropriate sample size for your study. Check with your unit manager to make sure the Biostats meeting can be billed to your unit cost center. 

Henry Ford Health Public Health Sciences (PHS) can assist with this step. 

Interpretation of Results

• Cathy Draus:  [email protected]  at the Center for Nursing Research and Evidence-Based Practice (H101 Main campus) can assist with interpretation of statistical results. 

Dissemination

• Prepare manuscript for journal submission or apply to present a poster or podium presentation at a conference
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• Plan for the use of the evidence in the practice setting and make recommendations in your article or presentation as to how the evidence can be translated into practice. It's also important to indicate the implications your study has for nursing practice
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Understanding quantitative research: part 1

Affiliation.

• 1 Research Department of Mental Health Sciences, University College London, London. [email protected]
• PMID: 23346707
• DOI: 10.7748/ns2012.12.27.15.52.c9485

This article, which is the first in a two-part series, provides an introduction to understanding quantitative research, basic statistics and terminology used in research articles. Critical appraisal of research articles is essential to ensure that nurses remain up to date with evidence-based practice to provide consistent and high-quality nursing care. This article focuses on developing critical appraisal skills and understanding the use and implications of different quantitative approaches to research. Part two of this article will focus on explaining common statistical terms and the presentation of statistical data in quantitative research.

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• Quantitative research. Norkett L. Norkett L. Nurs Stand. 2013 Jun 26-Jul 2;27(43):59. doi: 10.7748/ns2013.06.27.43.59.s52. Nurs Stand. 2013. PMID: 23987722 No abstract available.

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• Understanding quantitative research: part 2. Hoare Z, Hoe J. Hoare Z, et al. Nurs Stand. 2013 Jan 2-8;27(18):48-55; quiz 57. doi: 10.7748/ns2013.01.27.18.48.c9488. Nurs Stand. 2013. PMID: 23431654
• Building the capacity for evidence-based clinical nursing leadership: the role of executive co-coaching and group clinical supervision for quality patient services. Alleyne J, Jumaa MO. Alleyne J, et al. J Nurs Manag. 2007 Mar;15(2):230-43. doi: 10.1111/j.1365-2834.2007.00750.x. J Nurs Manag. 2007. PMID: 17352707
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• Methodological considerations of undertaking research with clinical mentors in the UK: a critical review of the literature. Jinks AM. Jinks AM. Nurse Educ Today. 2007 Oct;27(7):667-76. doi: 10.1016/j.nedt.2006.09.014. Epub 2006 Dec 13. Nurse Educ Today. 2007. PMID: 17169465 Review.
• Evidence-based medicine: specific skills necessary for developing expertise in critical appraisal. Morris MJ, Fewell AE, Oleszewski RT. Morris MJ, et al. South Med J. 2012 Mar;105(3):114-9. doi: 10.1097/SMJ.0b013e31824b197c. South Med J. 2012. PMID: 22392205 Review.
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• Quantitative vs. Qualitative Research

You can find evidence for clinical decision making in quantitative and qualitative research studies .  Quantitative research  refers to any research based on something that can be accurately and precisely measured and will include studies that have numerical data . Quantitative data are expressed numerically and analyzed statistically. The data are collected from experiments and tests, metrics, databases, and surveys. In healthcare research they  often  include studies of intervention effectiveness, satisfaction with care, the incidence, prevalence, and etiology of diseases, and the properties of measurement tools (Kolaski, 2023).

Findings in qualitative studies are not based on measurable statistics. Qualitative data are descriptive rather than numerical. Qualitative research derives data from observation, interviews, verbal interactions, or textual analyses and focuses on the meanings and interpretations of the participants. Qualitative research studies in healthcare investigate the impact of illnesses and interventions. The research explores experiences, attitudes, beliefs, and perspectives of patients, caregivers, and clinicians (Kolaski, 2023). The analysis of qualitative research is interpretative, subjective, and impressionistic.  

Kolaski, K., Logan, L. R., & Ioannidis, J. P. A. (2023). Guidance to best tools and practices for systematic reviews. Systematic Reviews , 12 (1), 96. https://doi.org/10.1186/s13643-023-02255-9

For more information on qualitative research:

Curtis, A. & Keeler, C. (2022). An introduction to qualitative methods for the nurse researcher.  American Journal of Nursing, 122  (8), 52-56. https://doi: 10.1097/01.NAJ.0000854992.17329.51.

Noyes, J., Booth, A., Cargo, M., Flemming, K., Harden, A., Harris, J., Garside, R., Hannes, K., Pantoja, T., & Thomas, J. (2023). Chapter 21: Qualitative evidence.  In Higgins, J.P.T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M.J., Welch, V.A. (Eds.).  Cochrane handbook for systematic reviews of interventions version 6.4.  Cochrane.  www.training.cochrane.org/handbook

Video:  UniversityNow: Quantitative vs. Qualitative Research

Appraising Quantitative and Qualitative Research

The articles below provide a step-by-step appraisal on how to critique quantitative and qualitative research articles:

Ryan, F., Coughlan, M. & Cronin, P. (2007). Step-by-step guide to critiquing research. Part 1: quantitative research.  British Journal of Nursing, 16 (11), 658-663 .

Ryan, F., Coughlan, M. & Cronin, P. (2007). Step-by-step guide to critiquing research. Part 2: qualitative research.  British Journal of Nursing, 16 (2), 738-744 .

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Nursing - Quantitative & Qualitative Articles: Quantitative

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"How does numerical value teach us about a population's problems?" .cls-1{fill:#fff;stroke:#79a13f;stroke-miterlimit:10;stroke-width:5px;}.cls-2{fill:#79a13f;} Numeric data collected from studies can indicate why a health problem exists, such as correlating data between environmental or genetic factors to a condition. This data can help us find appropriate interventions based on a specific cause.

What is quantitative research, search for quantitative, identify articles, check quality.

• Quantitative Research from the Dictionary of Nursing Theory and Research Although in isolation the term is not explicitly used very often, quantitative research is concerned with precise measurement, replicability, prediction, and control. It includes techniques and procedures such as standardized tests, random sampling and/or assignment, tests of statistical significance, and causal modeling. It may be preceded by descriptive pilot studies that are preliminary steps to a subsequent experimental or correlational study.
• Quantitative research design by Jacqueline Bloomfield & Murray J. Fisher "The aim of this article is to explain what is meant by research design and to discuss the four different types of research design that are commonly used in quantitative research" (Bloomfield & Fisher, 2019).
• Focusing on the fundamentals: A simplistic differentiation between qualitative and quantitative research by Shannon Rutberg & Christina D. Bouikidis This article describes qualitative, quantitative, and mixed methods research. Various classifications of each research design, including specific categories within each research method, are explored. Attributes and differentiating characteristics, such as formulating research questions and identifying a research problem, are examined, and various research method designs and reasons to select one method over another for a research project are discussed (2018, Rutberg & Bouikidis).
• What do the stats mean? Improving reporting of quantitative nursing research by Denis C. Visentin & Glenn E. Hunt An editorial is presented on the use of statistical methods to improve the report in mental health (MH) nursing research quantitatively. It expresses the view that methods which are easily understandable should be used to provide information and deliver measures. An overview of ways to improve the focus on effect size and the significance of communication between statistical analyst and clinical researcher is also presented.
• Statistics for the non-statistician by Trisha Greenhalgh It is possible to be seriously misled by taking the statistical competence (and/or the intellectual honesty) of authors for granted. Statistics can be an intimidating science, and understanding its finer points often calls for expert help. But I hope that this chapter has shown [ shows ] you that the statistics used in most medical research papers can be evaluated - at least up to a point - by the non-expert using a simple checklist such as that in Appendix 1 . In addition, you might like to check the paper you are reading (or writing) against common errors given in Box 5.1. If you're hungry for more on statistics and their misinterpretation, try this classic paper 'Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations' by Greenland and colleagues (Greenhalgh, 2020, Chapter 5, "Summary").

Bloomfield, J., & Fisher, M. J. (2019). Quantitative research design. Journal of the Australasian Rehabilitation Nurses’ Association (JARNA) , 22 (2), 27–30. https://doi.org/10.33235/jarna.22.2.27-30

Greenhalgh, T. (2019). Statistics for the non-statistician. In How to read a Paper : The basics of evidence-based medicine and healthcare . (Sixth ed., pp. 62-78). Wiley Blackwell.

Quantitative research. (2010). In A. B. Powers, Dictionary of nursing theory and research (4th ed.). Springer Publishing Company. Credo Reference: https://ezproxy.simmons.edu/login?url=https://search.credoreference.com/content/entry/spnurthres/quantitative_research/0.embed?institutionId=5600

Rutberg, S., & Bouikidis, C. D. (2018). Focusing on the Fundamentals: A Simplistic Differentiation Between Qualitative and Quantitative Research. Nephrology Nursing Journal, 45(2), 209–213.

Visentin, D. C., & Hunt, G. E. (2017). What do the stats mean? Improving reporting of quantitative nursing research. International Journal of Mental Health Nursing , 26 (4), 311–313. https://doi.org/10.1111/inm.12352

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Qualitative and Quantitative Studies

To find qualitative and quantitative studies, try adding one of these words/phrases to your search terms. The word "qualitative" or "quantitative" will sometimes appear in the title, abstract, or subject terms, but not always. Look at the methods section of the article to determine what type of study design was used.

From A Dictionary of Nursing

• Critical Appraisal Tools from the Joanna Briggs Institute ▸ JBI’s critical appraisal tools assist in assessing the trustworthiness, relevance and results of published papers. ▸ Checklist for Systematic Reviews
• Trust It or Trash It ▸ This is a tool to help you think critically about the quality of health information (including websites, handouts, booklets, etc.). ▸ Created by the Genetic Alliance
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Understanding Qualitative and Quantitative Research in Nursing: Quantiative Research Information

• Qualitative Research Information
• Quantiative Research Information

About Quantiative Research

Quantitative research consists of information expressed in numbers, variables, and percentages. It seeks to confirm that all problems, dilemmas, or hypotheses have clear, concrete, and objective solutions that can be expressed in a numerical format. This type of research focuses on specific, narrow questions in a double-blind study, usually with a large random group and variables. The data collected can be analyzed with the help of statistics in an unbiased manner with the objective to explain, describe, or predict.

Helpful Website Links on Quantitative Research in Nursing

• A nurses’ guide to Quantitative Research
• Understanding Quantiative Research (Article)
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• Quantitative vs qualitative research methods... 'What direction for mental
• Implications for quantitative and qualitative reviews.

Quantitative research consist of following elements

• a hypothesis
•  a random or specific study group with a common similarity
•  variables (any element or behavior that can affect or change the outcomes of a study, such as a medication, treatment, or nursing intervention)
•  outcomes.

Quantitative research is usually conducted in a controlled environment, such as a lab or healthcare unit. It can be categorized as follows.

3 Types of Quantitative Research

Correlational research is the methodical investigation of relationships or interactions between two or more variables without determining the cause-and-effect relationship the variables may have on each other. An example is studying two chemotherapy medications for compatibility without studying how the medications can have adverse interactions with food or other common medications.

Quasi-experimental research explores a cause-and-effect relationship among variables. It also evaluates the underlying cause of a problem and studies the effects of variables (such as a nursing intervention) to evaluate their effect on the problem.

Descriptive research offers an accurate representation of the characteristics of a particular individual, situation, or group. Descriptive research is a way of discovering new meaning, describing numerically something that currently exists, determining the frequency with which something occurs, and categorizing information.

To find articles in ERIC   click on the  advanced search  tab. Use the phrase "quantitative research"  as one of your search terms.

Related terms that may be searched:  

Bayesian statistics

Correlation

Effect size

Error of measurement

Factor analysis

Goodness of fit

Hypothesis testing

Item analysis

Least squares

Monte Carlo Methods

Maximum likelihood

Multivariate analysis

Regression (statistics)

Robustness (statistics)

Statistical analysis

Statistical inference

Statistical significance

Markov processes

Also the following may be use, but not restricted to Subject Terms

Experimental design, design of experiments, statistical design, or research design

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Research Methods in Nursing

Qualitative, quantitative, observational, correlational, quasi-experimental, experimental, mixed-methods, triangulation, ask a librarian.

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QUALitative research "is best suited for research aimed at rich description or in-depth understanding of a phenomenon, rather than determining causality; it is particularly useful in understanding the relevance of contextual features in the expression of the phenomenon. Qualitative approaches are most often chosen when little is known about a topic or when new perspectives are needed; other functions of qualitative approaches include generating hypotheses, refining theory, providing illustrative examples, creating taxonomies, and generating items for instrument development.

Relying primarily on inductive rather than deductive processes, qualitative studies generally share several “ground-up” features that differentiate them from “top-down” quantitative research. These features are driven by two central tenets: an orientation to cases rather than variables, as well as a preference for emergent rather than fixed designs."

More Information from Encyclopedia of Nursing Research

Four types of qualitative research design often applied to nursing research are:

• Phenomenology - the study of human life experiences and how they appear in human consciousness
• Grounded Theory - seeks to explain variations in social interactional and social structural problems and processes
• Ethnography - As a research process, ethnography is a comparative method for investigating patterns of human behavior and cognition through observations and interactions in natural settings
• Narrative Inquiry - the analysis of meaning in context through interpretation of persons' life experiences

For more details , look up these research designs in:

Encyclopedia of Nursing Research

Dictionary of Nursing Theory and Research

QUANtitative research "consists of the collection, tabulation, summarization, and analysis of numerical data for the purpose of answering research questions or hypotheses. The term quantitative research is of recent origin and is distinguished from qualitative research in design, process, and the use of quantification techniques to measure and analyze the data. The vast majority of all nursing studies can be classified as quantitative.

Quantitative research uses statistical methodology at every stage in the research process. At the inception of a research project, when the research questions are formulated, thought must be given to how the research variables are to be quantified, defined, measured, and analyzed. Study subjects are often selected for a research project through the statistical method of random sampling, which promotes an unbiased representation of the target population among the sample from whom generalizations will be made. Statistical methods are used to summarize study data, to determine sampling error, and in studies in which hypotheses are tested, to analyze whether results obtained exceed those that could be attributed to sampling error (chance) alone."

Quantitative Research Design can be Non-Experimental (Descriptive or Correlational) or Experimental (including Quasi-Experimental).

" Observational designs are nonexperimental, quantitative designs. In contrast to experimental designs in which the investigator manipulates the independent variable and observes its effect, the investigator conducting observational research observes both the independent and dependent variables. In observational studies, variation in the independent variable may be due to genetic endowment, self-selection, or occupational or environmental exposures."

" Correlational research examines the relationships between variables, but unlike experimental or quasi-experimental studies, correlational studies lack active manipulation of the independent variable(s). Therefore, postulation of relationships among study variables in causal terms is risky. Discussion of associations in correlational studies, however, sometimes gives an indication of how likely it is that a cause-and-effect relationship might exist."

More Information from Dictionary of Nursing Theory and Research

" Quasi-experimental research is similar to experimental research in that there is manipulation of an independent variable. It differs from experimental research because there is no control group, no random selection, no random assignment, and/or no active manipulation. Quasi-experimental research is a useful way to test causality in settings when it is impossible or unethical to randomly assign subjects to treatment and control groups or to withhold treatment from some subjects."

Experimental research "involves manipulation of the principal independent variable, i.e., the actual administration of treatments or interventions that comprise the categories of the independent variable. An investigation is made of the effect of the independent variable on the dependent variable.

A true experiment is characterized by random assignment of individual subjects to the treatment conditions and a high degree of control over unwanted influence of extraneous variables and other factors that could bias the results of the study."

"True experiments have the potential to provide strong evidence about the hypothesized causal relationship between independent and dependent variables. Experiments are characterized by manipulation, control, and randomization. The quality of experiments depends on the validity of their design."

" Mixed methods research is a term associated with research that uses a combination of methods that are usually identified with qualitative research and methods that are usually identified with quantitative research. It should not be confused with the terms mixed models or mixed effects that are used in other contexts such as the analysis of variance.

It is important to understand that the use of a mixed methods approach does not make research better or more valid than the use of either a qualitative or a quantitative approach."

"In nursing research, triangulation refers to the use of multiple sources to validate findings by the nursing researcher. It involves the combination of both quantitative and qualitative research methods within a single study."

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Clarifying Quantitative Research Designs

Chapter 8 Clarifying Quantitative Research Designs Chapter Overview Identifying Designs Used in Nursing Studies Descriptive Designs Typical Descriptive Design Comparative Descriptive Design Correlational Designs Descriptive Correlational Design Predictive Correlational Design Model Testing Design Understanding Concepts Important to Causality in Designs Multicausality Probability Bias Control Manipulation Examining the Validity of Studies Statistical Conclusion Validity Internal Validity Construct Validity External Validity Elements of Designs Examining Causality Examining Interventions in Nursing Studies Experimental and Control or Comparison Groups Quasi-Experimental Designs Pretest and Post-test Designs with Comparison Group Experimental Designs Classic Experimental Pretest and Post-test Designs with Experimental and Control Groups Post-test–Only with Control Group Design Randomized Controlled Trials Introduction to Mixed-Methods Approaches Key Concepts References Learning Outcomes After completing this chapter, you should be able to: 1.  Identify the nonexperimental designs (descriptive and correlational) and experimental designs (quasi-experimental and experimental) commonly used in nursing studies. 2.  Critically appraise descriptive and correlational designs in published studies. 3.  Describe the concepts important to examining causality—multicausality, probability, bias, control, and manipulation. 4.  Examine study designs for strengths and threats to statistical conclusion, and to internal, construct, and external validity. 5.  Describe the elements of designs that examine causality. 6.  Critically appraise the interventions implemented in studies. 7.  Critically appraise the quasi-experimental and experimental designs in published studies. 8.  Examine the quality of randomized controlled trials (RCTs) conducted in nursing. 9.  Discuss the implementation of mixed-methods approaches in nursing studies. Key Terms Bias, p. 223 Blinding, p. 241 Causality, p. 222 Comparative descriptive design, p. 216 Construct validity, p. 227 Control, p. 224 Control or comparison group, p. 230 Correlational design, p. 217 Cross-sectional design, p. 212 Descriptive correlational design, p. 218 Descriptive design, p. 212 Design validity, p. 211 Experimental designs, p. 237 Experimental or treatment group, p. 230 Experimenter expectancy, p. 228 External validity, p. 228 Internal validity, p. 226 Intervention, p. 230 Intervention fidelity, p. 230 Longitudinal design, p. 212 Low statistical power, p. 226 Manipulation, p. 224 Mixed-methods approaches, p. 243 Model testing design, p. 221 Multicausality, p. 223 Nonexperimental designs, p. 212 Predictive correlational design, p. 220 Probability, p. 223 Quasi-experimental design, p. 232 Randomized controlled trial (RCT), p. 241 Research design, p. 211 Statistical conclusion validity, p. 224 Study validity, p. 224 Threats to validity, p. 224 Triangulation, p. 244 Typical descriptive design, p. 214 A research design is a blueprint for conducting a study. Over the years, several quantitative designs have been developed for conducting descriptive, correlational, quasi-experimental, and experimental studies. Descriptive and correlational designs are focused on describing and examining relationships of variables in natural settings. Quasi-experimental and experimental designs were developed to examine causality, or the cause and effect relationships between interventions and outcomes. The designs focused on causality were developed to maximize control over factors that could interfere with or threaten the validity of the study design. The strengths of the design validity increase the probability that the study findings are an accurate reflection of reality. Well-designed studies, especially those focused on testing the effects of nursing interventions, are essential for generating sound research evidence for practice ( Brown, 2014 ; Craig & Smyth, 2012 ). Being able to identify the study design and evaluate design flaws that might threaten the validity of the findings is an important part of critically appraising studies. Therefore this chapter introduces you to the different types of quantitative study designs and provides an algorithm for determining whether a study design is descriptive, correlational, quasi-experimental, or experimental. Algorithms are also provided so that you can identify specific types of designs in published studies. A background is provided for understanding causality in research by defining the concepts of multicausality, probability, bias, control, and manipulation. The different types of validity—statistical conclusion validity, internal validity, construct validity, and external validity—are described. Guidelines are provided for critically appraising descriptive, correlational, quasi-experimental, and experimental designs in published studies. In addition, a flow diagram is provided to examine the quality of randomized controlled trials conducted in nursing. The chapter concludes with an introduction to mixed-method approaches, which include elements of quantitative designs and qualitative procedures in a study. Identifying Designs Used in Nursing Studies A variety of study designs are used in nursing research; the four most commonly used types are descriptive, correlational, quasi-experimental, and experimental. These designs are categorized in different ways in textbooks ( Fawcett & Garity, 2009 ; Hoe & Hoare, 2012 ; Kerlinger & Lee, 2000 ). Sometimes, descriptive and correlational designs are referred to as nonexperimental designs because the focus is on examining variables as they naturally occur in environments and not on the implementation of a treatment by the researcher. Some of these nonexperimental designs include a time element. Designs with a cross-sectional element involve data collection at one point in time. Cross-sectional design involves examining a group of subjects simultaneously in various stages of development, levels of education, severity of illness, or stages of recovery to describe changes in a phenomenon across stages. The assumption is that the stages are part of a process that will progress over time. Selecting subjects at various points in the process provides important information about the totality of the process, even though the same subjects are not monitored throughout the entire process ( Grove, Burns, & Gray, 2013 ). Longitudinal design involves collecting data from the same subjects at different points in time and might also be referred to as repeated measures. Repeated measures might be included in descriptive, correlational, quasi-experimental, or experimental study designs. Quasi-experimental and experimental studies are designed to examine causality or the cause and effect relationship between a researcher-implemented treatment and selected study outcome. The designs for these studies are sometime referred to as experimental because the focus is on examining the differences in dependent variables thought to be caused by independent variables or treatments. For example, the researcher-implemented treatment might be a home monitoring program for patients initially diagnosed with hypertension, and the dependent or outcome variable could be blood pressure measured at 1 week, 1 month, and 6 months. This chapter introduces you to selected experimental designs and provides examples of these designs from published nursing studies. Details on other study designs can be found in a variety of methodology sources ( Campbell & Stanley, 1963 ; Creswell, 2014 ; Grove et al., 2013 ; Kerlinger & Lee, 2000 ; Shadish, Cook, & Campbell, 2002 ). The algorithm shown in Figure 8-1 may be used to determine the type of design (descriptive, correlational, quasi-experimental, and experimental) used in a published study. This algorithm includes a series of yes or no responses to specific questions about the design. The algorithm starts with the question, “Is there a treatment?” The answer leads to the next question, with the four types of designs being identified in the algorithm. Sometimes, researchers combine elements of different designs to accomplish their study purpose. For example, researchers might conduct a cross-sectional, descriptive, correlational study to examine the relationship of body mass index (BMI) to blood lipid levels in early adolescence (ages 13 to 16 years) and late adolescence (ages 17 to 19 years). It is important that researchers clearly identify the specific design they are using in their research report. Fig 8-1 Algorithm for determining the type of study design. Descriptive Designs Descriptive studies are designed to gain more information about characteristics in a particular field of study. The purpose of these studies is to provide a picture of a situation as it naturally happens. A descriptive design may be used to develop theories, identify problems with current practice, make judgments about practice, or identify trends of illnesses, illness prevention, and health promotion in selected groups. No manipulation of variables is involved in a descriptive design. Protection against bias in a descriptive design is achieved through (1) conceptual and operational definitions of variables, (2) sample selection and size, (3) valid and reliable instruments, and (4) data collection procedures that might partially control the environment. Descriptive studies differ in level of complexity. Some contain only two variables; others may include multiple variables that are studied over time. You can use the algorithm shown in Figure 8-2 to determine the type of descriptive design used in a published study. Typical descriptive and comparative descriptive designs are discussed in this chapter. Grove and colleagues (2013) have provided details about additional descriptive designs. Fig 8-2 Algorithm for determining the type of descriptive design. Typical Descriptive Design A typical descriptive design is used to examine variables in a single sample ( Figure 8-3 ). This descriptive design includes identifying the variables within a phenomenon of interest, measuring these variables, and describing them. The description of the variables leads to an interpretation of the theoretical meaning of the findings and the development of possible relationships or hypotheses that might guide future correlational or quasi-experimental studies. Fig 8-3 Typical descriptive design. Critical Appraisal Guidelines Descriptive and Correlational Designs When critically appraising the designs of descriptive and correlational studies, you need to address the following questions: 1.  Is the study design descriptive or correlational? Review the algorithm in Figure 8-1 to determine the type of study design. 2.  If the study design is descriptive, use the algorithm in Figure 8-2 to identify the specific type of descriptive design implemented in the study. 3.  If the study design is correlational, use the algorithm in Figure 8-5 to identify the specific type of correlational design implemented in the study. 4.  Does the study design address the study purpose and/or objectives or questions? 5.  Was the sample appropriate for the study? 6.  Were the study variables measured with quality measurement methods? Research Example Typical Descriptive Design Research Study Excerpt Maloni, Przeworski, and Damato (2013) studied women with postpartum depression (PPD) after pregnancy complications for the purpose of describing their barriers to treatment for PPD, use of online resources for assistance with PPD, and preference for Internet treatment for PPD. This study included a typical descriptive design; key aspects of this study’s design are presented in the following excerpt. “Methods An exploratory descriptive survey design was used to obtain a convenience sample of women who self-report feelings of PPD across the past week [sample size n  = 53]. Inclusion criteria were women between 2 weeks and 6 months postpartum who had been hospitalized for pregnancy complications. Women were excluded if they had a score of < 6 on the Edinburgh Postnatal Depression Scale (EPDS)…. EPDS is a widely used screening instrument to detect postpartum depression…. In addition, a series of 26 descriptive questions assessed women’s barriers to PPD treatment, whether they sought information about depression after birth from any sources and their information seeking about PPD from the Internet, how often they sought the information, and whether the information was helpful. Questions were developed from review of the literature.… Content validity was established by a panel of four experts.… The survey was posted using a university-protected website using standardized software for surveys.” ( Maloni et al., 2013, pp. 91-92 ) Critical Appraisal Maloni and associates (2013) clearly identified their study design as descriptive and indicated that the data were collected using an online survey. This type of design was appropriate to address the study purpose. The sample section was strengthened by using the EPDS to identify women with PPD and using the sample criteria to ensure that the women had been hospitalized for pregnancy complications. However, the sample size of 53 was small for a descriptive study. The 26-item questionnaire had content validity and was consistently implemented online using standard survey software. This typical descriptive design was implemented in a way to provide quality study findings. Implications for Practice Maloni and co-workers (2013) noted that of the 53 women who were surveyed because they reported PPD, 70% had major depression. The common barriers that prevented them from getting treatment included time and the stigma of PPD diagnosis. Over 90% of the women did use the Internet as a resource to learn about coping with PPD and expressed an interest in a web-based PPD treatment. Comparative Descriptive Design A comparative descriptive design is used to describe variables and examine differences in variables in two or more groups that occur naturally in a setting. A comparative descriptive design compares descriptive data obtained from different groups, which might have been formed using gender, age, educational level, medical diagnosis, or severity of illness. Figure 8-4 provides a diagram of this design’s structure. Fig 8-4 Comparative descriptive design. Research Example Comparative Descriptive Design Research Study Excerpt Buet and colleagues (2013) conducted a comparative descriptive study to describe and determine differences in the hand hygiene (HH) opportunities and adherence of clinical (e.g., nurses and physicians) and nonclinical (e.g., teachers and parents) caregivers for patients in pediatric extended-care facilities (ECFs). The following study excerpt includes key elements of this comparative descriptive design: “Eight children across four pediatric ECFs were observed for a cumulative 128 hours, and all caregiver HH opportunities were characterized by the World Health Organization [WHO] ‘5 Moments for HH.’… A convenience sample of two children from each site ( n  = 8) was observed.… Four observers participated in two hours of didactic training and two hours of monitored practice observations at one of the four study sites to ensure consistent documentation and interpretation of observations. Observers learned how to accurately record HH opportunities and HH adherence using the WHO ‘5 Moments of HH’ data acquisition tool, discussed below. Throughout the study, regular debriefings were also held to review and discuss data recording.…The World Health Organization ( WHO, 2009 ) ‘5 Moments for HH’ define points of contact when healthcare workers should perform HH: ‘before touching a patient, before clean/aseptic procedures, after body fluid exposure/risk, after touching a patient, and after touching patient surroundings…. During approximately 128 hours of observation, 865 HH opportunities were observed.” ( Buet et al., 2013, pp. 72-73 ) Critical Appraisal Buet and associates (2013) clearly described the aspects of their study design but did not identify the specific type of design used in their study. The design was comparative descriptive because the HH opportunities and adherence for clinical and nonclinical caregivers were described and compared. The study included 128 hours of observation (16 hours per child) of 865 HH opportunities in four different ECF settings. Thus the sampling process was strong and seemed focused on accomplishing the study purpose. The data collectors were well trained and monitored to ensure consistent observation and recording of data. HH was measured using an observational tool based on international standards ( WHO, 2009 ) for HH. Implications for Practice Buet and co-workers (2013) found that the HH of the clinical caregivers was significantly higher than the nonclinical caregivers. However, the overall HH adherence for the clinical caregivers was only 43%. The low HH adherence suggested increased potential for transmission of infections among children in ECFs. Additional HH education is needed for clinical and nonclinical caregivers of these children to prevent future adverse events. Quality and Safety Education for Nurses ( QSEN, 2013 ) implications from this study encourage nurses to follow evidence-based practice (EBP) guidelines in adhering to HH measures to ensure safe care of their patients and reduce their risk of potentially life-threatening infections ( Sherwood & Barnsteiner, 2012 ). Correlational Designs The purpose of a correlational design is to examine relationships between or among two or more variables in a single group in a study. This examination can occur at any of several levels— descriptive correlational, in which the researcher can seek to describe a relationship, predictive correlational, in which the researcher can predict relationships among variables, or the model testing design, in which all the relationships proposed by a theory are tested simultaneously. In correlational designs, a large range in the variable scores is necessary to determine the existence of a relationship. Therefore the sample should reflect the full range of scores possible on the variables being measured. Some subjects should have very high scores and others very low scores, and the scores of the rest should be distributed throughout the possible range. Because of the need for a wide variation on scores, correlational studies generally require large sample sizes. Subjects are not divided into groups, because group differences are not examined. To determine the type of correlational design used in a published study, use the algorithm shown in Figure 8-5 . More details on correlational designs referred to in this algorithm are available from other sources ( Grove et al., 2013 ; Kerlinger & Lee, 2000 ). Fig 8-5 Algorithm for determining the type of correlational design. Descriptive Correlational Design The purpose of a descriptive correlational design is to describe variables and examine relationships among these variables. Using this design facilitates the identification of many interrelationships in a situation ( Figure 8-6 ). The study may examine variables in a situation that has already occurred or is currently occurring. Researchers make no attempt to control or manipulate the situation. As with descriptive studies, variables must be clearly identified and defined conceptually and operationally (see Chapter 5 ). Fig 8-6 Descriptive correlational design. Research Example Descriptive Correlational Design Research Study Excerpt Burns, Murrock, and Graor (2012) conducted a correlational study to examine the relationship between BMI and injury severity in adolescent males attending a National Boy Scout Jamboree. The key elements of this descriptive correlational design are presented in the following study excerpt. “Design This study used a descriptive, correlational design to examine the relationship between obesity and injury severity.… The convenience sample consisted of the 611 adolescent males, aged 11-17 years, who received medical attention for an injury at one of eight participating medical facilities. Exclusion criteria were adolescent males presenting with medical complaints unrelated to an injury (e.g., sore throat, dehydration, insect bite) and those who were classified as ‘special needs’ participants because of the disability affecting their mobility or requiring the use of an assistive device.… There were 20 medical facilities located throughout the 2010 National Boy Scout Jamboree. Each facility was equipped to manage both medical complaints and injuries.…” ( Burns et al., 2012, pp. 509–510 ) “Measures Past medical history, weight (in pounds) and height (in inches) were obtained from the HMR [health and medical record]. BMI [body mass index] and gender-specific BMI percentage were calculated electronically using online calculators from the Centers for Disease Control and Prevention and height and weight data. The BMI value was plotted on the CDC’s gender-specific BMI-for-age growth chart to obtain a percentile ranking (BMI-P)…. BMI-P defines four weight status categories: less than 5% is considered underweight, 5% to less than 85% is categorized healthy weight, 85% to less than 95% is the overweight category, and 95% or greater is categorized as obese. Age was measured in years and was self-reported. Severity of injury was measured using the ESI [Emergency Severity Index] Version 4. This five-level triage rating scale was developed by the Agency for Healthcare Research and Quality and provides rapid, reproducible, clinically relevant stratification of patients into levels based on acuity and resource needs.… Training sessions were held for each medical facility to educate staff on the project, process, data collection techniques, and injury severity scoring methods.… All BMI and BMI-P values were recalculated to verify accuracy. To assess interrater reliability for injury severity scoring, ESI scores reported were compared with the primary researcher’s scores. When discrepancies were found, the primary researcher reviewed the treatment record to determine the most accurate score.” ( Burns et al., 2012, p. 510 ) Critical Appraisal Descriptive Correlational Design Burns and colleagues (2012) clearly identified their study design in their research report. The sampling method was a nonrandom sample of convenience that is commonly used in descriptive and correlational studies. Nonrandom sampling methods decrease the sample’s representativeness of the population; however, the sample size was large and included 20 medical facilities at a national event. The exclusion sampling criteria ensured that the subjects selected were most appropriate to address the study purpose. The adolescents’ height and weight were obtained from their medical records but the researchers did not indicate if these were reported or measured by the healthcare professionals. Self-reported height and weight for subjects could decrease the accuracy of the BMI and BMI-P calculated in a study. The BMI-P and severity injury scores were obtained using reliable and valid measurement methods, and the data from the medical facilities were checked for accuracy. The design of this study seemed strong and the knowledge generated provides a basis for future research. Implications for Practice Burns and associates (2012) found a significant relationship between BMI-P and injury severity. They noted that overweight/obese adolescents may have increased risks of serious injuries. Additional research is needed to examine the relationship of BMI to injury risk and to identify ways to prevent injuries in these adolescents. The findings from this study also emphasize the importance of healthy weight in adolescents to prevent health problems. QSEN (2013) implications are that evidence-based knowledge about the relationship between obesity and severity of injury provides nurses and students with information for educating adolescents to promote their health. Predictive Correlational Design The purpose of a predictive correlational design is to predict the value of one variable based on the values obtained for another variable or variables. Prediction is one approach to examining causal relationships between variables. Because causal phenomena are being examined, the terms dependent and independent are used to describe the variables. The variable to be predicted is classified as the dependent variable, and all other variables are independent or predictor variables. A predictive correlational design study attempts to predict the level of a dependent variable from the measured values of the independent variables. For example, the dependent variable of medication adherence could be predicted using the independent variables of age, number of medications, and medication knowledge of patients with congestive heart failure. The independent variables that are most effective in prediction are highly correlated with the dependent variable but are not highly correlated with other independent variables used in the study. The predictive correlational design structure is presented in Figure 8-7 . Predictive correlational designs require the development of a theory-based mathematical hypothesis proposing variables expected to predict the dependent variable effectively. Researchers then use regression analysis to test the hypothesis (see Chapter 11 ). Fig 8-7 Predictive correlational design. Research Example Predictive Correlational Design Research Study Excerpt Coyle (2012) used a predictive correlational design to determine if depressive symptoms were predictive of self-care behaviors in adults who had suffered a myocardial infarction (MI). The following study excerpt presents key elements of this design. “Design, Setting, and Sample A descriptive correlational design examined the relationship between the independent variable of depressive symptoms [agitation and loss of energy] and the dependent variable of self-care. Data were collected from 62 patients in one hospital, who were recovering from an MI in the metropolitan Washington, areaA….” ( Coyle, 2012, p. 128 ) Measures “Beck Depression Inventory II Depressive symptoms were measured using the BDI-II [Beck Depression Inventory II], a well-validated, 21-item scale designed to measure self-reported depressive symptomatology.… Internal-consistency estimates coefficient alpha of the total scores were .92 for psychiatric outpatients and .93 for college students. Construct validity was .93 (p < .001) when correlated with the BDI-I. In this study, the BDI-II Cronbach’s alpha was .68 at baseline.” ( Coyle, 2012, p. 128 ) “Health Behavior Scale Self-care behaviors after an MI were measured by the Health Behavior Scale (HBS), developed specifically for measuring the extent to which persons with cardiac disease perform prescribed self-care behaviors.… This self-report, a 20-item instrument, assesses the degree to which patients perform five types of prescribed self-care (following diet, limiting smoking, performing activities, taking medications, and changing responses to stressful situations).… Cronbach’s alphas for different self-care behaviors ranged from .82 to .95. In this study, reliability was measured by Cronbach’s alpha and was .62 at 2 weeks and .71 at 30 days….Prior to hospital discharge, the Medical and Demographic Characteristics Questionnaire and BDI-II were administered by the researcher.… At 2 weeks and at 30 days after hospital discharge, participants were contacted by telephone to determine responses to the HBS.” ( Coyle, 2012, pp. 128-129 ) Critical Appraisal Coyle (2012) might have identified her study design more clearly as predictive correlational but did clearly identify the dependent variable as self-care and the independent variables as depressive symptoms. The design also included the longitudinal measurement of self-care with the HBS at 2 weeks and 30 days. The design was appropriate to accomplish the study purpose. The sample of 62 subjects was adequate because the study findings indicated significant results. The BDI-II has documented reliability (Cronbach’s alphas > 0.7) and validity from previous studies, but the reliability of .68 was low in this study. Reliability indicates how consistently the scale measured depression and, in this study, it had 68% consistency and 32% error (1.00 − .68 = .32 × 100% = 32%; see Chapter 10 ). HBS had strong reliability in previous studies but the validity of the scale was not addressed. The reliability of HBS was limited at 2 weeks (62% reliable and 38% error) but acceptable at 30 days (71% reliable and 29% error). This study has a strong design with more strengths than weaknesses, and the findings are probably an accurate reflection of reality. The study needs to be replicated with stronger measurement methods and a larger sample. Implications for Practice Coyle (2012) found that depressive symptoms of agitation and loss of energy were significantly predictive of self-care performance in patients with an MI at 30 days post–hospital discharge. Coyle recommended screening post-MI patients for depressive symptoms so that their symptoms might be managed before they were discharged. Further research is recommended to examine depression and self-care behaviors after hospital discharge to identify and treat potential problems. Model Testing Design Some studies are designed specifically to test the accuracy of a hypothesized causal model (see Chapter 7 for content on middle range theory). The model testing design requires that all concepts relevant to the model be measured and the relationships among these concepts examined. A large heterogeneous sample is required. Correlational analyses are conducted to determine the relationships among the model concepts, and the results are presented in the framework model for the study. This type of design is very complex; this text provides only an introduction to a model testing design implemented by Battistelli, Portoghese, Galletta, and Pohl (2013) . Research Example Model Testing Design Research Study Battistelli and co-workers (2013) developed and tested a theoretical model to examine turnover intentions of nurses working in hospitals. The concepts of work-family conflict, job satisfaction, community embeddedness, and organizational affective commitment were identified as predictive of nurse turnover intention. The researchers collected data on these concepts using a sample of 440 nurses from a public hospital. The analysis of study data identified significant relationships ( p  < 0.05) among all concepts in the model. The results of this study are presented in Figure 8-8 and indicate the importance of these concepts in predicting nurse turnover intention. Fig 8-8 Results of the structural equation modeling analysis of the hypothesized model of turnover intention on the cross-validation sample ( n  = 440, standardized path loadings, p  < 0.05, two-tailed). (From Battistelli, A., Portoghese, I., Galletta, M., & Pohl, S. [2012]. Beyond the tradition: Test of an integrative conceptual model on nurse turnover. International Nursing Review, 60 (1), p. 109.) Understanding Concepts Important to Causality in Designs Quasi-experimental and experimental designs were developed to examine causality or the effect of an intervention on selected outcomes. Causality basically says that things have causes, and causes lead to effects. In a critical appraisal, you need to determine whether the purpose of the study is to examine causality, examine relationships among variables (correlational designs), or describe variables (descriptive designs). You may be able to determine whether the purpose of a study is to examine causality by reading the purpose statement and propositions within the framework (see Chapter 7 ). For example, the purpose of a causal study may be to examine the effect of a specific, preoperative, early ambulation educational program on length of hospital stay. The proposition may state that preoperative teaching results in shorter hospitalizations. However, the preoperative early ambulation educational program is not the only factor affecting length of hospital stay. Other important factors include the diagnosis, type of surgery, patient’s age, physical condition of the patient prior to surgery, and complications that occurred after surgery. Researchers usually design quasi-experimental and experimental studies to examine causality or the effect of an intervention (independent variable) on a selected outcome (dependent variable), using a design that controls extraneous variables. Critically appraising studies designed to examine causality requires an understanding of such concepts as multicausality, probability, bias, control, and manipulation. Multicausality Very few phenomena in nursing can be clearly linked to a single cause and a single effect. A number of interrelating variables can be involved in producing a particular effect. Therefore studies developed from a multicausal perspective will include more variables than those using a strict causal orientation. The presence of multiple causes for an effect is referred to as multicausality . For example, patient diagnosis, age, presurgical condition, and complications after surgery will be involved in causing the length of hospital stay. Because of the complexity of causal relationships, a theory is unlikely to identify every element involved in causing a particular outcome. However, the greater the proportion of causal factors that can be identified and examined or controlled in a single study, the clearer the understanding will be of the overall phenomenon. This greater understanding is expected to increase the ability to predict and control the effects of study interventions.

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Mixed Methods in Nursing Research : An Overview and Practical Examples

Ardith z. doorenbos.

School of Nursing, University of Washington, USA, Box 357266, Seattle, WA 98177

Mixed methods research methodologies are increasingly applied in nursing research to strengthen the depth and breadth of understanding of nursing phenomena. This article describes the background and benefits of using mixed methods research methodologies, and provides two examples of nursing research that used mixed methods. Mixed methods research produces several benefits. The examples provided demonstrate specific benefits in the creation of a culturally congruent picture of chronic pain management for American Indians, and the determination of a way to assess cost for providing chronic pain care.

Introduction

Mixed methods is one of the three major research paradigms: quantitative research, qualitative research, and mixed methods research. Mixed methods research combines elements of qualitative and quantitative research approaches for the broad purpose of increasing the breadth and depth of understanding. The definition of mixed methods, from the first issue of the Journal of Mixed Methods Research, is “research in which the investigator collects and analyzes data, integrates the findings, and draws inferences using both qualitative and quantitative approaches or methods in a single study or program of inquiry” ( Tashakkori & Creswell, 2007 , p.4).

Mixed methods research began among anthropologists and sociologists in the early 1960s. In the late 1970s, the term “triangulation” began to enter methodology conversations. Triangulation was identified as a combination of methodologies in the study of the same phenomenon to decrease the bias inherent in using one particular method ( Morse, 1991 ). Two types of sequencing for mixed methods design have been proposed: simultaneous and sequential. Type of sequencing is one of the key decisions in mixed methods study design. Simultaneous sequencing is postulated to be simultaneous use of qualitative and quantitative methods, where there is limited interaction between the two sources of data during data collection, but the data obtained is used in the data interpretation stage to support each method's findings and to reach a final understanding. Sequential sequencing is postulated to be the use of one method before the other, as when the results of one method are necessary for planning the next method.

Since the 1960s, the use of mixed methods has continued to grow in popularity ( O'Cathain, 2009 ). Currently, although there are numerous designs to consider for mixed methods research, the four major types of mixed methods designs are triangulation design, embedded design, explanatory design, and exploratory design ( Creswell & Plano Clark, 2007 ). The most common and well-known approach to mixed methods research continues to be triangulation design.

There are many benefits to using mixed methods. Quantitative data can support qualitative research components by identifying representative patients or outlying cases, while qualitative data can shed light on quantitative components by helping with development of the conceptual model or instrument. During data collection, quantitative data can provide baseline information to help researchers select patients to interview, while qualitative data can help researchers understand the barriers and facilitators to patient recruitment and retention. During data analysis, qualitative data can assist with interpreting, clarifying, describing, and validating quantitative results.

Four broad types of research situations have been reported as benefiting particularly from mixed methods research. The first situation is when concepts are new and not well understood. Thus, there is a need for qualitative exploration before quantitative methods can be used. The second situation is when findings from one approach can be better understood with a second source of data. The third situation is when neither a qualitative nor a quantitative approach, by itself, is adequate to understanding the concept being studied. Lastly, the fourth situation is when the quantitative results are difficult to interpret, and qualitative data can assist with understanding the results ( Creswell & Plano Clark, 2007 ).

The purpose of this article is to illustrate mixed methods methodology by using examples of research into the chronic pain management experience among American Indians. These examples demonstrate the methodology used to provide (a) a detailed multilevel understanding of the chronic pain care experience for American Indians using triangulation design (multilevel model), and (b) a comparison of cost for two different chronic pain care delivery models, also using triangulation design (data transformation model).

An Example : Understanding the Pain Management Experience Among American Indians

Chronic pain poses unique challenges to the American health care system, including ever-escalating costs, unintentional poisonings and deaths from overdoses of painkillers, and incalculable suffering for patients as well as their families. Approximately 100 million adults in the United States are affected by chronic pain, with treatment costs and losses in productivity totaling $635 billion annually ( Institute of Medicine, 2011 ). Symptoms of pain are the leading reason patients visit health care providers ( Hing, Cherry, & Woodwell, 2006 ).

At the level of the community-based primary care provider, especially in tribal areas of the United States, there is often not enough capacity to manage complex chronic pain cases, and this is often due to lack of access to specialty pain care ( Momper, Delva, Tauiliili, Mueller-Williams, & Goral, 2013 ). The American Indian population in particular is underserved by health care and the most vulnerable to the impact of chronic pain, with high rates of drug poisoning due to opioid analgesics ( Warner, Chen, Makuc, Anderson, & Minino, 2011 ). There are 2.9 million people who report exclusive and an additional 1.6 million who report partial American Indian ancestry in the United States. They are a diverse group, residing in 35 states and organized into 564 federally recognized tribes ( U.S. Census Bureau, 2010 ). However, there is a scarcity of published literature exploring the experience, epidemiology, and management of pain among American Indians ( Haozous, Knobf, & Brant, 2010 ; Haozous & Knobf, 2013 ; Jimenez, Garroutte, Kundu, Morales, & Buchwald, 2011 ).

Using Mixed Methods to Overcome Barriers to Research

Barriers to effective research into chronic pain management among American Indians include the relatively small number of American Indian patients in any circumscribed area or tribe, the limitations of individual databases, and widespread racial misclassification. A mixed methods research approach is needed to understand the complex experience, epidemiology, and management of chronic pain among American Indians and to address the strengths and weaknesses of quantitative methodologies (large sample size, trends, generalizable) with those of qualitative methodologies (small sample size, details, in-depth).

This first example is from an ongoing study that uses triangulation design to provide a better understanding of the phenomenon of chronic pain management among American Indians. The study uses a multilevel model in which quantitative data collected at the national and state levels will be analyzed in parallel with the collection and analysis of the qualitative data at the patient level (see Figure 1 ). This allows the weakness of one approach to be offset by the strengths of the other. The results of the separate level analyses will be compared, contrasted, and blended leading to an overall interpretation of results.

Role of quantitative data

Previous examination of U.S. national databases has reported a higher prevalence of lower back pain in American Indians than in the general population (35% compared to 26% ; Deyo, Mirza, & Martin, 2002 ). Thus, at level 1, quantitative administrative data sets representing health care received by American Indians, both across the United States and in broad regions, will be used to evaluate macro-level trends in utilization of health care and in basic outcomes, such as opioid-related deaths.

At level 2, more detailed quantitative Washington state tribal clinic data will be used to identify American Indian populations, evaluate breakdowns in the delivery of care, and identify processes that lead to unsuccessful outcomes. For example, in a study conducted with community health practitioners in Alaska, participants reported low levels of knowledge and comfort around discussing cancer pain ( Cueva, Lanier, Dignan, Kuhnley, & Jenkins, 2005 ).

Role of qualitative data

At level 3, qualitative research through focus groups and key informant interviews will provide even more refined information about perceptions of recommended and received care. These interviews will provide insight into selected immediate and proximal factors. These factors include patients' choice and use of services; attitudes, motivations, and perceptions that influence their decisions; interpersonal factors, such as social support; and perceived discrimination. This qualitative data will shed light on potential barriers to care that are not easily recognized in administrative or clinical records, and thereby will provide greater detail about patient views of chronic pain care.

Role of (qualitative) indigenous methodologies

Since the focus of this study is on the chronic pain experience among American Indian patients, it is important that the qualitative work in level 3 be guided by indigenous methodologies, in both data collection and analysis. The phrase “indigenous methodologies” refers to an evolving framework for creating research that places the epistemologies of indigenous participants and communities at the center of the work, while building an equitable and respectful setting for bidirectional learning ( Evans, Hole, Berg, Hutchinson, & Sookraj, 2009 ; Louis, 2007 .; Smith, 2004 ). Although the tenets of indigenous methodologies vary according to the source, there is agreement among sources that research with indigenous populations should be wellness-oriented, holistic, community-oriented, and focused on indigenous knowledge, and should incorporate bidirectional learning ( Louis, 2007 ; Smith, 2004 ).

The ongoing project aligns with these guidelines by building knowledge about the chronic pain experience from the perspective of American Indian patients. The data is being interpreted with the goal of designing a usable and relevant model that will resonate at the American Indian community level. The researchers have conducted focus groups with the needs and priorities of the participants placed at the forefront, to best achieve the goals of learning and building knowledge that reflects the participants' experiences. Specifically, the focus groups were scheduled within three tribes, ensuring high familiarity and social support among group members. These focus groups met either at a tribal community center or in a nearby tribally owned casino in the evening. Each focus group started with a dinner, followed by discussion.

The focus group facilitator was well-known to the community, and although not American Indian, had been an active participant in community events and had provided expert knowledge and consultation to the tribes. Additionally, each focus group was co-facilitated by a tribal elder. The high familiarity among the participants and the research team was an important component of the bidirectional learning: it helped reduce much of the mistrust that has historically prevented medical researchers from obtaining high-quality data in similarly vulnerable populations ( Guadagnolo, Cina, & Helbig, 2009 ).

Benefits of Triangulation Design: Multilevel Model

In summary, only a mixed methods study that included quantitative and qualitative methods could provide the data required for a comprehensive multilevel assessment of the chronic pain experience among American Indians. Although this study is ongoing, the plan is for a nationwide analysis of variations in chronic pain outcomes among American Indians to examine the structure of service delivery and organization. Analysis of the state tribal clinic data will address intermediate factors and will examine community-level variation in pain management and local access to pain specialists. Preliminary analysis of the focus group data has already demonstrated that there is insufficient pain management among American Indians, due in part to lack of knowledge about pain management among providers and lack of access to pain specialists.

An Example; Comparing the Costs of Two Models for Providing Chronic Pain Care to American Indians

Telehealth is one innovative approach to providing access to high-quality interdisciplinary pain care for American Indians. A telehealth model with a unique approach based on provider-to-provider videoconference consultations allows community-based providers to present complex chronic pain cases to a panel of pain specialists through a videoconferencing infrastructure that also incorporates longitudinal outcomes tracking to monitor patient progress. Telehealth is an innovative model of health care delivery, and its use among American Indians has been expanding over the past several years ( Doorenbos et al., 2010 ; Doorenbos et al., 2011a ; 2011b ). Although the use of telehealth for providing chronic pain consultation is still in early stages, the long-term effectiveness of this approach and its impact on increasing capacity for pain management among community providers is being investigated ( Haozous et al., 2012 ; Tauben, Towle, Gordon, Theodore, & Doorenbos, 2013 ). The mixed methods approach for this transaction cost analysis used a unique triangulation design with a data transformation model to build a body of evidence for telehealth pain management.

With ever increasing mandates to reduce the cost and increase the quality of pain management, health care institutions are faced with the challenge of demonstrating that new technologies provide value while maintaining or even improving the quality of care ( Harries & Yellowlees, 2013 ). Transaction cost analysis can provide this evidence by using mixed methods research methodologies to provide comparative evaluation of the costs and consequences of using alternative technologies and the accompanying organizational arrangements for delivering care ( Williamson, 2000 ).

The theory of transaction cost developed from the observation that our structures for governing transactions—the ways in which we organize, manage, support, and carry out exchange — have economic consequences ( Williamson, 1991 ). Though prices matter, this theory recognizes that prices can and do deviate from the cost of production and do not include the cost of transacting ( Coase, 1960 ). Setting aside neoclassical economic conceptions of price, output, demand, and supply, the transaction becomes the unit of analysis ( Williamson, 1985 ).

In transactions, there are typically two parties engaging in the exchange of goods or services, and both exert effort to carry out the transaction, incurring costs in the hope or with the expectation of realizing benefits. Some ways of structuring or supporting a given transaction, such as consultation or treatment for a patient from a health care provider, may be more efficient than others. The analysis examines the actual costs incurred and the related consequences experienced by the parties over time, with the hypothesis that efficiency results from the discriminating alignment of transactions with alternative, more efficient structures of governance ( Williamson, 2002 ).

Specialty health care services participating in the study described here included the University of Washington (UW) Center for Pain Relief and the UW TelePain program. The UW Center for Pain Relief is an outpatient multispecialty consultation and treatment clinic that uses the assembled expertise and skills of physicians and other medical team providers to assist in diagnosis and care for chronic pain, for example for people with painful disorders that have persisted beyond expected duration, or for people who have persistent uncontrolled pain despite appropriate treatment for the underlying medical condition. The clinic also offers pain consultation and treatment for a variety of new-onset or acute problems that may benefit from selective anesthetic procedures, such as nerve blocks or spinal nerve root compression.

The UW TelePain program serves tribal providers in the Washington, Wyoming, Alaska, Montana, and Idaho (WWAMI) region. These tribal providers include primary care physicians, physician assistants, and nurse practitioners. The tribal providers have access to weekly videoconferences both with other community providers and with university-based pain and symptom management experts. During videoconferences, providers manage cases, engage in evidence-based practice activities, and receive peer support. Throughout the process, these community providers are responsible for direct patient care, and they act on recommendations of the consulting pain specialists.

The two care delivery models discussed above — traditional in-clinic consultation at the Center for Pain Relief and telehealth case consultation through TelePain — provided this mixed methods study using triangulation design and a data transformation model with two comparative arrangements for delivering the same transaction: delivery of pain care to patients (see Figure 2 ).

Qualitative and Quantitative Data Collection Procedures

Participant observation and structured interviews were used to identify and describe two comparable completed transactions for patients with chronic pain. Members of the clinical care teams selected one transaction from each service for which the care could be said to represent the routines and norms of their health care organization. The chosen transactions were carried out with patients of the same gender, similar age, and similar health characteristics. For the study, clinical care teams from each service provided two qualitative on-site interviews documenting clinical work flow and processes (i.e., the steps in the transaction). For the in-clinic transaction, members of the clinical care team interviewed included a nurse care coordinator, pain specialist, medical assistant, patient outcomes assessment coordinator, nurse triage manager, patient support services supervisor, and financial authorization specialist. For the Tele-Pain transaction, team members interviewed included the TelePain nurse care coordinator, two pain specialists, an information technology specialist, and the clinic provider.

The following details the process of the mixed methods analysis. First, individual steps, or discrete tasks, within each transaction (in-clinic versus TelePain) were identified using qualitative interviews and itemized in detail. Details from the qualitative data included a description of each task, the person (s) engaged, the duration of engagement of each person in minutes, the information accrued to the patient's medical record, the technologies employed, and the locations where tasks were conducted and information was transmitted or stored.

The quantitative data collected included date and time, and therefore duration in business days, that accumulated with each step in the transaction. Finally, the costs of each step collected from the qualitative data were identified and transformed into quantitatively estimated data for each transaction. Analysis focused on the primary costs in health care: the value of people's time. These values were limited to labor costs for the in-clinic and telehealth personnel; proxies for the value of time were used with estimates of time for the patient. Costs were estimated as a function of time spent per task and per patient, and the actual wage, including benefits, of personnel engaged in the transaction.

Qualitative and Quantitative Data Analysis

Personal identifiable information was redacted from each patient's medical record, and the records were reviewed for comparability as well as for norms and routines of care for the in-clinic and telehealth organizations. The characteristics of the two patients were similar. Both were first-time patients to their respective organizations, and were referred by their primary care providers for specialized care. The reasons for seeking care and report of conditions potentially related to chronic pain were similar. Both transactions resulted in a consultation recommending referral for additional specialized care or treatment.

Two work flows, one in-clinic and one telehealth, were developed by documenting actual tasks undertaken during the transactions. In follow-up interviews, these work flows were presented to participants for review and comment. These interviews resulted in a complete itemized list of dates, personnel, and time spent per person on discrete steps or tasks. Tables and graphs expressing the steps, with cost accrual over time and in sum, were developed and compared for each transaction, to each other, and with respect to participants' rationales for the tasks in each transaction.

The equation expressing the cost per transaction is as follows, where the total cost of the transaction ( C T ) is the sum of the costs of each discrete task ( k i ) in the transaction, measured per participant ( x, y, z …) on the task, as the product of time ( t ) and wage rate ( w ), or in the case of the patient ( x, y, z …), a proxy for the value of time ( w ) and estimated time ( t ).

In total, 46 discrete steps were taken for the typical in-clinic transaction at the UW Center for Pain Relief (one patient case, reviewed by two pain specialists) versus 27 steps for the typical TelePain transaction (three patient cases, reviewed by six pain specialists). The greater number and types of administrative steps taken to schedule, execute, and follow up the in-clinic consultation resulted in greater duration of time between receipt of initial referral request and completion of the initial consultation with the pain specialists. A total of 153 business days (213 calendar days) elapsed between referral and the completion of the entire in-clinic transaction, versus 4 business days (4 calendar, days) for the TelePain transaction. Importantly, for the transaction at the UW Center for Pain Relief, 72 business days transpired before consultation concluded with a referral for the patient's record; the same conclusion was reached in 4 days in the TelePain transaction. These methods used to determine transaction costs provide an excellent example of mixed methods research, where both qualitative and quantitative data and analysis are needed to provide the transaction cost results.

Mixed methods are increasingly being used in nursing research. We have detailed two studies in which mixed methods research with triangulation design brought a richness to the examination of the phenomenon that a single methodology would not In the two examples described, a major advantage of the triangulation design is its efficiency, because both types of data are collected simultaneously. Each type of data can be collected and analyzed separately and independently, using the techniques traditionally associated with each data type. Both simultaneous and sequential data collection lend themselves to team research, in which the team includes researchers with both quantitative and qualitative expertise.

Challenges include the effort and expertise required due to the simultaneous data collection, and the fact that equal weight is usually given to each data type. Thus this research requires a team, or extensive training in both quantitative and qualitative methodologies, and careful adherence to the methodological rigor required for both methodologies. Nursing researchers may face the possibility of inconsistency in research findings arising from the objectivity of quantitative methods and the subjectivity of qualitative methods. In these cases, additional data collection may be required.

The first example, regarding the pain management experience among American Indians, used triangulation design in a multilevel model format. The multilevel model was useful in designing this study as different methods were needed at different levels to fully understand the complex health care system. In this example, quantitative data is being collected and analyzed at the national and state levels, and qualitative data is being collected at the patient level. Both qualitative and quantitative data are being collected simultaneously. The findings from each level will then be blended into one overall interpretation.

The second example, a transaction cost analysis, also used triangulation design, but the model used was that of data transformation. As in the multilevel model used in the first example, the data transformation model involved the separate but concurrent collection of qualitative and quantitative data. A novel step in this model involves transforming the qualitative data into quantitative data, and then comparing and interrelating the data sets. This required the development of procedures for transforming the qualitative data, related to, time spent on a step and salary of the provider, into quantitative cost data.

The two studies presented as examples demonstrate mixed methods research resulting in the creation of (a) a rich description of the American Indian chronic pain experience, and (b) a way to assess cost for providing chronic pain care via tribal clinics. In both examples, the quantitative data and their subsequent analysis provide a general understanding of the research problem. The qualitative data and their analysis refine and explain the results by exploring participants' views in more depth. Research using a single methodology would not have been able to achieve the same results.

Acknowledgments

Research reported in this paper was supported by the National Institute of Nursing Research of the National Institutes of Health under award number #R01NR012450 and the National Cancer Institute of the National Institutes of Health under award number #R42 CA141875. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Differences between Qualitative & Quantitative Research

" Quantitative research ," also called " empirical research ," refers to any research based on something that can be accurately and precisely measured.  For example, it is possible to discover exactly how many times per second a hummingbird's wings beat and measure the corresponding effects on its physiology (heart rate, temperature, etc.).

" Qualitative research " refers to any research based on something that is impossible to accurately and precisely measure.  For example, although you certainly can conduct a survey on job satisfaction and afterwards say that such-and-such percent of your respondents were very satisfied with their jobs, it is not possible to come up with an accurate, standard numerical scale to measure the level of job satisfaction precisely.

It is so easy to confuse the words "quantitative" and "qualitative," it's best to use "empirical" and "qualitative" instead.

Hint: An excellent clue that a scholarly journal article contains empirical research is the presence of some sort of statistical analysis

See "Examples of Qualitative and Quantitative" page under "Nursing Research" for more information.

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There are two kinds of research: Quantitative and Qualitative

Quantitative is research that generates numerical data. If it helps, think of the root of the word "Quantitative." The word "Quantity" is at its core, and quantity just means "amount" or "how many." Heart rates, blood cell counts, how many people fainted at the jazz festival-- these are all examples of quantitative measures.

Qualitative , on the other hand, is a more subjective measurement. Think of the root of the word again, this time it's "Quality." If someone is called a quality person or someone's selling a high quality product, they're being measured in subjective terms, rather than concrete, objective terms (like numbers.) Qualitative research includes things like interviews or focus groups.

Just like when we examine whether or not our article is an example of Primary Research, the best way to examine what kind of data your article uses is by reading the article's Abstract, Methodologies, and Results sections. That will tell you how the research was conducted and what kind of data (qualitative or quantitative) was collected.

An example of what to look for in the Abstract can be seen here:

You can see that data was evaluated (66% of students were in compliance with school immunization requirements), a strategy was implemented (letters and emails were sent to student's parents/guardians), and at the end of the study, new quantitative data is reported (99.6% of students were in compliance with vaccination requirements).

Finding qualitative research can be trickier, since it can often take more time to collect. Examples of qualitative data include things like interview transcripts, focus group feedback, and journal entries detailing people's experiences and feelings. The easiest way to search for a qualitative study is to include the word "qualitative" as a keyword in your database search along with the search terms about the topic you're interested in.

Check out the video below to see an example of searching for qualitative research in CINAHL.

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What is Quantitative Research?

Quantitative research , also called " empirical research ," is research based on traditional scientific methods which generate numerical data and seek to establish causal relationships between two or more variables , using statistical methods to test the strength and significance of the relationships. Observations in quantitative research are measured in numbers.

Quantitative research starts with a testable hypothesis. One of the goals of quantitative research design is others can repeat the findings of the study. Some examples of quantitative research methods include close-ended interviews, surveys, clinical trials, and lab experiments.

Adapted from Finding Quantitative or Qualitative Nursing Research Articles (Simmons University)

Types of Quantitative Research

Four main types of quantitative research design:

Descriptive Design  - Seeks to describe the current status of a variable or phenomenon. The researcher develops a hypothesis after the data is collected. Data collection is mostly observational.

Correlational Design  - Explores the relationship between variables using statistical analyses. The researcher does not look for cause and effect. Data collection is mostly observational.

Quasi-Experimental Design  - Seeks to establish a cause-effect relationship between two or more variables. The researcher does not assign groups or manipulate the independent variable. Control groups are identified and exposed to the variable. Results are compared with results from groups not exposed to the variable.

Experimental Design -  Uses the scientific method to establish cause-effect relationship among a group of variables in a research study. Researchers make an effort to control all variables except the one being manipulated (the independent variable). The effects of the independent variable on the dependent variable are collected and analyzed for a relationship.

Adapted from Quantitative Approaches - Center for Innovation in Research and Teaching  

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An Introduction to Quantitative Research (37 mins) From Academic Videos Online, this video presented by Jaime Dyce covers seven areas: an introduction to quantitative research, quantitative data collection, experimental design, quantitative analysis, quantitative and qualitative methodologies, writing research reports, and ethics.

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental? (YouTube) Students often have difficulty classifying quantitative research designs. In quantitative research, designs can be classified into one of three categories: descriptive non-experimental, quasi-experimental or experimental. To identify which of these designs your study is using follow the steps in this video.

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It's Not Always One or the Other!

It's important to keep in mind that research studies and articles are not always 100% qualitative or 100% quantitative. A mixed methods study involves both qualitative and quantitative approaches. If you need to find articles that are purely qualitative or purely quanititative, be sure to look carefully at the methodology sections to make sure the studies did not utilize both methods. 

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Qualitative and Quantitative Research

In general, quantitative research seeks to understand the causal or correlational relationship between variables through testing hypotheses, whereas qualitative research seeks to understand a phenomenon within a real-world context through the use of interviews and observation. Both types of research are valid, and certain research topics are better suited to one approach or the other. However, it is important to understand the differences between qualitative and quantitative research so that you will be able to conduct an informed critique and analysis of any articles that you read, because you will understand the different advantages, disadvantages, and influencing factors for each approach. 

The table below illustrates the main differences between qualitative and quantitative research. Be aware that these are generalizations, and that not every research study or article will fit neatly into these categories. 

Systematic reviews, meta-analyses, and integrative reviews are not exactly designs, but they synthesize, analyze, and compare the results from many research studies and are somewhat quantitative in nature. However, they are not truly quantitative or qualitative studies.

References:

LoBiondo-Wood, G., & Haber, J. (2010). Nursing research: Methods and critical appraisal for evidence-based practice (7 th ed.). St. Louis, MO: Mosby Elsevier

Mertens, D. M. (2010). Research and evaluation in education and psychology (3 rd ed.). Los Angeles: SAGE

Quick Overview

This 2-minute video provides a simplified overview of the primary distinctions between quantitative and qualitative research.

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Nursing Research - Undergraduate: Quantitative vs. Qualitative

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What is the difference?

Quantitative research and qualitative research are two types of original research that you will come across when you are researching for original nursing research. 

While both contain useful data to use, you may be required to use one particular type of data for a paper or presentation. 

Definitions

Quantitative research : A traditional approach to research in which variables are identified and measured in a reliable and valid way (Houser, 2018, p. 34)

Qualitative research : A naturalistic approach to research in which the focus is on understanding the meaning of an experience from the individual's perspective (Houser, 2018, p. 35)

Houser, J. (2018). Nursing research: Reading, using, and creating evidence  (4th ed.).  Jones & Bartlett Learning. 

Quantitative Research

Think of quantitative research as a scientific experiment. You have your hypothesis, an item you want to change, an item you are comparing the change against, and then the results of your experiment.

At its core, quantitative research involves a control variable and an independent variable. Typically with nursing research, the independent variable will be the proposed change or intervention that you are looking to implement in your practice . Results of a quantitative research study should be something that can be replicated. Data results for quantitative research typically involve hard data, such as blood pressure, temperature, oxygen levels, etc. 

Types of Research Used for Evidence-Based Practice

Ovid. [OvidWoltersKluwer]. (2015, October 6). Types of research used for evidence- based practice [Video file]. YouTube.  https://youtu.be/jwOu24btBVk

Qualitative Research

Qualitative research focuses more on soft data, meaning it observes an individual's experience and cannot be replicated (Houser, 2018). Types of research studies that are qualitative include: "observations, in-depth interviews or focus-groups, case studies, and social interaction studies" (Houser, 2018). 

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Emotions reflected in colours: experiences of nursing students during the COVID-19 period

• Eda Atay   ORCID: orcid.org/0000-0003-2592-8254 1 ,
• Ezgi Dirgar   ORCID: orcid.org/0000-0001-8214-7441 1 , 2 ,
• Kadiriye Pehlivan   ORCID: orcid.org/0000-0001-8596-5837 1 ,
• Betül Tosun   ORCID: orcid.org/0000-0002-4505-5887 1 , 3 ,
• Ahmet Ayaz   ORCID: orcid.org/0000-0002-4081-2033 1 , 4 ,
• Ayla Yava   ORCID: orcid.org/0000-0003-3468-6779 1 &
• Juan M. Leyva-Moral 1 , 5  

BMC Nursing volume  23 , Article number:  575 ( 2024 ) Cite this article

Metrics details

The aim of this study was to identify nursing students’ fears and emotions and to concretise the metaphors they used to describe their feelings towards the COVID-19 pandemic.

This study was conducted with nursing students at a foundation university between December 2021 and February 2022 using a sequential mixed methods research design. In the quantitative part of the study, 323 nursing students answered the ‘Positive and Negative Emotion Scale’ and the ‘COVID-19 Fear Scale’. In the qualitative part, students were asked to metaphorise COVID-19 with a colour and 21 in-depth interviews were conducted on the reasons for choosing this colour.

The average age of the students participating in the study was 21.41 ± 1.97 years and 78% of them were female. It was observed that 15.8 of the students had previously tested positive for COVID-19. Most of the participants (98.5%) were vaccinated against COVID-19 and 31.9% had a relative who died due to COVID-19. When the participants were asked which colour they compared COVID-19 to, it was observed that more than half of them chose red (51.4%) among bright colours and 13% chose black among dark colours. In this study, it was determined that students who chose dark colours to describe COVID-19 had higher COVID-19 Fear and Negative Emotion Scale scores. In in-depth interviews, it was observed that students who chose dark colours were more deeply affected by the COVID-19 process, while students who chose light colours associated this period with negative emotions.

In this study, it was observed that nursing students’ feelings and thoughts about the COVID-19 period in line with their experiences affect the choice of colour in metaphorisation.

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Introduction

The COVID-19 pandemic has turned into a social trauma that has deeply affected individuals in many ways, such as in physical, social, economic, and psychological terms [ 1 , 2 ]. The pandemic had devastating effects on society and higher education institutions, nursing education, and clinical learning environments [ 3 ]. Educators have had to move face-to-face online courses, conceptualize, offer alternative clinical experiences, and redefine how student performance is evaluated and graded [ 4 ]. As some students have expressed, the pandemic has deprived them of learning to provide nursing care in this new viral age [ 3 ]. Studies conducted on nursing students have shown that as the duration of social isolation increases, stress and fear increase too; however, psychological problems such as anxiety and depression, and coping behaviors weaken [ 5 , 6 ]. Existing evidence indicates that nursing students felt negatively affected by lockdown during the pandemic, they felt overwhelmed and nervous, and they feared infection and death [ 3 , 7 ]. Examining the emotions of nursing students during the pandemic process is challenging and insufficient when relying solely on standardized measurement tools. In recent years, metaphors have been frequently used to determine individuals’ perceptions of phenomena and events they have encountered [ 8 ]. Therefore, the use of metaphors is a method that can significantly contribute to the in-depth examination of students’ emotions.

Metaphors are defined as tools to explain events and objects using different concepts and representations [ 9 ]. They help associate individuals’ perceptions of events with familiar situational images [ 10 , 11 ]. Metaphors concretize abstract concepts by conveying the events experienced in an emotionally authentic way [ 12 , 13 ]. Thus, they provide an opportunity to reveal how a person interprets experiences and events [ 14 ] and illuminate previously unperceived aspects of phenomena and deepen understanding [ 10 ]. An in-depth examination of nursing students’ feelings towards the pandemic process is crucial in identifying their individual and professional needs and creating strategies to address them. However, there are few studies that deeply examine nursing students’ feelings, thoughts, and experiences related to the pandemic process [ 15 , 16 ].

Emotions such as fear and anxiety experienced by nursing students during the COVID-19 outbreak have mainly been evaluated using quantitative methods [ 5 , 17 ]. Given the complexity of emotions and the inadequacy of standard measurement tools alone, the integration of metaphors can provide richer insights into nursing students’ emotional experiences during the pandemic. Studies have analyzed metaphors to understand the feelings, thoughts, and experiences of various populations working on the front lines during the COVID-19 pandemic [ 2 , 9 , 13 ]. For example, Çakmak et al. (2022) discovered that patients used metaphors like “black hole/dark” for COVID-19 treatment, “steel” for family relationships, and the “sea” for mental health. Fear of death and uncertainty negatively affected family relationships and mental well-being [ 9 ]. Gök & Kara (2022) employed metaphor analysis and identified seven categories: “being restricted,” “restlessness,” “uncertainty/obscurity,” “deadly/dangerous,” “struggling,” “faith/destiny,” and “supernatural.” These categories reflected three themes: “anxiety/concern,” “risk,” and “faith.” In their study aimed at revealing implicit collective emotions related to the COVID-19 pandemic among individuals aged 19 to 79 [ 13 ], Stanley et al. (2021) demonstrated four aligned mental models of the pandemic: (a) uncertainty, (b) danger, (c) grotesque, and (d) misery. According to these mental models, participants’ implicit emotional experiences of COVID-19 converged around several deeply held emotions: (a) grief, (b) disgust, (c) anger, and (d) fear. The study emphasized that these findings have both theoretical and practical implications. It was highlighted that metaphors served to document collective emotions associated with a collective traumatic experience unfolding in real time [ 2 ].

Michel et al. (2021) reported that pandemic stressors harmed students’ well-being and learning, leading to frustration due to limited clinical experiences. Barriers included reduced engagement, poor communication, increased workloads, isolation, learning anxiety, and logistical challenges [ 7 ]. For this reason, it is thought to be important to express the feelings of nursing students, who will serve in the field as health professionals after graduation, about the COVID-19 pandemic through metaphors, including those related to colours, in order to increase the form and quality of nursing education to be provided in a similar pandemic. In addition, it is believed that expressing feelings about the pandemic process will increase both individual and professional resilience and thus the basic step of becoming a qualified healthcare professional will be taken.

Metaphors offer important support for understanding the strong relationships between colour concepts and abstract emotional states [ 18 ]. For instance, it has been published that university students associated the colour red with feelings of energy, love, passion, courage, excitement, danger, and aggression. This association with red can help us understand nursing students’ experiences of intense emotions like passion for their work or the danger and stress they felt during the pandemic. Similarly, the colour blue was associated with feelings of pleasure, comfort, calm, confidence, security, and coldness. This can illustrate moments when nursing students felt calm or secure in their knowledge, as well as times when they might have felt emotionally distant or isolated. The colour yellow, linked with feelings of warmth, joy, hope, optimism, pleasantness, and happiness, can highlight the moments of joy or optimism they experienced, even in challenging times. By using colour metaphors, we can better grasp the nuanced emotional experiences of nursing students during the pandemic, allowing for a more comprehensive understanding of their emotional landscape and helping to inform strategies to support them more effectively in future crises [ 16 ].

Based on the existing literature, this study aims to explore the fears and emotions of nursing students and capture the metaphors they employ to express their sentiments with appropriate themes regarding the COVID-19 pandemic, with a particular focus on the use of colours in these metaphors.

Materials and methods

The study was conducted using an explanatory sequential mixed method research design with nursing students at a foundation university in Turkey between December 2021 and February 2022. This design involves two distinct phases: an initial quantitative phase followed by a qualitative phase to explain and build upon the quantitative results. Specifically, quantitative data was first collected using surveys, and then qualitative data was obtained through focus group interviews to gain deeper insights and explanations related to the survey findings [ 19 ].

Research questions

What are the predominant fears and emotions experienced by nursing students during the COVID-19 pandemic?

How do nursing students use colour metaphors to express their psychological, social, and physical experiences related to the COVID-19 pandemic?

What are the common themes that emerge from the metaphors nursing students use to describe their sentiments towards the COVID-19 pandemic?

Study sample

For the quantitative part of the study, a total of 387 nursing students from a foundation university in Turkey were invited to participate in the study between December 2021 and February2022. The inclusion criteria for this study were to be an actively enrolled undergraduate student in the nursing department in 2021–2022 and to volunteer to participate in the study. Those who did not meet the inclusion criteria were excluded from the study. A sample size of 320 participants was calculated for a 50% heterogeneity, 3% margin error, and a 99% of confidence level. By the end of data collection period 323 valid questionnaires were received (83.46% participation rate). In qualitative research, the quality of the sample is important. For this reason, research is usually conducted with a small number of purposively determined samples. As a result of in-depth interviews, individual interviews are terminated when the data reaches saturation [ 20 ]. For the qualitative part of the study, at least 2 participants representing each of the colours selected in the quantitative part were foreseen, but the final sample size was determined according to data saturation and in-depth interviews were conducted with 21 students.

Data collection process and measurement tools

Sociodemographic data collection.

Sociodemographic details were collected, including participants’ gender, academic year, previous COVID-19 positive diagnosis, COVID-19 vaccine status, chronic diseases, living with, previous family COVID-19 positive diagnosis, and any relative who died because of COVID-19.

Quantitative data collection

Next, quantitative data were collected using the Positive and Negative Affect Scale initially created by Watson et al. (1988) and later validated into Turkish by Gençöz (2000) [ 21 , 22 ]. Then, the COVID-19 Fear Scale, initially created by Ahorsu et al. (2020) [ 23 ] and validated into Turkish by Satıcı et al. (2020), was used [ 24 ].

The Positive and Negative Affect Scale consists of 20 items in two sub-dimensions (10 positive and ten negative emotions). The emotion in each item in the scale is scored between “1 = very little” and “5 = very much.” Each sub-dimension varies between 1 and 50 points. The total score obtained from the sub-dimensions of the scale indicates positive or negative emotional loads. In the Turkish adaptation of the scale, the internal consistency coefficient for positive mood is 0.86, and the internal consistency coefficient for negative mood is 0.83. In this study, the internal consistency coefficient of the positive mood of the scale is 0.86, and the internal consistency coefficient of the negative mood is 0.86.

The COVID-19 Fear Scale consists of seven 5-point Likert type items (1 = strongly disagree, 5 = strongly agree) and one dimension. The scale is scored between 7 and 35, indicating that individuals with high scores have a high fear of COVID-19. In the Turkish adaptation of the scale, the Cronbach’s coefficient of the scale is 0.82. In this study, the Cronbach’s coefficient of the scale is 0.86.

In the last step of the quantitative data collection tool, a triggering question was asked: “With which colour does COVID-19 define/remind/represent you?” This is a single question and not a scale. This question was asked to prepare the ground for the qualitative data collection part of the research and to create a group of students who prefer different colours when selecting the students to be interviewed in the focus group. The students were free to choose colours, and all of the students chose colours without separating them into shades. Then, as a result of the in-depth interviews, the colour choices and expressions of the students according to the answers were evaluated by the researchers, and the participants were divided into two groups (dark and light colours). Six participants were purposively selected from 67 students expressing dark colours (black, grey, or purple) and 21 participants were purposively selected from 256 students expressing bright colours (white, orange, blue, or green).

Qualitative data collection

Open-ended questions were used for the qualitative part. Interviews were conducted face-to-face in a safe and quiet room at the university. All interviews were audio recorded and transcribed verbatim immediately after. To encourage anonymity, students were referred to as Participant 1, Participant 2 instead of using their personal information. Interviews lasted approximately 35 min and were performed by a researcher with proven experience performing qualitative interviews. The script of the interview was created by the researchers in line with the literature [ 5 , 19 ], and it was approved using discussion and consensus techniques with all the research members and two experts from the Department of Guidance and Psychological Counseling and the Psychology Department.

The interview included five open-ended questions:

“How did the COVID-19 pandemic affect you psychologically?“

“How did the COVID-19 pandemic affect you socially?“

“How did the COVID-19 pandemic affect you physically?“

“What is the most intense feeling you feel in the COVID-19 pandemic?“

“What is the reason for choosing the colour … for what COVID-19 makes you feel?”

Data analysis

Quantitative data was analyzed using descriptive and inferential statistics with the support of the software IBM SPSS Statistics for Windows, Version 23.0. Spearman Correlation Analysis Test was used to investigate the relationship with continuous variables. Statistical significance was set at a p-value < 0.05.

Qualitative data was analyzed using the Metaphor Identification Procedure (MIP) with the support of MAXQDA 22 software. Below are the stages suggested by MIP:

Read the entire text to form a general understanding of the meaning.

The words in the text are determined.

a. It determines how the meaning of each word in the text applies to an entity, relationship, or attribute in context. The words that come before and after the determined word are taken into account.

Determine if each word has a more basic meaning out of context.

If the word has a more basic meaning in other contexts than the given context, the contextual meaning is compared with the basic meaning and it is examined whether it contradicts.

If yes, that word is marked as a metaphor [ 20 ]. At the beginning of the form, a written instruction containing explanations was provided to help students understand the concept of metaphor. Students were first asked to generate a colour metaphor to describe COVID-19, and then they were asked to explain in detail the reasons for their selected metaphors. In the initial stage, the metaphors were identified and selected. They were read in detail by a researcher to gain insight into the context in which the participants’ metaphors emerged. A total of 23 metaphors produced by the students were considered for evaluation. Based on the evaluation, metaphors that expressed common meanings and showed similarities were grouped together. After grouping, metaphors consisting of positive and negative colours related to the research topic were obtained. The explanatory texts obtained from the research, the generated metaphors, and the themes created by the researcher through analysis were validated and verified for reliability by obtaining expert opinions from two academics. The expert opinions were compared with the researcher’s analyses until consensus was reached.

The emotions expressed by the colours were grouped using the existing literature [ 25 , 26 , 27 , 28 , 29 ]. Dark colours were identified as black, gray, purple colours and bright colours were identified as white, orange, blue, and green. According to AL-Ayash et al. (2016) [ 30 ] and Hemphill (1995) [ 31 ] it was accepted that bright colours elicited mainly positive emotional associations, and dark colours elicited mainly negative emotional associations (Table  1 ).

Ethical considerations

Ethical approval was received from the Hasan Kalyoncu University Faculty of Health Sciences Non-Invasive Research Ethical Board, and permission was received from the universities where the study was conducted (Date:6 December 2021, Decision No:2021/036). All participants were provided with detailed oral and written information about the study. No risks were identified for participating in the study. All participants provided informed consent. All data collected was anonymous and treated confidential. This study was conducted in accordance with the provisions of the Declaration of Helsinki.

Quantitative results

The mean age of the students participating in the study ( n  = 323) was 21.41 ± 1.97 years, and 78% were female. Of the students, 6.2% ( n  = 20) had chronic diseases, and 68.7% lived with their families. It was observed that 15.8% had previously tested positive for COVID-19. Most (98.5%) participants were vaccinated against COVID-19, and 31.9% had a relative who died due to COVID-19 (Table  2 ).

When the answers given to the question of which colour the participants represent COVID-19 to were analyzed, it was determined that more than half of them chose red ( n  = 16, 51.4%), while 16.7% selected green, and 5.9% chose blue among the bright colours for COVID-19. Regarding the selected dark colours, 42 of the participants associated black colour (13%), 17 of them related gray (5.3%), and eight of them related purple (2.5%) with COVID-19 (Table  3 ).

There was no statistically significant relationship between the COVID-19 Fear Scale scores and the Positive Affect sub-dimension. However, it was observed that there was a moderate positive relationship between the scores obtained from the COVID-19 Fear Scale and the scores of the Negative Affect sub-dimension. As the fear of COVID-19 increased, so did the negative emotions of the students ( r  = 0.328, p  = 0.015) (Table  4 ).

Scores of the students in the Positive Affect sub-dimension were similar in terms of the colour group they chose (bright and dark colours) (t = 1.022, p  = 0.30). However, when the mean scores of the Negative Affect sub-dimension were examined, a statistically significant difference was found between the bright and dark-coloured student groups (t = 2.802, p  < 0.001). In addition, the COVID-19 Fear Scale mean scores of the students who preferred dark colours were higher than those who preferred bright colours (t = 2.514, p  = 0.01) (Table  5 ).

Qualitative results

Twenty-one students with age ranged 18–22 were interviewed (14 female). Eight students were in their 4th year, six were in the 3rd year, four were in their 2nd year, and three were in the 1st year. Nursing degree in turkey lasts four years.

Regarding the colours, six students identified COVID-19 with a dark colour (black, gray, and purple), and 15 did so using bright colours (white, orange, blue, and green) group. Nursing students’ perceptions about the COVID-19 process were examined in-depth using the colour metaphor and focusing on physical, psychological, and social factors. These themes were then classified into two positive effects and adverse effects within themselves (Fig.  1 ).

Nursing students’ perceptions of the Covid-19 process, concept map model

Physical factors

Nursing students participating in the study were physically affected at different levels by the COVID-19 pandemic process. The adverse effects of physical factors included changes in existing routines in meeting basic needs, such as excessive nutrition, sleep disturbance, and immobility, as well as new habits like increased digital exposure, disease development, and acquiring COVID-19. The sub-themes of sleep disturbance, immobility, and digital exposure were prominent among the adverse effects. According to the results, these adverse effects were mainly expressed by students who chose dark colours like black, gray, and purple.

“Of course , it caused many sleep irregularities; you play with the phone until 1–2 a.m. , and you get up around 11-noon” (P2 , chose black colour.)

“During the COVID period , we stayed at home during the curfew time. We worked at home , and I also had to study , and I spent the whole time sitting at a desk. My movements were languid. It was difficult to walk; a need to sleep was present all the time” (P17 , chose gray colour).

Nursing students who chose bright colours such as blue, yellow, orange, and white also mentioned the positive effects despite the negative ones related to their experiences in this process. Among the physical factors, the sub-themes of fresh air, nutrition, and sports were evaluated positively. P4, account illustrates the efforts made to adapt to the conditions “ I tried different sports that I could do at home”. “Exercises like Pilates and yoga were activities I could do in calm and quiet environments without going outside.”

Psychological factors

Nursing students were most affected psychologically by the COVID-19 pandemic. They reported negative emotions such as loss/death experiences, uncertainty and pessimism, sadness, and anxiety/fear. These adverse effects were more frequent in students who chose dark colours like black, gray, and purple.

“COVID-19 came down on us like a black cloud; that is , the whole world was affected by it. Black expressed fear for me , a pitch-black dark environment. It is something like death. When you close your eyes , everything is dark” (P1 , chose black colour) .

“I had a panic attack when the first death occurred.” (P12 , chose red colour) .

Another sub-theme was uncertainty about the future. Students expressed the uncertainty created by the pandemic as a negative feeling, particularly concerning the education process, gaining professional knowledge and experience, and its effects on their family and social lives.

“It was our exam week; that is , when I was studying for the exams , the schools being closed suddenly created a feeling of uncertainty in me. Moreover , I did not know what to do. Should I sit down and study? Were the schools going to re-open? Was this going to continue? I had a feeling of uncertainty a lot” (P18 , chose gray colour) .

“…Nursing is an applied profession and due to the pandemic , we couldn’t get clinical practice. Right now I am feeling a lot of uncertainty about my profession. For example , will I be able to get full satisfaction in my profession with the education I have received? For example , since I am currently in the 3rd grade , how much can I learn until the next year? How much can I improve myself? How much can I put into practice? I honestly don’t know” (P7 , chose red colour) .

In addition, P1, who stated that his family had difficult times after being diagnosed with COVID-19 and chose the black colour, referred to the concept of hopelessness accompanying uncertainty, saying:

“I felt like I was in such a vacuum. I felt this period would never end as if we would be constantly exposed to it , and I would continue to live this life continuously.”

Although the COVID-19 pandemic has had many adverse psychological effects on the lives of nursing students, it has also been observed that this process has given them a positive perspective and strengthened their spirituality, awareness of social support systems, and expressions of hope. Notably, these sub-themes are expressed mainly by students who chose bright colours such as white, blue, and orange.

“There was much negativity in us , but I always tried to look at it from the bright side. For example , my father is a workaholic and has a heart condition. Since COVID-19 prevented him from going to work , he had the opportunity to rest” (P5 , chose white colour).

Social factors

The data showed that the COVID-19 pandemic had a comprehensive social impact on nursing students, their families, and friends. The sub-themes of the factors that negatively affected nursing students socially included interpersonal relationships, isolation/constraints, and obstacles encountered in individual activities. Interpersonal relations were discussed broadly, including relations with family, relatives, and friends. Nursing students emphasized a decrease in interaction and sharing with their immediate environment due to measures to maintain physical and social distance and conflicts related to spending extended periods with family during isolation.

“Frankly , tension can be experienced for any reason. In other words , even if there was an event that would not be misunderstood , there was a problem arising from seeing each other all the time” (P16 , chose red colour.)

“I am a person who loves school very much. I loved the school environment , the friendly environment. Unfortunately , the school closed and constantly opened because of the virus. Being away from teachers and friends , being away from school affected me badly because I could not go to school” (P19 , chose purple colour).

Nursing students who chose dark colours like black, purple, and gray, as well as red, were more negatively affected in social aspects during the COVID-19 process. Individual activities were included in the negative factors because the constraints and isolation measures limited students’ ability to engage in social and academic activities, leading to feelings of frustration and loneliness. However, students also mentioned the benefits of academic studies, individual activities, family/friend relationships, and online personal and professional development trainings in the digital environment, which made this period instructive in many ways.

“I am 22 years old , but I spent more time with my family during this period. I think we had our first breakfast at that time. After the prohibitions , I became happier because I spent more time with my parents” (P3 , chose green).

Similarly, participant P16, who thought they had more opportunities for individual activities during the pandemic process and chose the red colour, said, “I took the time to read books. In addition , I watched many foreign series”.

This study showed that COVID-19 had significant physical, psychological, and social effects on nursing students. The results revealed that the negative emotions of nursing students increased as their COVID-19 fears increased, and the negative emotions and fears of COVID-19 were higher in students who described COVID-19 in dark colours. In studies examining colour-emotion associations, negative emotions and experiences are expressed by individuals in dark colours such as black and gray, while positive emotions are expressed in bright colours such as white, pink, and green [ 29 , 31 ]. Black has been associated with fear, sadness, and hatred [ 29 ], while gray has been related to depression, boredom, and disappointment [ 32 ]. During the pandemic, the most intense emotions experienced by the nursing students were fear, sadness, and anxiety [ 5 , 17 ]. In accordance with previous evidence [ 5 , 6 ], this study shows that nursing students’ negative emotions and fears related to COVID-19 were more present in students who defined COVID-19 in dark colours compared to those who chose bright colours.

The study identified fear, sadness, and anxiety as the predominant emotions experienced by nursing students during the COVID-19 pandemic. These emotions were particularly intense for those who associated the pandemic with dark colours. The findings align with previous studies that link dark colours with negative emotions, providing insight into the emotional state of nursing students during this period [ 5 , 17 ].

Those choosing dark colours were more deeply affected physically, socially, and psychologically in the in-depth interviews. This is an expected result considering the prolonged nature of the COVID-19 pandemic, the detailed knowledge nursing students have due to their vocational training, and the fact that the majority of them are young adults [ 7 , 33 , 34 ]. Clarke et al. (2008) stated that bright colours such as blue, green, and white induced low anxiety levels and had relaxing effects on individuals [ 27 ]. In this study, it is observed that nursing students who chose bright colours also had positive experiences despite the negativities faced during this process. Students who chose the colours white, blue, and green reported positive gains during the pandemic.

The study showed that nursing students used colour metaphors to vividly express their psychological, social, and physical experiences related to the COVID-19 pandemic. Dark colours were linked to negative feelings and experiences, whereas bright colours were associated with more positive experiences and outcomes. This use of colour metaphors provided a unique and expressive way for students to communicate their inner states and coping mechanisms.

Some nursing students described COVID-19 in dark colours due to its adverse effects on physical factors, such as nutrition, inactivity, and changes in sleep patterns during the pandemic. Similarly, studies indicate that nutrition and inactivity problems occur due to changes in the habits of university students, such as sleeping and waking up late and the deterioration of sleep quality [ 7 , 35 , 36 ]. The deterioration in meeting basic life needs means that individuals face the risk of degeneration in their health, which is a state of complete well-being in biopsychosocial terms.

The study also determined that the COVID-19 pandemic had adverse social effects, particularly affecting interpersonal relationships and restrictions, on nursing students who chose dark colours. Restrictions experienced during the pandemic made individuals feel at risk and vulnerable [ 13 ], reduced social activities with individuals and social groups, moved communication to the digital environment, and negatively affected interpersonal relationships [ 36 ]. However, WHO (2020) maintained that people are inherently social beings and need each other during such crises. They reported that alternative solutions, such as regular phone calls with family and friends and video conferences, would help bridge the gap created by social distance and restrictions [ 37 ]. Notably, students who stated that their family relations had strengthened and that they efficiently used this period for activities carried out individually and within the family environment associated COVID-19 with bright colours (green, white, orange).

The common themes that emerged from the metaphors used by nursing students included loss/death experiences, uncertainty, pessimism, sadness, and fear. These themes were particularly prevalent among students who described COVID-19 in dark colours. The lack of clear protocols regarding infection control and treatment procedures heightened feelings of fear and alarm caused by uncertainty. Social isolation and restrictions further intensified feelings of panic and anxiety, contributing to the negative emotions associated with dark colours [ 8 , 13 ].

During the COVID-19 pandemic, nursing students frequently expressed themes such as loss/death experiences, uncertainty, pessimism, sadness, and fear. The lack of a clear protocol regarding infection control and treatment procedures increased feelings of fear and alarm caused by uncertainty in individuals. Social isolation and restrictions also led individuals to experience panic by increasing their feelings of intolerance and anxiety towards the process [ 9 ]. As COVID-19 is a global health crisis threatening the entire world, this process is considered the most challenging form of psychological struggle [ 13 ]. This may have contributed to the negative emotions experienced by nursing students who associated COVID-19 mainly with the colours black, gray, and purple. Additionally, nursing students paired COVID-19 with red and associated this period with a sense of alarm. While some studies associate red with positive emotions [ 27 , 28 ], others relate red with anger, hatred, and alarm [ 29 ]. Jonauskaite et al. (2020) stated that individuals’ colour choices are related to universal associations and may differ according to language, culture, geography, and physical environmental conditions [ 29 ]. Therefore, although the pandemic process has positive and negative effects globally, it is considered that nursing students may have associated negative emotions such as danger, alarm status, and fear with the colour red.

The discussion highlights the complex and multifaceted impact of the COVID-19 pandemic on nursing students, emphasizing the need for supportive measures to address their physical, psychological, and social well-being.

Limitations

This study has some limitations that must be considered. First, this study was conducted only with students in the nursing department of a foundation university, and it is possible to obtain different results when a multicenter study is conducted. Since the researchers are faculty members/staff of the university, the students may have felt under pressure in their answers and interviews. Finally, this study was conducted when the pandemic subsided, cases decreased, and students started face-to-face education. Therefore, findings are limited to students’ recollections and expressions.

Conclusions

Nursing students expressed their fears, feelings, and experiences about the COVID-19 pandemic. Metaphors obtained through in-depth interviews have yielded crucial insights into nursing students’ implicit feelings regarding their pandemic experiences. It has been observed that nursing students associating COVID-19 with dark colours are more adversely affected physically, socially, and psychologically, whereas students associating it with bright colours may experience both positive and negative effects. Developing emotional support programs is essential to better understand nursing students’ pandemic experiences and enhance their emotional well-being. These programs should be tailored to meet the unique needs of students during such challenging times and foster a supportive environment.

Comprehensive research based on various colour metaphors should be undertaken to thoroughly investigate and understand different emotional responses and experiences among nursing students. This research will provide valuable insights into how colour associations may impact their coping mechanisms and emotional states. Encouraging interdisciplinary studies is necessary to gain a more holistic understanding of the pandemic’s effects on health sciences students. Collaborative research efforts can help identify commonalities and differences in experiences across disciplines, facilitating the development of targeted support strategies.

It is crucial to prepare emergency action plans for similar crisis periods post-pandemic. These plans should encompass various potential challenges and uncertainties that may arise, ensuring that institutions and individuals are better equipped to handle future crises. Continuous updates to these plans are imperative to adapt to changing circumstances and improve preparedness. By addressing these areas, we can better support nursing students and enhance their ability to cope with current and future challenges, ultimately fostering a more resilient and well-prepared healthcare workforce.

Availability of data and materials

The data that support the findings of this study are available from the authors, but access to these data is restricted to protect the personal information of the participants.

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Department of Nursing, Faculty of Health Sciences, Hasan Kalyoncu University, Gaziantep, Turkey

Eda Atay, Ezgi Dirgar, Kadiriye Pehlivan, Betül Tosun, Ahmet Ayaz, Ayla Yava & Juan M. Leyva-Moral

Department of Midwifery, Faculty of Health Sciences, Gaziantep University, Gaziantep, Turkey

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Faculty of Nursing, Hacettepe University, Ankara, Turkey

Betül Tosun

Department of Guidance and Psychological Counseling, Faculty of Education, Hasan Kalyoncu University, Gaziantep, Turkey

Department of Nursing, Faculty of Medicine, Universitat Autònoma de Barcelona, Coordinator of the Vulnerability and Health Nursing Research Group (GRIVIS-UAB), Barcelona, Spain

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EA: Conceptualization, Methodology, Formal analysis, Writing - Original Draft, Visualization. ED: Conceptualization, Data curation, Writing- Original draft preparation. KP: Writing - Original Draft, Visualization, Investigation. BT: Methodology, Formal analysis, Writing - Original Draft, Visualization Supervision.AY: Formal analysis, Writing - Original Draft, Visualization. AY: Reviewing and Editing, Visualization, Supervision.JML: Reviewing and Editing, Visualization, Supervision.

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Correspondence to Betül Tosun , Ahmet Ayaz , Ayla Yava or Juan M. Leyva-Moral .

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This study was in accordance with the Declaration of Helsinki and approved by Hasan Kalyoncu University Health Sciences Non-Invasive Research Ethics Committee. Written informed consent was obtained after all participants understood the content of the tool and were informed about the research objectives. In addition, the participants explained the procedures on how to keep their records confidential, such as not revealing the identity of the participants, deleting the records and destroying the transcripts six months after the analysis, and ensuring the security of the devices containing the recording files through passwords and encryption. There is no conflict of interest with any person or organization in the study. This research has not received any specific grants from any funding organizations, commercial or non-profit sectors.

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Atay, E., Dirgar, E., Pehlivan, K. et al. Emotions reflected in colours: experiences of nursing students during the COVID-19 period. BMC Nurs 23 , 575 (2024). https://doi.org/10.1186/s12912-024-02256-6

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Received : 30 May 2024

Accepted : 12 August 2024

Published : 19 August 2024

DOI : https://doi.org/10.1186/s12912-024-02256-6

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