synthesis literature def

Animal testing is necessary to save human lives. Incidents have happened where humans have died or have been seriously harmed for using drugs that had not been tested on animals (Smith 2008).   

Animals feel pain in a way that is physiologically and neuroanatomically similar to humans (Chowdhury 2012).   

Animal testing is not always used to assess the toxicology of a drug; sometimes painful experiments are undertaken to improve the effectiveness of cosmetics (Turner 2015) 

Animals in distress can suffer psychologically, showing symptoms of depression and anxiety (Panatta and Hudson 2016). 

Whenever you report to a friend the things several other friends have said about a film or CD you engage in synthesis.  People synthesize information naturally to help other see the connections between things they learn;  for example, you have probably stored up a mental data bank of the various things you've heard about particular professors.  If your data bank contains several negative comments, you might synthesize that information and use it to help you decide not to take a class from that particular professor.  Synthesis is related to but not the same as classification, division, or comparison and contrast.  Instead of attending to categories or finding similarities and differences, synthesizing sources is a matter of pulling them together into some kind of harmony.  Synthesis searches for links between materials for the purpose of constructing a thesis or theory.

The basic research report (described below as a background synthesis) is very common in the business world.  Whether one is proposing to open a new store or expand a product line, the report that must inevitably be written will synthesize information and arrange it by topic rather than by source.  Whether you want to present information on child rearing to a new mother, or details about your town to a new resident, you'll find yourself synthesizing too. And just as in college, the quality and usefulness of your synthesis will depend on your accuracy and organization.

In the process of writing his or her background synthesis, the student explored the sources in a new way and become an expert on the topic.  Only when one has reached this degree of expertise is one ready to formulate a thesis.  Frequently writers of background synthesis papers develop a thesis before they have finished.  In the previous example, the student might notice that no two colleges seem to agree on what constitutes "co-curricular," and decide to research this question in more depth, perhaps examining trends in higher education and offering an argument about what this newest trend seems to reveal.  [ More information on developing a research thesis .] 

On the other hand, all research papers are also synthesis papers in that they combine the information you have found in ways that help readers to see that information and the topic in question in a new way.  A research paper with a weak thesis (such as: "media images of women help to shape women's sense of how they should look") will organize its findings to show how this is so without having to spend much time discussing other arguments (in this case, other things that also help to shape women's sense of how they should look).  A paper with a strong thesis (such as "the media is the single most important factor in shaping women's sense of how they should look") will spend more time discussing arguments that it rejects (in this case, each paragraph will show how the media is more influential than other factors in that particular aspect of women's sense of how they should look"). 

[See also thesis-driven research papers .] [See also " Preparing to Write the Synthesis Essay ," " Writing the Synthesis Essa y ," and " Revision . "]

Because each discipline has specific rules and expectations, you should consult your professor or a guide book for that specific discipline if you are asked to write a review of the literature and aren't sure how to do it.

[See also " Preparing to Write the Synthesis Essay ," " Writing the Synthesis Essa y," and " Revision ."]

Regardless of whether you are synthesizing information from prose sources, from laboratory data, or from tables and graphs, your preparation for the synthesis will very likely involve comparison . It may involve analysis , as well, along with classification, and division as you work on your organization. 

Sometimes the wording of your assignment will direct you to what sorts of themes or traits you should look for in your synthesis.  At other times, though, you may be assigned two or more sources and told to synthesize them.  In such cases you need to formulate your own purpose, and develop your own perspectives and interpretations.  A systematic preliminary comparison will help. Begin by summarizing briefly the points, themes, or traits that the texts have in common (you might find summary-outline notes useful here).  Explore different ways to organize the information depending on what you find or what you want to demonstrate ( see above ).  You might find it helpful to make several different outlines or plans before you decide which to use.  As the most important aspect of a synthesis is its organization, you can't spend too long on this aspect of your paper!

A synthesis essay should be organized so that others can understand the sources and evaluate your comprehension of them and their presentation of specific data, themes, etc. The following format works well: 

The introduction (usually one paragraph)     1.    Contains a one-sentence statement that sums up the focus of your synthesis.     2.    Also introduces the texts to be synthesized:             (i)  Gives the title of each source (following the citation guidelines of whatever style                     sheet you are using);               (ii)  Provides the name of each author;             (ii)  Sometimes also provides pertinent background information about the authors,                    about the texts to be summarized, or about the general topic from which the                    texts are drawn. 

The body of a synthesis essay: This should be organized by theme, point, similarity, or aspect of the topic.  Your organization will be determined by the assignment or by the patterns you see in the material you are synthesizing.  The organization is the most important part of a synthesis, so try out more than one format.     Be sure that each paragraph :     1.     Begins with a sentence or phrase that informs readers of the topic of the paragraph;     2.     Includes information from more than one source;     3.     Clearly indicates which material comes from which source using lead in phrases and             in-text citations.  [Beware of plagiarism:  Accidental plagiarism most often occurs             when students are synthesizing sources and do not indicate where the synthesis              ends and their own comments begin or vice verse.]     4.    Shows the similarities or differences between the different sources in ways that make             the paper as informative as possible;     5.    Represents the texts fairly--even if that seems to weaken the paper! Look upon             yourself as a synthesizing machine;  you are simply repeating what the source says,             in fewer words and in your own words.  But the fact that you are using your own              words does not mean that you are in anyway changing what the source says. 

Conclusion. When you have finished your paper, write a conclusion reminding readers of the most significant themes you have found and the ways they connect to the overall topic.  You may also want to suggest further research or comment on things that it was not possible for you to discuss in the paper. If you are writing a background synthesis, in some cases it may be appropriate for you to offer an interpretation of the material or take a position (thesis).  Check this option with your instructor before you write the final draft of your paper. 

• What do you like best about your peer's synthesis? (Why? How might he or she do more of it?);
• Is it clear what is being synthesized? (i.e.: Did your peer list the source(s), and cite it/them correctly?);
• Is it always clear which source your peer is talking about at any given moment? (Mark any places where it is not clear);
• Is the thesis of each original text clear in the synthesis? (Write out what you think each thesis is);
• If you have read the same sources,
• did you identify the same theses as your peer? (If not, how do they differ?);
• did your peer miss any key points from his or her synthesis? (If so, what are they?);
• did your peer include any of his own opinions in his or her synthesis? (If so, what are they?);
• Where there any points in the synthesis where you were lost because a transition was missing or material seems to have been omitted?  (If so, where and how might it be fixed?);
• What is the organizational structure of the synthesis essay? (It might help to draw a plan/diagram);
• Does this structure work?  (If not, how might your peer revise it?);
• How is each paragraph structured?  (It might help to draw a plan/diagram);
• Is this method effective?  (If not, how should your peer revise?);
• Was there a mechanical, grammatical, or spelling error that annoyed you as you read the paper?  (If so, how could the author fix it?  Did you notice this error occurring more than once?)  Do not comment on every typographical or other error you see.  It is a waste of time to carefully edit a paper before it is revised!
• What other advice do you have for the author of this paper?

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Synthesizing Sources

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When you look for areas where your sources agree or disagree and try to draw broader conclusions about your topic based on what your sources say, you are engaging in synthesis. Writing a research paper usually requires synthesizing the available sources in order to provide new insight or a different perspective into your particular topic (as opposed to simply restating what each individual source says about your research topic).

Note that synthesizing is not the same as summarizing.  

• A summary restates the information in one or more sources without providing new insight or reaching new conclusions.
• A synthesis draws on multiple sources to reach a broader conclusion.

There are two types of syntheses: explanatory syntheses and argumentative syntheses . Explanatory syntheses seek to bring sources together to explain a perspective and the reasoning behind it. Argumentative syntheses seek to bring sources together to make an argument. Both types of synthesis involve looking for relationships between sources and drawing conclusions.

In order to successfully synthesize your sources, you might begin by grouping your sources by topic and looking for connections. For example, if you were researching the pros and cons of encouraging healthy eating in children, you would want to separate your sources to find which ones agree with each other and which ones disagree.

After you have a good idea of what your sources are saying, you want to construct your body paragraphs in a way that acknowledges different sources and highlights where you can draw new conclusions.

As you continue synthesizing, here are a few points to remember:

• Don’t force a relationship between sources if there isn’t one. Not all of your sources have to complement one another.
• Do your best to highlight the relationships between sources in very clear ways.
• Don’t ignore any outliers in your research. It’s important to take note of every perspective (even those that disagree with your broader conclusions).

Example Syntheses

Below are two examples of synthesis: one where synthesis is NOT utilized well, and one where it is.

Parents are always trying to find ways to encourage healthy eating in their children. Elena Pearl Ben-Joseph, a doctor and writer for KidsHealth , encourages parents to be role models for their children by not dieting or vocalizing concerns about their body image. The first popular diet began in 1863. William Banting named it the “Banting” diet after himself, and it consisted of eating fruits, vegetables, meat, and dry wine. Despite the fact that dieting has been around for over a hundred and fifty years, parents should not diet because it hinders children’s understanding of healthy eating.

In this sample paragraph, the paragraph begins with one idea then drastically shifts to another. Rather than comparing the sources, the author simply describes their content. This leads the paragraph to veer in an different direction at the end, and it prevents the paragraph from expressing any strong arguments or conclusions.

An example of a stronger synthesis can be found below.

Parents are always trying to find ways to encourage healthy eating in their children. Different scientists and educators have different strategies for promoting a well-rounded diet while still encouraging body positivity in children. David R. Just and Joseph Price suggest in their article “Using Incentives to Encourage Healthy Eating in Children” that children are more likely to eat fruits and vegetables if they are given a reward (855-856). Similarly, Elena Pearl Ben-Joseph, a doctor and writer for Kids Health , encourages parents to be role models for their children. She states that “parents who are always dieting or complaining about their bodies may foster these same negative feelings in their kids. Try to keep a positive approach about food” (Ben-Joseph). Martha J. Nepper and Weiwen Chai support Ben-Joseph’s suggestions in their article “Parents’ Barriers and Strategies to Promote Healthy Eating among School-age Children.” Nepper and Chai note, “Parents felt that patience, consistency, educating themselves on proper nutrition, and having more healthy foods available in the home were important strategies when developing healthy eating habits for their children.” By following some of these ideas, parents can help their children develop healthy eating habits while still maintaining body positivity.

In this example, the author puts different sources in conversation with one another. Rather than simply describing the content of the sources in order, the author uses transitions (like "similarly") and makes the relationship between the sources evident.

Reading & Writing Purposes

Reading & writing to synthesize, synthesis defined.

The concept of a “coherent whole” is essential to synthesis. When you synthesize in writing, you examine different types of information (ideas, examples, statistics, etc., from different sources) and different themes (perspectives and concepts) from different sources with the purpose of blending them together to help explain one main idea. So you have to look for relationships 1) among the sources’ themes and 2) between these themes and your own ideas in order to blend all of the pieces to make a coherent whole.

The concept of a “coherent whole” is important in terms of language, too. Once you examine content and choose the parts to synthesize, you need to express those parts in your own language in order to create a coherent whole in terms of writing style.

Synthesis is like combining different ingredients to make a stew. If you choose and combine carefully, with the end result (supporting your main idea) in mind, the ingredients will be both separate and well-blended, with all ingredients contributing as they should to the final taste.

View the following video for a basic definition of, and introduction to, the concept of synthesis.

As stated in the video, synthesis means combining similar information to create something new.  Reading and writing to synthesize means that you read information from many sources relating to a particular topic, question, insight, or assertion.  You extract appropriate pieces of information from each source, information that relates to your insight in some way (supporting it, negating it, offering additional detail).  You react to those pieces of information and relate them to your insight, to create something new–your own reasoned argument.

One standard example of reading and writing to synthesize is a research paper–a basic assignment in many college courses.  Skills that you develop in researching and synthesizing information also transfer to writing a business report or proposal. When you research a topic, you find information from many different sources which informs your personal thoughts and assertion about that topic.  However, reading and writing to synthesize involves more than just finding information and inserting it into an essay, report, or proposal.  You use the information you find to help create and support your own, unique thoughts.

Example of Synthesis in an Academic Setting

A research paper is the classic example of synthesis in an academic setting. You may be assigned to write a research paper in a sociology course, for example.  You may have read a number of selections dealing with different cultures, and the assignment asks you to synthesize information from these articles along with information from at least four other sources, to support your unique thesis. You start with a main idea in order to start the synthesis. You might create the following main idea: People within a culture have to both assimilate and adapt to their cultural and physical environments in order to thrive . You then might combine appropriate parts from different reading selections:

• definition and examples of assimilation from a chapter in a sociology textbook
• examples from researched articles on assimilation, cultural adaptation
• examples from interviews with people who have assimilated

Your research paper would blend themes from all of these sources to support your original insight and assertion (thesis) about assimilation and adaptation.

Example of  Synthesis in a Business Setting

After receiving more than ten different requests for flex time over the last year, you’ve decided that it makes sense to institute flexible hours for the fifty workers you supervise in your department.  You research a number of other businesses and examples of companies moving to flex time, quote or summarize those, and put them all into a document, which you send to your boss.  Your request is denied, because while your boss understood that flex time worked in other companies, she could not relate your research to the actual situation in your department. If you had offered your own analysis of how different companies’ strategies would benefit your own department, the outcome might have been different and your proposal approved.  It’s worth saying again: it’s important to blend the information you find with your own purpose, whether that is a proposal at work or an essay designed to offer your own, unique thoughts, supported by research.

Reading to Synthesize

Synthesis builds upon analysis.  You need to be able to read and analyze the quality of a text in order to decide whether you want to bring that text into the conversation. However, reading to synthesize moves in almost an opposite direction from analysis.  As you analyze, you break the text down into its parts in order to evaluate the text.  Analysis is like taking a puzzle apart and examining each piece, or analyzing a cake to find out what the ingredients are and how they work together.

On the other hand, as you read with the purpose of synthesizing, you search for thoughts about the same focused topic, thoughts which can be similar or different, in order to get a picture of the whole ongoing conversation about that topic. Then you decide if you agree or disagree with those thoughts–you join the conversation or discourse. Synthesis is like examining puzzle pieces with the purpose of putting the whole picture together, or baking a cake with ingredients that complement each other.

The process of reading to synthesize, in itself, blends or synthesizes many reading skills, which may include the following:

• skimming texts
• preview questions and answers
• reading for main idea (which may involve annotating, note taking, and more)
• summarizing
• analyzing the quality of the texts
• applying chosen texts to your insight
• reacting to the ideas in the texts

The main thing to remember as you read with the purpose of synthesizing is that your task is to find relationships among ideas.  Reading to synthesize does not merely consist of finding appropriate quotations and plugging them into an essay; instead, ideas from multiple texts need to be considered thoughtfully and linked with your own insights, reactions, and commentary.

Use an Idea Matrix to Synthesize Ideas

A idea matrix supports reading to synthesize, especially if you are reading multiple texts about a topic.  An idea matrix is a table that helps you identify and organize ideas from those texts according to their themes. It allows you to compare and contrast different insights about those themes. An idea matrix is a useful graphic, since one source may include ideas about many different themes.

Here’s one example of an idea matrix which synthesizes information from multiple sources around a specific focus.

Focus of Reading: Lessons Learned from the 1918 flu pandemic

To create an idea matrix, identify a topic around which your texts converge and state it clearly above the matrix. When you identify this focus, make sure it’s not too broad (e.g., pandemics – you’d have thousands of texts to read) or too narrow (e.g., number of U.S. deaths from the 1918 flu – you’d just need to consult one valid text for that information). The focus should be something that is part of a conversation happening among a manageable number of texts (e.g., lessons learned from the 1918 flu ).

The text column lists each source’s exact name.  It should also include the author, publication information for an eventual bibliography, url, and any other important identifying information.

The themes emerge from the sources you’ve read. You may choose to note them as paraphrases, summaries, and/or quotations.

Link to additional examples of idea matrices about different themes:

• Anxiety in Graduate Students from Ashford University’s Writing Center
• Thesis that makes an assertion about Democratic and Coaching Styles of Leadership

An idea matrix for reading can help you synthesize information from many texts, identify idea relationships within that information, and eventually help you formulate your own thoughts to add to the conversation.

Writing to Synthesize

Writing to synthesize involves taking those related ideas that you’ve extracted from multiple texts and incorporating them into a research paper, report, or proposal that’s structured around your own main insight, assertion, or thesis.

Don’t Do This:

In writing a document that synthesizes ideas from multiple texts, it’s the impulse of many students to summarize or paraphrase a paragraph or a whole article, insert the summary, and then move on to the next text and summary.  That’s not good practice, since it doesn’t link ideas in terms of their themes, and doesn’t focus on how those themes relate to your own ideas.

Instead , Do This:

Work from the idea matrix you built as you read different texts.  For a college research paper, turn your topic or focus statement at the top of the matrix into a thesis sentence , a sentence that makes an assertion or provides an insight offering your own informed views on the topic.  Offering your own perspective is key.  You’ll then structure the body of your essay using the groups of supporting ideas/themes you noted in the idea matrix, in whatever order you choose.  Each group gets its own topic sentence and unit of support.  And each group of supporting ideas includes your own thoughts, applications, and reactions to the texts included in that group.  One general rule is that you always structure “writing to synthesize” around your own ideas, and that you always offer your own ideas about information from each text used – that’s your contribution to the conversation.

The following video offers a clear discussion and examples to reinforce the concept of writing to synthesize.

This video about writing to synthesize researched sources incorporates information about using an idea matrix:

They Say/I Say Approach to Synthesis

Another way to think of synthesis is as though you’re joining a conversation; you’re listening to (reading) different texts, and bringing your own insight and experience to that conversation.  One good way of understanding synthesis in terms of reading and writing is to consider the “They Say/I Say” format created by Gerald Graff and Cathy Birkenstein, which helps you synthesize your own ideas with the text’s. The following video, although somewhat lengthy, provides a summary of Graff’s and Birkenstein’s text.

The video below explains how to write a synthesis applying the They Say/I Say framework. There’s a useful extended example showing how a writer incorporated appropriate pieces of different texts into an essay. (Note – don’t get too caught up in the MLA/APA format details at this point – focus on the concept of synthesis and how to synthesize texts in an essay.)

Summary: Reading & Writing to Synthesize

• Synthesis means that you’re coordinating different pieces (themes, ideas, types of information) to create a coherent and new whole.
• All of the pieces you synthesize in a piece of writing for college need to focus around your own insight/assertion/ thesis.
• Often, a research essay assignment will expect that you synthesize information to address and offer your unique insight about a debatable issue.
• Synthesis itself involves blending many reading and thinking skills, such as skimming, annotating, summarizing, and analyzing, among others.
• There are different approaches to synthesis that may help you read and write about multiple texts.  They Say/I Say helps you blend your own ideas with ideas in other texts.  An Idea Matrix helps you organize ideas from multiple texts around the focus of your own main idea.
• Reading & Writing to Synthesize. Authored by : Susan Oaks. Project : Introduction to College Reading & Writing. License : CC BY-NC: Attribution-NonCommercial
• video Synthesizing Information. Provided by : GCFLearnFree.org. Located at : https://www.youtube.com/watch?v=7dEGoJdb6O0 . License : Other . License Terms : YouTube video
• video They Say/I Say: The Moves that Matter in Academic Writing. Authored by : jilljitsu81. Located at : https://www.youtube.com/watch?v=2s8SWS-SZDw . License : Other . License Terms : YouTube video
• image of man's face on a puzzle, with one piece askew. Authored by : Richard Reid. Provided by : Pixabay. Located at : https://pixabay.com/photos/puzzle-jigsaw-jigsaw-puzzle-1487340/ . License : CC0: No Rights Reserved
• video Strategies for Synthesis. Authored by : Mary Lourdes Silva. Located at : https://www.youtube.com/watch?v=c7HtCHtQ9w0 . License : Other . License Terms : YouTube video
• video Synthesis: Definition and Examples. Provided by : WUWritingCenter. Located at : https://www.youtube.com/watch?v=sLhkalJe7Zc . License : Other . License Terms : YouTube video
• video Research Synthesis. Provided by : USU Libraries - Utah State University. Located at : https://www.youtube.com/watch?v=ObK6J7vGnw8 . License : Other . License Terms : YouTube video
• image of woman with laptop and pie chart showing relationship of parts to whole. Authored by : Tumisu. Provided by : p. Located at : https://pixabay.com/photos/analytics-charts-business-woman-3265840/ . License : CC0: No Rights Reserved

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A Guide to Evidence Synthesis: What is Evidence Synthesis?

• Meet Our Team
• Our Published Reviews and Protocols
• What is Evidence Synthesis?
• Types of Evidence Synthesis
• Evidence Synthesis Across Disciplines
• Finding and Appraising Existing Systematic Reviews
• 0. Develop a Protocol
• 1. Draft your Research Question
• 2. Select Databases
• 3. Select Grey Literature Sources
• 4. Write a Search Strategy
• 5. Register a Protocol
• 6. Translate Search Strategies
• 7. Citation Management
• 8. Article Screening
• 9. Risk of Bias Assessment
• 10. Data Extraction
• 11. Synthesize, Map, or Describe the Results
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What are Evidence Syntheses?

What are evidence syntheses.

According to the Royal Society, 'evidence synthesis' refers to the process of bringing together information from a range of sources and disciplines to inform debates and decisions on specific issues. They generally include a methodical and comprehensive literature synthesis focused on a well-formulated research question.  Their aim is to identify and synthesize all  of the scholarly research on a particular topic, including both published and unpublished studies. Evidence syntheses are conducted in an unbiased, reproducible way to provide evidence for practice and policy-making, as well as to identify gaps in the research. Evidence syntheses may also include a meta-analysis, a more quantitative process of synthesizing and visualizing data retrieved from various studies. 

Evidence syntheses are much more time-intensive than traditional literature reviews and require a multi-person research team. See this PredicTER tool to get a sense of a systematic review timeline (one type of evidence synthesis). Before embarking on an evidence synthesis, it's important to clearly identify your reasons for conducting one. For a list of types of evidence synthesis projects, see the next tab.

How Does a Traditional Literature Review Differ From an Evidence Synthesis?

How does a systematic review differ from a traditional literature review.

One commonly used form of evidence synthesis is a systematic review.  This table compares a traditional literature review with a systematic review.

Video: Reproducibility and transparent methods (Video 3:25)

Reporting Standards

There are some reporting standards for evidence syntheses. These can serve as guidelines for protocol and manuscript preparation and journals may require that these standards are followed for the review type that is being employed (e.g. systematic review, scoping review, etc). ​

• PRISMA checklist Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses.
• PRISMA-P Standards An updated version of the original PRISMA standards for protocol development.
• PRISMA - ScR Reporting guidelines for scoping reviews and evidence maps
• PRISMA-IPD Standards Extension of the original PRISMA standards for systematic reviews and meta-analyses of individual participant data.
• EQUATOR Network The EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network is an international initiative that seeks to improve the reliability and value of published health research literature by promoting transparent and accurate reporting and wider use of robust reporting guidelines. They provide a list of various standards for reporting in systematic reviews.

Video: Guidelines and reporting standards

PRISMA Flow Diagram

The  PRISMA  flow diagram depicts the flow of information through the different phases of an evidence synthesis. It maps the search (number of records identified), screening (number of records included and excluded), and selection (reasons for exclusion).  Many evidence syntheses include a PRISMA flow diagram in the published manuscript.

See below for resources to help you generate your own PRISMA flow diagram.

• PRISMA Flow Diagram Tool
• PRISMA Flow Diagram Word Template
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• Last Updated: Sep 3, 2024 4:30 PM
• URL: https://guides.library.cornell.edu/evidence-synthesis

• Open access
• Published: 11 September 2024

Too stressed to think? A scoping review of the literature for healthcare educators utilising high acuity clinical scenarios

• Jason Betson   ORCID: orcid.org/0000-0002-9883-8586 1 , 2 ,
• Erich C. Fein   ORCID: orcid.org/0000-0003-4119-0130 4 ,
• David Long   ORCID: orcid.org/0000-0002-6278-7377 2 &
• Peter Horrocks   ORCID: orcid.org/0000-0002-0220-175X 3  

BMC Medical Education volume  24 , Article number:  990 ( 2024 ) Cite this article

Metrics details

The practise of paramedicine can be highly stressful particularly where urgent lifesaving decisions need to be made. Traditionally, educators have adopted the approach of placing students in simulated stressful situations as a way of learning to cope with these challenges. It is unclear from the literature whether traditional stress inoculation enhances or hinders learning. This scoping review aims to identify and examine both the peer-reviewed and grey literature reporting physiological stress responses to high-acuity scenarios in paramedicine and cognate healthcare disciplines.

Adhering strictly to JBI Evidence Synthesis Manual for conducting a scoping review, medical subject headings and areas, keywords and all other possible index terms were searched across EBSCOhost (Medline, CINAHL and APA PsycInfo), Scopus and, PubMed. English language articles both published (peer-reviewed academic papers, reports and conference proceedings) and unpublished (grey literature, Google Scholar reports) were included, and publications citing retrieved articles were also checked.

Searches performed across five electronic databases identified 52 articles where abstracts indicated potential inclusion. From this, 22 articles which reported physiological or psychophysiological responses to stressful scenario-based education were included.

This review identified that an acceptable level of stress during simulation can be beneficial, however a point can be exceeded where stress becomes a hinderance to learning resulting in underperformance. By identifying strategies to moderate the impact of acute stress, educators of paramedic and other healthcare students can utilise high-acuity clinical scenarios to their andragogical armamentarium which has the potential to improve real-world clinical outcomes.

Peer Review reports

Introduction

In many high-income countries, paramedic education has progressively moved from a post-employment vocational training model of the latter part of the 20th century to now sit firmly within the pre-employment tertiary education sector [ 1 ]. This evolution of education enables paramedics to provide high-level emergency care as new-to-practice clinicians in high pressure, time-critical environments [ 2 ]. To do this, education providers often utilise high-fidelity simulations to apply clinical or other skills in realistic environments. These simulations, are often comprised of high-acuity scenarios which are designed to depict a high severity of illness or injury [ 3 ] requiring rapid medical interventions, which can invoke increased physiological and cognitive stress. It may be the case that if these simulations are too stressful, clinical learnings from them may be lost due to the high stress load the participant is exposed to as shown by Takahashi, et al. [ 4 ] who identified higher cortisol levels post stress exposure in university students, which correlated with an increased level of memory impairment and poorer performance.

Links between physiological stress and knowledge application have also been reported in the paramedicine sector. LeBlanc, et al. [ 5 ] demonstrated that clinicians made more drug calculation errors following exposure to stressful events, whilst senior paramedics exhibited clinical and documentational vulnerabilities during high-acuity scenarios [ 6 ]. In the emerging field of undergraduate paramedicine education research, few studies have explored high-acuity scenario-based education and any associated physiological and cognitive stress. This is in contrast to Harvey, et al. [ 7 ], LeBlanc, et al. [ 6 ] and, more recently Hase, et al. [ 8 ] who have recommended that training in high-acuity areas of medicine should include challenge-promoting interventions specifically relevant to stress mitigation.

In the expanding cohort of university-trained paramedicine students, research on empathy [ 9 ], prevention of mental health and psychological disorders [ 10 ], workplace violence [ 11 ], physical characteristics [ 12 ] and pre-employment fitness testing [ 13 ] have been published. However, linkages between time critical high-acuity scenario-based education with resultant physiological stress and its potential impacts on cognitive decision-making has not been studied. A recent systematic review explored the physiological responses to acute stress in workers of several occupations, mostly within the human service industry [ 14 ]. Whilst this paper draws appropriate conclusions about acute physiological changes leading to performance decrement, possible implications for frontline healthcare workers were limited by a small number of healthcare-based studies included within the review. A gap also exists between self-awareness of one’s own physiological stress and how this may impact clinical judgement. Therefore, the purpose of this review was to better understand the physiological and cognitive stress responses observed in the participants undertaking high-acuity clinical scenarios. By appreciating the existence of contributory factors and how they influence stress, educators of paramedics and other healthcare workers can determine which elements of physiologically and mentally stressful scenario-based education can be considered in the design of their own programs.

Study design

Full systematic literature reviews (SLRs) are generally considered to be the foundation for evidence-based practice, particularly in healthcare [ 15 ]. This form of evidence synthesis relies on an extensive base of published literature and is frequently used to validate or refute current practice [ 16 ]. However, within the scope of the present study, little extant research reports on physiological changes triggered by high stress learning situations or the consequences this effect has on clinical performance. Given the inter-relationship between acute stress and the degradation of cognitive decision-making ability [ 7 , 17 ], further research is warranted to characterise this physiological response in undergraduate paramedicine students. In this paper, we employed a scoping review method to explore the extent of published and unpublished literature from cognate heath disciplines to identify key characteristics or factors related to our topic of interest.

Our final protocol was registered on the 21st March 2023, and is publicly available on the Open Science Framework platform ( https://osf.io/dxchy/ ).

Identifying the research question

This scoping review aims to identify and map the scope of current published literature related to physiological stress responses to high-acuity scenarios and, importantly, identify and analyse the knowledge gaps [ 18 ]. To achieve the aim, the following search strategy was employed:

Participants: higher education students or students in non-university training programs studying towards a recognised healthcare qualification.

Concept: any study that incorporates clinical scenarios / simulations where physiological (cardiovascular or endocrine) /or psychophysiological data is recorded.

Context: any undergraduate or postgraduate higher education setting or equivalent non-university training facility for the participants mentioned above.

Search strategy and eligibility criteria

The latest version [ 19 ] of Joanna Briggs Institute (JBI) comprehensive guide for authors conducting a scoping reviews [ 20 ] has been followed step-by-step within this review. A search period restriction from 2000 onwards was applied due to the rapid expansion of wearable technology including augmented and virtual reality. To ensure the review examined the acute physiological stress response, it was necessary to focus on articles that assessed markers of stress in real-time as participants were exposed to a stress-inducing task. An initial search was conducted across three prominent databases (Medline, PubMed and Scopus) to determine key terms as a guide to developing a thorough search strategy. From this and with the assistance of a senior research librarian, the secondary search expanded all identified keywords and incorporated medical subject headings (MeSH), major subject areas, and all other possible index terms as noted in the Appendix 1 . The protocol incorporated both published (peer-reviewed academic papers, reports and conference proceedings) and unpublished (incorporating theses and dissertations, research and technical reports) evidence but did exclude non-English language articles. Sources were gathered using EBSCOhost (including Medline, CINAHL and APA PsycInfo) Scopus, and PubMed. Google Scholar was also searched as there is a small body of evidence that suggests this search engine produces highly comprehensive results [ 21 , 22 ] whilst also searching ‘grey literature’ (published informally, non-commercially or remains unpublished), a format neglected by other databases. Selection of papers for inclusion in the study were then undertaken independently by two members of the research team (DL and EF). Finally, any other articles that cited the retrieved articles were also checked using citation alert with the ISI Web of Knowledge (Appendix 1 ).

Extracting and charting the data

Data were extracted from the included studies by two reviewers (JB and PH) utilising the JBI template of evidence details, characteristics and results extraction instrument [ 19 ]. Initial piloting of the data extraction resulted in some additional data being sought from each publication to allow quality appraisal to occur. This refined data extraction gathered details about study year, study country, study aim, study setting, study design, interventions, and comparators. Additionally, the data included sample size, methods, results, and author recommendations. A third member of the review team (DL) performed an accuracy check.

Quality assessment

Methodological validity and risk-of-bias appraisal, undertaken concurrently with data charting, was performed via the Mixed Methods Appraisal Tool (MMAT) version 2018 critical appraisal instrument designed by Hong, et al. [ 23 ]. For the purpose of this scoping review, an overall score was calculated from mean values of each section to determine methodological quality of each reviewed study (Appendix 4 ). The authors agreed that no cut-off scores would be applicable as the use of the MMAT was not for inclusion or exclusion purpose, but rather to describe the quality of the of publications reported in this review.

Synthesis of results

The first author performed narrative synthesis of identified themes and discussed these with the review team for validation. Descriptive results are subsequently reported which align with the intended scope and objectives of this review.

Ethics statement

Ethical approval was not required for this scoping review.

The search strategy yielded 1427 results, of which 52 remained after title and abstract proofing and duplicate removal (Level 1). Consensus was not reached on seven papers with resolution sought from a third member of the review team (PH) (Appendix 2 ). Of the 52 studies, 30 were excluded for reasons outlined in Appendix 3 . In addition, reference lists of three excluded review articles were checked, although nil additional suitable articles were identified. Unpublished (grey) literature was also assessed with no additional studies deemed suitable for inclusion. Figure  1 illustrates a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flow diagram [ 24 ] of the process and Table  1 lists the 22 studies deemed eligible for inclusion.

PRISMA flow diagram findings

From the twenty-two studies identified that met the inclusion criteria, the majority originated from Europe and North America. Only one study [ 25 ] involved paramedics or paramedicine students. Twelve studies involved medical or surgical trainees [ 7 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], five studied nursing or nurse anaesthetist students [ 37 , 38 , 39 , 40 , 41 ], three studies involved physiotherapy students [ 42 , 43 , 44 ], and one study examined psychology students [ 45 ].

No studies were identified that warranted exclusion based on major methodological flaws on any significant risk of bias. However, study design flaws and lower levels of evidence were common. Of most concern were poorly described methodologies and under-powered sample sizes incapable of producing statistically significant results (see Appendix 4 for tabulated quality assessment results). Examining the methodology used, sixteen studies adopted a mixed methods approach and six utilised a quantitative method. Randomised controlled trials were reported in seven papers and a battery of different metrics were recorded across the studies. Heart rate variability and salivary cortisol levels were the most frequently reported objective data, whilst the state trait anxiety inventory was the most common subjective measure (see Table  2 ).

In general terms, the twenty-two included studies had similar aims centring around determining how successful high stress simulation could be at replicating clinical experience. Most studies involved both male and female participants with samples sizes ranging from n  = 8 to n  = 166, with a mean of n  = 53 and a median of n  = 33. Multiple studies assessed and compared stress levels of participants in different situations, and then used these results to determine if stress had affected clinical performance. Other studies used similar data to improve education or training with the aim of ultimately increasing student confidence and performance. The key outcomes from the included studies are summarised in Table  3 .

This review identified studies exploring physiological responses of participants undertaking high stress scenario-based education or training. While acknowledging much of the evidence was of low methodological quality [ 46 ] and therefore limits generalisability, the results still provide some useful insights that may be used to inform educators of future paramedics and other healthcare workers.

One of the key findings from this review was the identification of pre-performance or anticipatory anxiety exhibited across multiple studies [ 37 , 38 , 45 ]. This is an area where simulation may not replicate clinical work. Students aware of an upcoming scenario well in advance have ample time to prepare and mount a physiological stress response. This could be controlled if students were given little notice, however this was not commonly reported in the studies. Healthcare educators utilising scenario-based education may choose to restrict prior notification as a means of assessing any changes in the stress response amongst their students. In high-acuity clinical work, paramedics usually have little time to prepare, which may reduce the anticipatory stress response. Potentially this may be seen as positive, as stress has been demonstrated to lead to poorer performance is some paramedic research [ 5 , 6 ]. However, the evident stress of attending high-acuity cases must also be considered and its impact on performance. In the context of anticipatory anxiety predicting future performance, little research has examined its immediate effect on motor task performance.

The reviewed publications also provide contradictory support for simulation as a tool to replicate the psychophysiological stress of high-acuity clinical work. Baker, et al. [ 37 ], in a study with trainees in the highly specialised field of anaesthetics, found simulation was able to replicate the physical and procedural forms of clinical work, however it was unable to replicate the intrinsic level of stress the trainees exhibited when working with a real patient in an operating theatre. These results are potentially influenced by small participant numbers ( n  = 8) and may also be applicable to highly specialised and highly technical fields such as anaesthesia. For paramedicine, contemporary literature [ 47 ] highlights simulation allowing for the training of skills that are rarely needed or rarely practiced in the field and supports recommendation made by O’Meara, et al. [ 2 ]. For educators of paramedics and other healthcare workers, simulating high-acuity situations is a crucial way to expose students to potential clinical scenarios they may face early in their career. In designing programs of study, careful use of stress-inducing high-acuity simulation can be a beneficial but can also lead to continued underperformance if the stress is chronically too high.

Barbadoro, et al. [ 26 ] and Judd, et al. [ 43 ] found simulation provided a higher level of stress in their participants when compared to equitable clinical work, whilst Demaria, et al. [ 28 ] found that high stress situations can be beneficial for learning. This benefit of high stress learning was also supported by the work of Keitel, et al. [ 32 ], who found increased levels of the key stress hormone cortisol correlated with improved memory retention and medical performance amongst medical trainees. An increased stress response was also reported when supervisors or assessors were present within the simulation [ 30 , 31 ] and, unsurprisingly, vital signs as a measure of physiological stress, increased when the simulation itself was exertive [ 25 , 27 , 36 ]. The stress placed on students involved in high-acuity simulation must be further studied to allow educators to determine what level of anxiety may enhance learning without impeding performance.

Performance ability or academic standing was also found to correlate with stress. McKay, et al. [ 38 ] found low performers increased stress and performed poorly, whereas high performers also increased stress but performed superbly in a cohort of student nurses. Paramedicine courses may show similar trends, with students likely to self-assess their academic abilities and stress tolerance. Educators could potentially use real-time learning analytics to offer tailored support and guidance based on live biometric data, proactively aiding students. This would be resource intensive for academics with large student numbers; but in smaller cohorts, the individual feedback around acceptable stress to achieve simulated clinical success may enhance the education program.

Lacking from the literature is a detailed discussion of a variety of variables related to student stress responses from the level of acuity of a scenario. These confounding variables, such as pre-established coping styles and perceived stress intensity within participants, need to be quantified to accurately gauge the success of any interventions aimed at alleviating the stress response, and in examining what levels of anxiety may enhance learning without impeding performance.

Limitations

Whilst the systematic approach to this scoping review explored multiple electronic bibliographic repositories, there is potential some contemporary conference proceedings, dissertations and theses, along with grey literature not readily available in electronic databases or Google Scholar, may have been missed. Non-English literature may have added value to this review and we attempted to seek translated papers where possible, but we accept that some results may have been missed through this process.

Directions and recommendations for future research

This scoping review identified inconsistencies and varying methodologies for the assessment of participant stress response in scenario-based education. Recommendations should be developed to identify gold standard quantification of psychophysiological stress responses during high stress scenarios. This would then allow meta-analysis or other systematic synthesis of data to be undertaken to accurately determine any inter-relationship between acute stress and the degradation of cognitive decision-making for healthcare education programs. In addition, variables related to student stress responses from the level of acuity of a scenario should be investigated. For example, individual differences in participants such as pre-established coping styles and strategies, perceived stress intensity, perceived control of stress or coping skill, as well as context specific stressors such as the outcomes associated with scenario performance (e.g., high stakes versus low stakes outcomes) may all be important variables for future research.

The studies identified in this scoping review have shown high-acuity simulation can induce stress comparable with paramedicine clinical practice. For educators, understanding the factors or elements which contribute to an acceptable level of stress can allow participants the opportunity to fail and learn from their errors during simulation. This further provides opportunities to improve student outcomes in paramedicine and other healthcare education by facilitating high-acuity clinical scenarios that challenge students without inducing stress levels that hinder performance. As educational and wearable technology further evolves, utilisation of real-time biofeedback through passive measurement devices also hold promise as an intervention to reduce the negative effects of acute physiological stress during training scenarios.

Data availability

Data supporting Fig.  1 ; Tables  1 , 2 and 3 and available within the Supplementary Information (Appendices).

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The authors wish to acknowledge and thank senior research librarian Meena Gupta for assistance with developing and refining key search terms within the research protocol.

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JB, DL, EF and PH conceived the study. JB and DL equally designed the study approach. DL, EF and PH undertook the review. JB and PH undertook data extraction and quality control. JB interpreted the data. JB, DL and EF drafted the manuscript and circulated to authors for contribution. All authors edited drafts and approved the current manuscript for publication. JB as the corresponding author is responsible for the overall content.

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Betson, J., Fein, E.C., Long, D. et al. Too stressed to think? A scoping review of the literature for healthcare educators utilising high acuity clinical scenarios. BMC Med Educ 24 , 990 (2024). https://doi.org/10.1186/s12909-024-05949-3

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Research Article

What is a heat wave: A survey and literature synthesis of heat wave definitions across the United States

Roles Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Validation, Writing – original draft

* E-mail: [email protected]

Affiliations Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, Michigan, United States of America, Remote Sensing and GIS Research and Outreach Services, Michigan State University, East Lansing, Michigan, United States of America

Roles Formal analysis, Visualization, Writing – review & editing

Roles Funding acquisition, Methodology, Writing – review & editing

Affiliation Department of Geography, University of Florida, Gainesville, Florida, United States of America

• Erin L. Bunting, 
• Vasily Tolmanov, 
• David Keellings

• Published: September 5, 2024
• https://doi.org/10.1371/journal.pclm.0000468
• Peer Review
• Reader Comments

Heat waves are the last extreme weather events without a formal, on the books, definition. Instead, across the U.S. those working on extreme heat event management, forecasting, and planning are using differing definitions in their work. With such differing definitions being used there are widespread impacts including some to human and environmental health, natural resource management, and long-term emergency management planning. For instance, when should heat advisories for vulnerable populations be released when an event impacts a region using multiple definitions? There are concrete and justifiable reasons for the lack of a formal heat wave definition including, at its simplest, differences in what temperature is extreme enough, compared to the region’s climatological regimens, to be deemed as an extreme heat event or heat wave. This study looks for patterns and commonalities in emergency managers and climatologists, those most commonly addressing or planning for such events, definition of heat wave events through a review of the literature and widespread survey across the United States. Through a short 11-questions survey and subsequent text mining, we find widespread variability in the common heat wave definitions but a consistent pattern of core key term usage including aspects of heat duration, extreme temperature, and humidity. However, we also see little to no usage of non-climatological variables such as exposure, vulnerability, population, and land cover/land use.

Citation: Bunting EL, Tolmanov V, Keellings D (2024) What is a heat wave: A survey and literature synthesis of heat wave definitions across the United States. PLOS Clim 3(9): e0000468. https://doi.org/10.1371/journal.pclm.0000468

Editor: Teodoro Georgiadis, Institute for BioEconomy CNR, ITALY

Received: December 14, 2023; Accepted: July 13, 2024; Published: September 5, 2024

Copyright: © 2024 Bunting et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data needed to recreate this article is provided in the supplemental materials associated with the article. Identifying data will not be released including the survey takers names or contact information, but these data are not essential for study recreation. This project was deemed exempt from full IRB review. Any questions related to the data, or the article can be addressed by emailing the authors Erin Bunting ( [email protected] ) or David Keellings ( [email protected] ). If you have any data requests or additional questions, and the authors are not available, please feel free to contact MSU RS&GIS ( [email protected] ). This group has prior knowledge of the project and a copy of project and IRB documents on a secure server. If you have any questions or concerns regarding the survey, contact please contact the Michigan State University Human Research Protection Program (HRPP) office at 517-355-2180 or via email at [email protected] . Please use the following MSU Study ID: STUDY00004016 if you communicate with MSU HRPP.

Funding: This research was funded by a grant from the National Science Foundation GSS program (Award #2203235). Multiple people and organizations made this publication possible and the authors wish to express their gratitude, especially to Dr. Laura Myers and Jacob Reed at the University of Alabama, Dan Wanyama, and the Staff of Remote Sensing and GIS Research and Outreach Services (RS&GIS) at Michigan State University. Additionally, we thank the reviewers for their time and effort put into manuscript review.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Extreme weather and climate events have been impacting human and natural landscape since the beginning of time. However, with changing climate patterns we are seeing greater impacts of these events globally. As stated in the most recent IPCC report it is an “established fact” that human induced change has resulted in “an increased frequency and intensity of some weather and climate extremes since pre-industrial times” [ 1 ]. With such changing patterns it is important to look at not just the impacts and patterns of such events but the premise of the event definition itself. Extreme weather and climate events are broadly defined as severe weather or climate conditions that induce devastating impacts to the human and natural landscapes. While such events can be weather-related (short in duration), or climate related (long in duration) there is a basic understanding that the event is defined as atypical and beyond the normal. Almost all these extreme events (e.g., hurricanes, tornadoes, blizzards) have standard definitions related to what triggers the event, how they are measured, and their severity classification. For instance, with hurricanes, the Saffir Simpson class is used to define the pressure and wind speeds associated [ 2 ]. With tornadoes, the Enhanced Fujita Scale (EF) uses derived engineering wind estimates in assessing strength and resulting damage [ 3 ].

While almost all climatological events have standard definitions one of the deadliest does not, heat waves. Over the past decade, heat waves of varying durations and intensities have impacted much of the globe. For instance, the 1995 heat wave in the central United States resulted in more than 1000 deaths [ 4 , 5 ]. Further, across France, the large 2003 European heat wave resulted in excess mortality of approximately 15,000 individuals, up 60% from normal mortality patterns [ 6 , 7 ]. In line with the IPCC remarks on trends of extreme events there has been an increased frequency of heat waves not just across the US and France, but across Europe, China, Australia as well [ 1 ]. Additionally, it has been projected that heat waves will be more frequent, intense, and longer-lasting into the future [ 1 , 8 – 10 ], thus raising concerns on planning for such events.

There is no worldwide consensus on a heat wave definition though its usually thought simply as an extended period of extreme heat. Not grounded in academic or climatological literature, the Merriam dictionary defines heat waves as “a period of unusually hot weather”. Additionally, in many published articles, simple definitions based purely on temperature are utilized, including those as simple as “an extreme heat event is defined when the temperature exceeds a given threshold with an appropriate spatial extent” [ 11 ]. Attempts have been made at standardizing a heat wave definition. For instance, in 1996 Environment Canada provided a more scientifically grounded definition of a heat wave as a period of more than three consecutive days of maximum temperatures at or above 32 degrees Celsius [ 12 ]. Additionally, governmental groups both in the US and around the world have developed de facto definitions of heat waves including: the US National Weather Service, NOAA, and UK Met Office. These definitions are being developed for the issuance of heat watches and warnings and therefore should be pertinent for use by emergency managers and state climatologists. With this in mind, one would expect high overlap between national government definitions of heat wave and those from managers and state climatologists. The aforementioned governmental groups define heat waves as such:

• US National Weather Service : A period of abnormally hot weather generally lasting more than 2 days. Heat waves can occur with or without high humidity.
• NOAA : A period of abnormally and uncomfortably hot and unusually humid weather. Typically, a heat wave lasts two or more days.
• UK Met Office : An extended period of hot weather relative to the expected conditions of the areas at that time of year, which may be accompanied by high humidity.
• World Meteorological Organization (WMO) : A period where local excess heat accumulates over a sequence of unusually hot days and nights.

What is lacking from these definitions is consistency regarding temperature thresholds, metrics, durations, or number of days used to define such events [ 13 ]. Further, even using these definitions, adverse heat impacts on human health have been documented at lesser extreme temperatures and durations [ 12 , 14 , 15 ]. It’s important to note that the use of various heat wave definitions results in temporal variability in heat wave classification, inability to compare events and synthesize results across regions, and inconsistent terminology in the literature. Many of these challenges can be overcome by finding some consistency across regions and definitions.

Why it is difficult to develop a consistent heat wave definition is fairly obvious, heat waves differ in their intensity (magnitude), extent, duration, and scope of impact [ 16 ]. Numerous studies have used different thresholds of mean or maximum temperature [ 17 ], percentiles of maximum temperature, heat indices, or even combinations of thresholds [ 18 , 19 ]. The common variables of these studies being the use of intensity and duration factors. [ 20 ] looked at 45 definitions of heat waves, combining 5 temperature thresholds, three temperature indicators (daily mean temperature, minimum temperature, and maximum temperature), 5 percentile metrics (90 th , 92.5 th , 95 th , 97.5 th , and 99 th ), and multiple event duration lengths (2, 3, and 4 days) to assess how different definitions align with mortality patterns. Overall, [ 20 ] found the best model fit, and therefore best heat wave definition, using daily mean temperature in the 99 th percentile in combination with a 3-day event duration. Similarly, two studies, one in the US and another from West Africa, found the best fit model was produced by a heat wave definition using both minimum and maximum temperatures in the 90 th percentiles with an event duration of 3 days [ 21 ]. Drawing from these, and other, studies we can see a heat wave definition needs to include factors of: (1) intensity or magnitude: based on a tested index or temperature threshold, (2) duration: defining the persistent of an event to be a heat wave, (3) extent: geographic areas impacted and measures of exposure, and possibly (4) severity [ 22 ].

There are no doubt other factors contributing to heat wave impacts such as frequency, timing, event size, and population density. Incorporation of these factors into a unifying heat wave definition is fraught with complications. For instance, a higher frequency of hot and humid conditions do not necessarily result in a heat wave and severe heat wave impacts [ 12 ]. Similarly, regions experiencing more hot and humid summer conditions already have physiological, behavioral, and infrastructure adaptations to extreme heat, likely reducing the harmful effects [ 12 ]. However, with such heat wave components (intensity, duration, and extent), we can begin to understand the social, cultural, and physical impacts of extreme heat events. For instance, with such a holistic definition we can begin to develop temporary modification to lifestyles to minimize heat stress exposure and impacts. More clearly such a definition enables evasive action and management practice to be developed.

Heat waves can be defined in several ways, through absolute and relative approaches. With an absolute heat wave definition an exact event duration threshold would be set in tandem with a pre-determined temperature and/or heat stress index level [ 12 ]. Whereas, if a relative heat wave definition were developed it would have to take into consideration acclimatization to weather, exposure, and human dimensions in addition to region specific climate trends. Most published studies on heat wave definitions look to model or develop the core metrics, threshold, and durations to define event occurrence. In this study we ask those on the ground for insight into heat wave event definition. Through a simple survey conducted across the United States in 2020 we look at what definitions each respondent is currently using, what variables they see as critical, impacts of differing definitions, and how relative definition approaches are used in their work. Overall, it was hypothesized that (1) emergency managers would have a different perspective on heat wave definition, especially as it relates to human exposure and acclimatation, (2) differing local to regional climate trends across the country would result in different definitions north to south across the U.S., and (3) only atmospheric variables would be considered in respondents’ heat wave definitions.

2. Data and methods

2.a. survey development.

The survey was designed to see how those involved in heat wave forecasting and management define such events and what factors contribute to their definition. The 11-question survey also included space for respondents to provide additional text and information to clarify their definitions and provide other important details. The survey was developed, tested, and IRB approved at Michigan State University and the University of Alabama. Respondents, none of whom were minors, were informed of survey privacy and provided consent language prior to taken the survey. All respondents provided written consent at the onset of the survey. The survey was developed within Qualtrics and consisted of 11 questions ( Fig 1 ).

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The survey commences with questions of occupation, geographic extent of work, and zip code of residence ( Fig 1 ). Question 4–11 are, using free response, asking how, in their professional capacity they, define heat wave events. Additionally, we ask: what variables are associated with the definition (in case this is not mentioned in the free response), what non-atmospheric variables are included, and looks to understand how event duration, size, and time of year factor into their definition.

The survey was open for approximately five months. Distribution of the survey occurred through emails to: (1) state climatologists listserv, (2) emergency management associations, (3) a contact list for all state-recognized emergency managers, and (4) the American Meteorological Association listserv. Overall, the survey was disseminated through associations and email groups related to the parties of interest.

2.b. Data processing and analysis

Once the survey period was completed the Qualtrics survey was closed, and the data were downloaded locally. To clean the data, we first looked at the completeness of responses. Overall, 137 individuals fully completed the survey and 25 partially completed the survey. It was decided if a respondent answered at least half of questions 4–11 (see Fig 1 ) that these would still be included in the analysis. Other responses were removed from the analysis.

Data analysis occurred in several ways. Text mining and pattern analysis were conducted within R [ 23 ]. Initially, analysis began with simple frequency counts of factors such as geographic extent of work, field of work, etc. Next, for questions four and five, text mining was conducted using several R packages including SnowballC [ 24 ]. First, by question, the responses were merged into a corporal collection of phrases containing natural text. From there the tm_map function was used to remove symbols, number, punctuation, and common words (i.e., cause, the, and, is, have, are, was, be, of) from each survey response entry. The resulting text is not in sentence format but rather the key words within each response.

After the data were cleaned for text mining, analysis began with simple frequency counts. Using this simple statistic, the common terms or words utilized by the respondents were tallied by question. Terms or words used more than twice were preserved in the analysis. In addition to the frequency analysis, word clouds were constructed using the wordcloud package in R [ 14 ].

The word cloud represents the extremes of thought, terminology, and definition of heat waves. The word clouds also highlight the frequency in term utilization by the respondents. For the remaining questions simple summary statistics were completed in R.

3.a. Profile of survey respondents

We see large diversity in the response pool, both spatially and across profession. Overall,162 individuals took part in this project with 137 fully completing the survey. This corresponds to an 84.55% completion rate. The 162 responses came from across the US and across a wide variety of subfields related to climatology, emergency management, and meteorology. Spatially, responses were collected from 43 of the 50 states with Hawaii, South Carolina, Massachusetts, Rhode Islands, Connecticut, Virginia, and West Virginia being the exceptions. While there are no direct responses from those states, they are partially represented by those that work regionally, nationally, or at the global scale. While there are survey responses across the country there is a slight skew to the southern portion of the United States. For those respondents that work county to statewide we see the most responses from Alabama, Mississippi, Florida, Arizona, and Texas ( Fig 2 ). However, there are responses at this scale spread evenly across the U.S.

Dots represents those that work at the county level. Colored polygons represent those that work at the state level and the number of people that responded to the survey that work at the state level. Numbers represent those that responded that worked at another scale of geometry (e.g., city, community, multiple counties, townships, etc.). Not represented are those that worked globally (n = 8), nationwide (n = 4), and those that work sub-county level (city or community). Map created in ArcGIS using survey data and a states shapefile from the US Census Bureau ( https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html ).

https://doi.org/10.1371/journal.pclm.0000468.g002

The professional profile of those that completed the survey is diverse and does not skew to one group of individuals or those with certain specialties. Eighty-seven (53.7%) of the completed surveys came from those that identified forecasting as their occupation. Of those 62 worked across multiple counties in a single state, 11 at the state level, 7 at the regional (multiple states or parts of multiple states), 3 at the county level, 2 at an “other level” (city to local level), 1 globally, and 1 at the community level. After forecasting the next highest group of responses came from emergency managers. Overall, 46 (28.2%) emergency managers from across the US completed the survey. Of those emergency managers 32 worked at the county level, 2 worked across multiple counties, 1 worked at the regional scale (across multiple states), 1 worked at the national level, 1 worked at the community level, and the remaining 9 worked at other scales (e.g., tribal lands, city, ecoregion, etc.). Lastly, 29 individuals identified their career as “other”, including: researchers, retired state climatologists, climatologists, land managers, and those in academia. From this group the majority worked at the state to global scale.

3.b Defining heat waves

Text mining of survey responses showed interesting keyword usage patterns ( Fig 3 ). For the question “What is your definition of the climatological term ‘heat wave’”, heat was not the most used word, instead days was mentioned 71 times by respondents. Answers related to this term included definitions like: “Several days of 95+ degrees”, “A period of multiple days beyond normal temperatures”, and “At least 3 consecutive days of high maximum temperatures”. After days, the terms heat (n = 68), period (n = 65), normal (n = 47), and high (n = 42) were the other common terms ( Fig 3 ). Overall, this word usage pattern highlights a highly important and common thought pattern in defining heat waves, such extreme events have a duration aspect that needs to be considered and defined. Heat waves are multi-day extreme events.

Response trend word clouds and frequency counts for the questions: (A) “What is your definition of the climatological term heat wave?”, and (B) “What atmospheric variables are part of your definition of a heat wave?”, and (C) “What other atmospheric variables are part of your definition of a heat wave”.

https://doi.org/10.1371/journal.pclm.0000468.g003

When asked, “What atmospheric variables are part of your definition of a heat wave”, the most selected term was maximum temperature (n = 129) followed by heat index (n = 85), minimum temperature (n = 75), and humidity (n = 74). These four terms were far more common than the next terms of the list, average temperature (n = 45) and other (n = 25). Respondents were given a list of possible terms for this question and asked to select all that apply to their definition of heat waves. The terms included include maximum temperature, minimum temperature, average temperature, humidity, heat index, and other. This word usage pattern highlights that extreme terms are central to the definition of heat waves.

Lastly, respondents were asked “What other atmospheric variables are part of your definition of a heat wave?” This question was asked so respondents have free response, instead of choosing from a bank of options as with the previous questions, the climatological variables that they use in management, forecasting, and planning. It is important to note most respondents did not list any additional atmospheric variables as part of their heat wave temperature. Of those that did the most common response was wet bulb globe temperature (WBGT), wind, cloud cover, and insolation. A few respondents listed, repetitively, humidity and heat index, in response to this question. Overall, this word usage pattern highlights the strong pattern of traditional climatological variables in the definition of heat waves, rather than characteristics of land, demographics, human health, or exposure.

3.c Beyond climatological terms, other factors to considered in defining heat waves

Beyond traditional climatological terms we asked respondent “Does your definition of a heat wave include non-atmospheric variables?” Only 27.5% of respondents replied in the affirmative that they did include non-atmospheric variables in their definition of a heatwave ( Fig 4 ). The common non-atmospheric variables included in the heat wave definition were grouped into categories of: (1) impact on humans, (2) seasonal variation, (3) physical variables, and (4) other. The highest percentage of responses were within the impact to humans categories with 35.3%. Common non-atmospheric variables listed by respondents included: soil moisture, land type, duration, percent impervious surfaces, and crop stress. Most respondents that included non-atmospheric variables in their definition of heat waves were forecasters (n = 25), with only 5 emergency managers and those identified as “other” in their career including non-atmospheric variables. Geographically, those that included non-atmospheric variables mostly worked at the state to multi county scale (n = 22). Others that included non-atmospheric variables worked at varying geographic extents including county scale (n = 7), regional scale (n = 3), and global scale (n = 2). Spatially, that 27.5%, where not clusters in one portion of the US. Those that included non-atmospheric variables spanned from Arizona to Vermont.

Response trends to the questions: (A) “Does your definition of a heat wave include non-atmospheric variables?” and (B) “If yes, what other variables do you include?”.

https://doi.org/10.1371/journal.pclm.0000468.g004

Next, respondents were asked if their heat wave definition included a threshold that must be surpassed to be considered a heat wave and what threshold measurement they used. More than 56% of respondents said yes, their definition included a threshold that must be crossed. Of these 18 were emergency managers, 33 were forecasters, and 16 listed other as their career. As such, of the respondents that completed the survey approximately 38% of forecasters, 39% of emergency managers, and 55% of others included a threshold in their heat wave definition. Spatially, those that included a threshold worked across all geographic extents from community / local to global, though slightly more worked at the statewide or multicounty scale.

It was thought that the main threshold both emergency managers and forecasters would use in their definition would be related to temperature. Many of the provided definitions stated something like:

“Temperature above 95 degrees Fahrenheit for an extended period of time”

“A persistent anomaly in daily surface temperature usually many days about the 98 th percentile.”

Instead, survey results show that only 45.7% of respondents reported using temperature thresholds in their heat wave definitions, followed by heat index (27.9%), heat duration (23.5%), and heat risk ( Fig 5 ). Spatially, if we look at the common temperature threshold mentioned by survey respondents there is an interesting dynamic playing out ( Table 1 ). Those surveyed from the Northeast, Southwest, and Central regions of the United States, as defined by the NOAA climate regions, all listed a temperature threshold of 90°F with little variability. The lowest temperature threshold mentioned was 80°F and it was from a respondent in the south region. Whereas the highest threshold reported was 105°F, occurring in both the East North Central and South regions. Overall, a north to south temperature threshold gradient was expected, as the more southerly regions are subject to climatically higher mean temperatures, but this was not seen in the survey responses.

Response trends to the questions: (A)” Does your definition of a heat wave include a threshold that must be crossed in order to be considered a heat wave?”, and (B) “what is the threshold measurement that you use?”.

https://doi.org/10.1371/journal.pclm.0000468.g005

https://doi.org/10.1371/journal.pclm.0000468.t001

Fig 5B shows that numerous respondents suggest a threshold in their heat wave definition based on duration. During the survey respondents were specifically asked “Does your definition of a heat wave include a minimum duration”. This question was asked, in tandem with the aforementioned in case a respondent did not feel duration was a threshold measure. Over 74% of respondents said yes that event duration was a part of their heat wave definition. Of these 25 were emergency managers (54.3% of EM total population), 56 where forecasters (64.4% of F total population, and 19 listed their career as other. Importantly, the most noted durations for events were 2 and 3 days ( Fig 6 ). Overall, 22.2% or respondents mentioned 2 days as the minimum duration for a heat wave to be defined and 56.6% listed 3 days as the minimum duration. The response pattern of did not vary by geographic extent of work or the region respondents work in.

Response trends to the questions: (A) “Does your definition of a heat wave include a minimum duration?”, and (B)” If yes, how many days?”.

https://doi.org/10.1371/journal.pclm.0000468.g006

At this point, respondents had solely been asked about common aspects of heat wave definitions such as duration, intensity, etc. The next question of the survey asked, “Is size of area affected part of your definition of a heat wave?” Only 21.8% of respondents considered event size in their heat wave definition, equating to just 29 respondents. Of these there was an even split between emergency managers and forecasters, the geographic extent of their work was mostly single or multiple counties, and almost all included duration in their heat wave definition.

The majority of those that responded in the affirmative said that a heat wave had to have a duration of 2 or 3 days to be defined as such an event. The range of durations suggested by those surveyed was as short as 1 day (3 respondents) and as long as 10 days (1 respondent) ( Fig 7 ).

Response trends to the questions: (A) “Is size of area affected part of your definition of a heat wave?”, and (B) “If yes, how is size incorporated into your definition of a heatwave?”.

https://doi.org/10.1371/journal.pclm.0000468.g007

If a heat wave only impacts a small area, does it matter in emergency management and forecasting as much as large events? That question was the impetus for the next survey question, “is size of area affected part of your definition of a heat wave.” The vast majority (78.2%) of people said no, size does not matter. Those that answered yes to this question were almost all forecasters (except 2 out of 14). Those that answered in the affirmative had a clear theme, larger events impact more people compared to isolated events and that is why event size matters. With this theme several people mentioned that an event needs to be state-wide or span multiple counties (44.8% of the previous questions affirmative answers).

The last two questions of the survey look to see how respondents think about defining heat waves across space and time. Respondents were asked “Is your definition of a heat wave dependent on time of year?” This question was almost a 50/50 split in terms of percentages with 53% saying the definition is not time of year dependent and 47% saying it is ( Fig 8 ). For those that answered in the affirmative they were asked how their definition varies through the year. There were a wide range of responses to this follow up question from those mentioning seasonal threshold values to others saying only summer matters as it’s the warmest season. Overall, the consensus of those surveyed is well represented by one respondent’s remakes, “I generally only call things "heat waves" when it is “hot” outside. I wouldn’t call a winter stretch of warmer than normal weather a "heat wave" unless it was drastically warmer than normal.” There is a consistent trend in these types of answers with others saying outright “heat waves have only occurred climatologically in our late spring to early fall months, when it’s hottest”. However, there were a small group (labeled statistical in Fig 8 ) that again link back to thresholds and say, in their opinion, that heat waves can happen if the temperatures exceed the 95 th percentile for that given time frames normal.

Response trends to the questions: (A)” Is your definition of a heat wave dependent on time of year?”, and (B) “If yes, what time of year?”.

https://doi.org/10.1371/journal.pclm.0000468.g008

Lastly, respondents were asked if their definition of a heat wave varies across space. This question was asked to see if one core definition of heat waves cannot be developed and instead a regional definition would be needed. Overall, only 36.3% responded in the affirmative whereas 63.7% said their heat wave definition did not vary across space ( Fig 9 ). Those answering in the affirmative, that their definition varied across space, were then asked how with the options of: (A) regional differences, (B) type of territory, (C) statistically, (D) varies by the gridded data, and (E) other. Overall, 42.9% of respondents that thought heat wave definitions should vary across space thought there were regional differences. When asked to explain their answer respondents had a variety of answers and mentioned topic such as: “definitions can be different across climate regions because citizens are acclimated to different levels” and “In my work the western high plains should have a higher threshold for defining heat waves than the eastern portion of the Southern Plains”, and “Since humans can become acclimated to "normal" conditions, I would vary the definition based upon a certain amount above climatology”. Next, 22.5% of respondent said heat wave definitions should vary because of type of territory. Those that responded in this manner mentioned: “amount of vegetation vs bare ground is important” and “population density and impervious surfaces need to be considered to account for urban heat island effects”. Third, 22.5% said statistically heat wave definitions should vary spatially. These respondents provided further understanding of their response with comments such as: “Heat wave might be the top 0.1% of high temperatures of all time for each location” and “temperature percentile varies by location”. Lastly, only 6.1% of respondents said their definition varies over space because of gridded data. Specifically, those that answered in this manner added comments like: “heat risk is calculated on a spatial grid by entire forecast area” and “gridded temperature data is 2.5 km resolution—so the values for defining the heat wave vary spatially even if the definition remains the same.” Overall, these responses highlight a need to better understand duration, thresholds, and the underlaying population exposure.

Response trends to the questions: (A) “Does your definition of a heat wave vary across space?”, and (B) “How does it vary spatially?”.

https://doi.org/10.1371/journal.pclm.0000468.g009

4. Discussion and conclusions

Heat waves are regarded by the U.S. National Weather Service as a major, in fact leading, cause of weather-related fatalities in the U.S. in most years [ 18 ]. Closing the gap on a formal heat wave definition is important because of such direct affect that these extreme events can induce. A formal definition is important to multiple subfields, beyond emergency management and forecasting, as in the literature it has been consistently noted that the direct adverse effects of heat waves include (1) increased demands for water and electricity [ 25 – 28 ], (2) reduced productivity and overall labor efficiency [ 25 ], (3) drought and overall crop stress or failure, and (4) human health, including cardiovascular and respiratory damage in addition to death. Further, in the literature heat-related studies have been published related to land management, policy development, vulnerability/exposure regarding human health, urban development, emergency planning, and ecosystem health.

Overall, our population is vulnerable to heat waves and therefore we need a better understanding of these extreme events. For instance, the impacts of heat waves on human health are widely documented regarding mortality and morbidity [ 29 , 30 ]. That said, heat wave vulnerability is unequal and unevenly distributed across both human and natural landscapes. The elderly, those residing in nursing homes, and the chronically ill are readily identifiable as susceptible subgroups, high at risk for extreme heat impacts, and studies have criticized the lack of effective heat management for such populations [ 31 – 35 ]. These populations are not the only ones impacted by heat waves and the way such events are managed. Children have a higher sensitivity, outdoor workers have more extensive exposure, and the homeless are just a few of the other vulnerable populations [ 36 – 40 ].

Heat related deaths occur when a rapid temperature increase outpaces the body’s ability to cool itself, though perspiration and increased blood circulation [ 41 ]. There are compounding factors to such mortality risk including high humidity and overall exposure. If we examine the characteristics of heat waves mentioned throughout this study and our survey (duration, intensity, timing, size) its known that these factors negatively impact public health by increasing the risk of heat-related mortality [ 41 ]. For instance, long duration heat events increase exposures, elevate even the daily minimum temperature, and limit the body’s ability to recover [ 17 , 27 ]. Additionally, large-scale events are more likely to expose broader human populations to such extremes, increasing the population vulnerable to such an event. Lastly, the timing of an event can have multiple implications including (1) early extreme events can result in large populations of people unprepared and (2) events during peak summer months can be assumed to be more intense in nature. Beyond health impacts, the timing of such events can alter soil available moisture, impacting plant phenology and productivity. With such changes in soil moisture there are not only connections to widespread drought but also to impacts of cropping and overall harvest quality and quantity, again linking back to human impacts. Overall, in the US, the Center for Disease Control (CDC) estimates that heat related deaths average over 1000 per year [ 42 ]. However, the impacts of extreme heat stretch far beyond the health ramifications and therefore needs to be studied and defined more holistically.

How can a formal unified heat wave definition impact emergency management? Simply, with the development of a formal definition and associated research emergency managers and forecasters can know what to expect, outcome wise and in knowing the vulnerable populations, and plan for during extreme heat events. Much research has gone into analyzing heat wave definitions to see how they align with differing outcome patterns, particularly human health impacts. For instance, Knowlton et al, analyzed heat wave induced hospitalizations and emergency room visits resulting from the 2006 California extreme event and found that a definition based on a higher maximum temperature threshold was associated with a greater relative risk for hospital admission [ 43 , 44 ]. Other studies have tested heat wave definition to decide the ideal scenario for the opening of cooling centers and to consider moving vulnerable people to safer locations [ 45 , 46 ]. Beyond hospitalization and evacuation with a formal heat wave definition managers can understand, through research of trends, what to expect for certain mortality risks. For example, Dong et al. 2016 found that a heat wave definition using the 93 rd percentile of maximum temperature and 5 day event duration was the best way to understand the trends between extreme heat and cardiovascular mortality [ 47 ]. These examples, explicitly related to health outcomes and exposure due to extreme heat are just a few ways in which emergency management could use such information and data.

We live in a warming climate, and with such changing regimes it’s projected that the duration, intensity, and frequency of heat waves will increase [ 48 ]. From the literature and survey, we can surmise a few key aspects of a formal heat wave definition. Factors that need to be considered in the formal definition include duration, intensity (a climatological threshold), and exposure. Survey results show that the majority respondents report a heat wave can be defined as an event that lasts at least 3 days. The mean duration listed by survey respondents was 3.14, the mode was 3 days, and the median was 3 days as well. Overall, the durations listed by respondents ranged from 1–10 days. Regardless of occupation and geographic extent of work most respondents felt a threshold value needed to be crossed for a heat wave to formally be defined. Both percentiles and numeric thresholds were suggested. Additionally, a large group of respondents felt that maximum temperature was the key metric to develop such a threshold. For those that wanted to include percentiles in their heat wave definition the 95 th percentile was the most noted. In terms of absolute temperatures there was a large range of possible thresholds suggested, 90–105 degrees. The mean temperature threshold suggested by respondents was 94.6 degrees. Interestingly, there were no survey response trends suggesting for spatial variation in heat wave definitions. In terms of time of year, most felt that heat waves would only be defined in the hottest months and are not solely associated with any monthly or seasonal temperatures beyond the normal. One of the most important non-climatological aspects brought up by survey respondents was exposure and associated population density. Such survey results do not solidify a formal heat wave definition, but they do show extremely similar thought processes across different occupations and regions of the US. With such similarities in defining heat waves there is no doubt similar patterns of use for such data in emergency management and forecasting, and perhaps for a future unified definition of heat waves.

Supporting information

S1 data. deidentified survey data collected including the full irb information and question in the header of the data file..

https://doi.org/10.1371/journal.pclm.0000468.s001

Acknowledgments

Multiple people and organizations made this publication possible, and the authors wish to express their gratitude. The authors wish to especially thank Dr. Laura Myers and Jacob Reed at the University of Alabama, Dan Wanyama, and the staff of Remote Sensing and GIS Research and Outreach Services (RS&GIS) at Michigan State University. Additionally, we thank the reviewers for their time and effort put into manuscript review.

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