types of literature synthesis

Literature Syntheis 101

How To Synthesise The Existing Research (With Examples)

By: Derek Jansen (MBA) | Expert Reviewer: Eunice Rautenbach (DTech) | August 2023

One of the most common mistakes that students make when writing a literature review is that they err on the side of describing the existing literature rather than providing a critical synthesis of it. In this post, we’ll unpack what exactly synthesis means and show you how to craft a strong literature synthesis using practical examples.

This post is based on our popular online course, Literature Review Bootcamp . In the course, we walk you through the full process of developing a literature review, step by step. If it’s your first time writing a literature review, you definitely want to use this link to get 50% off the course (limited-time offer).

Overview: Literature Synthesis

• What exactly does “synthesis” mean?
• Aspect 1: Agreement
• Aspect 2: Disagreement
• Aspect 3: Key theories
• Aspect 4: Contexts
• Aspect 5: Methodologies
• Bringing it all together

What does “synthesis” actually mean?

As a starting point, let’s quickly define what exactly we mean when we use the term “synthesis” within the context of a literature review.

Simply put, literature synthesis means going beyond just describing what everyone has said and found. Instead, synthesis is about bringing together all the information from various sources to present a cohesive assessment of the current state of knowledge in relation to your study’s research aims and questions .

Put another way, a good synthesis tells the reader exactly where the current research is “at” in terms of the topic you’re interested in – specifically, what’s known , what’s not , and where there’s a need for more research .

So, how do you go about doing this?

Well, there’s no “one right way” when it comes to literature synthesis, but we’ve found that it’s particularly useful to ask yourself five key questions when you’re working on your literature review. Having done so,  you can then address them more articulately within your actual write up. So, let’s take a look at each of these questions.

1. Points Of Agreement

The first question that you need to ask yourself is: “Overall, what things seem to be agreed upon by the vast majority of the literature?”

For example, if your research aim is to identify which factors contribute toward job satisfaction, you’ll need to identify which factors are broadly agreed upon and “settled” within the literature. Naturally, there may at times be some lone contrarian that has a radical viewpoint , but, provided that the vast majority of researchers are in agreement, you can put these random outliers to the side. That is, of course, unless your research aims to explore a contrarian viewpoint and there’s a clear justification for doing so. 

Identifying what’s broadly agreed upon is an essential starting point for synthesising the literature, because you generally don’t want (or need) to reinvent the wheel or run down a road investigating something that is already well established . So, addressing this question first lays a foundation of “settled” knowledge.

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2. Points Of Disagreement

Related to the previous point, but on the other end of the spectrum, is the equally important question: “Where do the disagreements lie?” .

In other words, which things are not well agreed upon by current researchers? It’s important to clarify here that by disagreement, we don’t mean that researchers are (necessarily) fighting over it – just that there are relatively mixed findings within the empirical research , with no firm consensus amongst researchers.

This is a really important question to address as these “disagreements” will often set the stage for the research gap(s). In other words, they provide clues regarding potential opportunities for further research, which your study can then (hopefully) contribute toward filling. If you’re not familiar with the concept of a research gap, be sure to check out our explainer video covering exactly that .

3. Key Theories

The next question you need to ask yourself is: “Which key theories seem to be coming up repeatedly?” .

Within most research spaces, you’ll find that you keep running into a handful of key theories that are referred to over and over again. Apart from identifying these theories, you’ll also need to think about how they’re connected to each other. Specifically, you need to ask yourself:

• Are they all covering the same ground or do they have different focal points  or underlying assumptions ?
• Do some of them feed into each other and if so, is there an opportunity to integrate them into a more cohesive theory?
• Do some of them pull in different directions ? If so, why might this be?
• Do all of the theories define the key concepts and variables in the same way, or is there some disconnect? If so, what’s the impact of this ?

Simply put, you’ll need to pay careful attention to the key theories in your research area, as they will need to feature within your theoretical framework , which will form a critical component within your final literature review. This will set the foundation for your entire study, so it’s essential that you be critical in this area of your literature synthesis.

If this sounds a bit fluffy, don’t worry. We deep dive into the theoretical framework (as well as the conceptual framework) and look at practical examples in Literature Review Bootcamp . If you’d like to learn more, take advantage of our limited-time offer to get 60% off the standard price.

4. Contexts

The next question that you need to address in your literature synthesis is an important one, and that is: “Which contexts have (and have not) been covered by the existing research?” .

For example, sticking with our earlier hypothetical topic (factors that impact job satisfaction), you may find that most of the research has focused on white-collar , management-level staff within a primarily Western context, but little has been done on blue-collar workers in an Eastern context. Given the significant socio-cultural differences between these two groups, this is an important observation, as it could present a contextual research gap .

In practical terms, this means that you’ll need to carefully assess the context of each piece of literature that you’re engaging with, especially the empirical research (i.e., studies that have collected and analysed real-world data). Ideally, you should keep notes regarding the context of each study in some sort of catalogue or sheet, so that you can easily make sense of this before you start the writing phase. If you’d like, our free literature catalogue worksheet is a great tool for this task.

5. Methodological Approaches

Last but certainly not least, you need to ask yourself the question: “What types of research methodologies have (and haven’t) been used?”

For example, you might find that most studies have approached the topic using qualitative methods such as interviews and thematic analysis. Alternatively, you might find that most studies have used quantitative methods such as online surveys and statistical analysis.

But why does this matter?

Well, it can run in one of two potential directions . If you find that the vast majority of studies use a specific methodological approach, this could provide you with a firm foundation on which to base your own study’s methodology . In other words, you can use the methodologies of similar studies to inform (and justify) your own study’s research design .

On the other hand, you might argue that the lack of diverse methodological approaches presents a research gap , and therefore your study could contribute toward filling that gap by taking a different approach. For example, taking a qualitative approach to a research area that is typically approached quantitatively. Of course, if you’re going to go against the methodological grain, you’ll need to provide a strong justification for why your proposed approach makes sense. Nevertheless, it is something worth at least considering.

Regardless of which route you opt for, you need to pay careful attention to the methodologies used in the relevant studies and provide at least some discussion about this in your write-up. Again, it’s useful to keep track of this on some sort of spreadsheet or catalogue as you digest each article, so consider grabbing a copy of our free literature catalogue if you don’t have anything in place.

Bringing It All Together

Alright, so we’ve looked at five important questions that you need to ask (and answer) to help you develop a strong synthesis within your literature review.  To recap, these are:

• Which things are broadly agreed upon within the current research?
• Which things are the subject of disagreement (or at least, present mixed findings)?
• Which theories seem to be central to your research topic and how do they relate or compare to each other?
• Which contexts have (and haven’t) been covered?
• Which methodological approaches are most common?

Importantly, you’re not just asking yourself these questions for the sake of asking them – they’re not just a reflection exercise. You need to weave your answers to them into your actual literature review when you write it up. How exactly you do this will vary from project to project depending on the structure you opt for, but you’ll still need to address them within your literature review, whichever route you go.

The best approach is to spend some time actually writing out your answers to these questions, as opposed to just thinking about them in your head. Putting your thoughts onto paper really helps you flesh out your thinking . As you do this, don’t just write down the answers – instead, think about what they mean in terms of the research gap you’ll present , as well as the methodological approach you’ll take . Your literature synthesis needs to lay the groundwork for these two things, so it’s essential that you link all of it together in your mind, and of course, on paper.

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• Synthesizing Sources | Examples & Synthesis Matrix

Synthesizing Sources | Examples & Synthesis Matrix

Published on July 4, 2022 by Eoghan Ryan . Revised on May 31, 2023.

Synthesizing sources involves combining the work of other scholars to provide new insights. It’s a way of integrating sources that helps situate your work in relation to existing research.

Synthesizing sources involves more than just summarizing . You must emphasize how each source contributes to current debates, highlighting points of (dis)agreement and putting the sources in conversation with each other.

You might synthesize sources in your literature review to give an overview of the field or throughout your research paper when you want to position your work in relation to existing research.

Table of contents

Example of synthesizing sources, how to synthesize sources, synthesis matrix, other interesting articles, frequently asked questions about synthesizing sources.

Let’s take a look at an example where sources are not properly synthesized, and then see what can be done to improve it.

This paragraph provides no context for the information and does not explain the relationships between the sources described. It also doesn’t analyze the sources or consider gaps in existing research.

Research on the barriers to second language acquisition has primarily focused on age-related difficulties. Building on Lenneberg’s (1967) theory of a critical period of language acquisition, Johnson and Newport (1988) tested Lenneberg’s idea in the context of second language acquisition. Their research seemed to confirm that young learners acquire a second language more easily than older learners. Recent research has considered other potential barriers to language acquisition. Schepens, van Hout, and van der Slik (2022) have revealed that the difficulties of learning a second language at an older age are compounded by dissimilarity between a learner’s first language and the language they aim to acquire. Further research needs to be carried out to determine whether the difficulty faced by adult monoglot speakers is also faced by adults who acquired a second language during the “critical period.”

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To synthesize sources, group them around a specific theme or point of contention.

As you read sources, ask:

• What questions or ideas recur? Do the sources focus on the same points, or do they look at the issue from different angles?
• How does each source relate to others? Does it confirm or challenge the findings of past research?
• Where do the sources agree or disagree?

Once you have a clear idea of how each source positions itself, put them in conversation with each other. Analyze and interpret their points of agreement and disagreement. This displays the relationships among sources and creates a sense of coherence.

Consider both implicit and explicit (dis)agreements. Whether one source specifically refutes another or just happens to come to different conclusions without specifically engaging with it, you can mention it in your synthesis either way.

Synthesize your sources using:

• Topic sentences to introduce the relationship between the sources
• Signal phrases to attribute ideas to their authors
• Transition words and phrases to link together different ideas

To more easily determine the similarities and dissimilarities among your sources, you can create a visual representation of their main ideas with a synthesis matrix . This is a tool that you can use when researching and writing your paper, not a part of the final text.

In a synthesis matrix, each column represents one source, and each row represents a common theme or idea among the sources. In the relevant rows, fill in a short summary of how the source treats each theme or topic.

This helps you to clearly see the commonalities or points of divergence among your sources. You can then synthesize these sources in your work by explaining their relationship.

If you want to know more about ChatGPT, AI tools , citation , and plagiarism , make sure to check out some of our other articles with explanations and examples.

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Synthesizing sources means comparing and contrasting the work of other scholars to provide new insights.

It involves analyzing and interpreting the points of agreement and disagreement among sources.

You might synthesize sources in your literature review to give an overview of the field of research or throughout your paper when you want to contribute something new to existing research.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

Topic sentences help keep your writing focused and guide the reader through your argument.

In an essay or paper , each paragraph should focus on a single idea. By stating the main idea in the topic sentence, you clarify what the paragraph is about for both yourself and your reader.

At college level, you must properly cite your sources in all essays , research papers , and other academic texts (except exams and in-class exercises).

Add a citation whenever you quote , paraphrase , or summarize information or ideas from a source. You should also give full source details in a bibliography or reference list at the end of your text.

The exact format of your citations depends on which citation style you are instructed to use. The most common styles are APA , MLA , and Chicago .

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Ryan, E. (2023, May 31). Synthesizing Sources | Examples & Synthesis Matrix. Scribbr. Retrieved April 2, 2024, from https://www.scribbr.com/working-with-sources/synthesizing-sources/

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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.

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• Lit Review Prep Use this template to help you evaluate your sources, create article summaries for an annotated bibliography, and a synthesis matrix for your lit review outline.

Synthesize your Information

Synthesize: combine separate elements to form a whole.

Synthesis Matrix

A synthesis matrix helps you record the main points of each source and document how sources relate to each other.

After summarizing and evaluating your sources, arrange them in a matrix or use a citation manager to help you see how they relate to each other and apply to each of your themes or variables.  

By arranging your sources by theme or variable, you can see how your sources relate to each other, and can start thinking about how you weave them together to create a narrative.

• Step-by-Step Approach
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How to Write a Literature Review

• 6. Synthesize
• Literature Reviews: A Recap
• Reading Journal Articles
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• 1. Identify the Question
• 2. Review Discipline Styles
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• 4. Manage Your References
• 5. Critically Analyze and Evaluate

Synthesis Visualization

Synthesis matrix example.

• 7. Write a Literature Review

• Synthesis Worksheet

About Synthesis

Approaches to synthesis.

You can sort the literature in various ways, for example:

How to Begin?

Read your sources carefully and find the main idea(s) of each source

Look for similarities in your sources – which sources are talking about the same main ideas? (for example, sources that discuss the historical background on your topic)

Use the worksheet (above) or synthesis matrix (below) to get organized

This work can be messy. Don't worry if you have to go through a few iterations of the worksheet or matrix as you work on your lit review!

Four Examples of Student Writing

In the four examples below, only ONE shows a good example of synthesis: the fourth column, or  Student D . For a web accessible version, click the link below the image.

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Click on the example to view the pdf.

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How to Synthesize Written Information from Multiple Sources

Shona McCombes

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B.A., English Literature, University of Glasgow

Shona McCombes is the content manager at Scribbr, Netherlands.

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Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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When you write a literature review or essay, you have to go beyond just summarizing the articles you’ve read – you need to synthesize the literature to show how it all fits together (and how your own research fits in).

Synthesizing simply means combining. Instead of summarizing the main points of each source in turn, you put together the ideas and findings of multiple sources in order to make an overall point.

At the most basic level, this involves looking for similarities and differences between your sources. Your synthesis should show the reader where the sources overlap and where they diverge.

Unsynthesized Example

Franz (2008) studied undergraduate online students. He looked at 17 females and 18 males and found that none of them liked APA. According to Franz, the evidence suggested that all students are reluctant to learn citations style. Perez (2010) also studies undergraduate students. She looked at 42 females and 50 males and found that males were significantly more inclined to use citation software ( p < .05). Findings suggest that females might graduate sooner. Goldstein (2012) looked at British undergraduates. Among a sample of 50, all females, all confident in their abilities to cite and were eager to write their dissertations.

Synthesized Example

Studies of undergraduate students reveal conflicting conclusions regarding relationships between advanced scholarly study and citation efficacy. Although Franz (2008) found that no participants enjoyed learning citation style, Goldstein (2012) determined in a larger study that all participants watched felt comfortable citing sources, suggesting that variables among participant and control group populations must be examined more closely. Although Perez (2010) expanded on Franz’s original study with a larger, more diverse sample…

Step 1: Organize your sources

After collecting the relevant literature, you’ve got a lot of information to work through, and no clear idea of how it all fits together.

Before you can start writing, you need to organize your notes in a way that allows you to see the relationships between sources.

One way to begin synthesizing the literature is to put your notes into a table. Depending on your topic and the type of literature you’re dealing with, there are a couple of different ways you can organize this.

Summary table

A summary table collates the key points of each source under consistent headings. This is a good approach if your sources tend to have a similar structure – for instance, if they’re all empirical papers.

Each row in the table lists one source, and each column identifies a specific part of the source. You can decide which headings to include based on what’s most relevant to the literature you’re dealing with.

For example, you might include columns for things like aims, methods, variables, population, sample size, and conclusion.

For each study, you briefly summarize each of these aspects. You can also include columns for your own evaluation and analysis.

The summary table gives you a quick overview of the key points of each source. This allows you to group sources by relevant similarities, as well as noticing important differences or contradictions in their findings.

Synthesis matrix

A synthesis matrix is useful when your sources are more varied in their purpose and structure – for example, when you’re dealing with books and essays making various different arguments about a topic.

Each column in the table lists one source. Each row is labeled with a specific concept, topic or theme that recurs across all or most of the sources.

Then, for each source, you summarize the main points or arguments related to the theme.

The purposes of the table is to identify the common points that connect the sources, as well as identifying points where they diverge or disagree.

Step 2: Outline your structure

Now you should have a clear overview of the main connections and differences between the sources you’ve read. Next, you need to decide how you’ll group them together and the order in which you’ll discuss them.

For shorter papers, your outline can just identify the focus of each paragraph; for longer papers, you might want to divide it into sections with headings.

There are a few different approaches you can take to help you structure your synthesis.

If your sources cover a broad time period, and you found patterns in how researchers approached the topic over time, you can organize your discussion chronologically .

That doesn’t mean you just summarize each paper in chronological order; instead, you should group articles into time periods and identify what they have in common, as well as signalling important turning points or developments in the literature.

If the literature covers various different topics, you can organize it thematically .

That means that each paragraph or section focuses on a specific theme and explains how that theme is approached in the literature.

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If you’re drawing on literature from various different fields or they use a wide variety of research methods, you can organize your sources methodologically .

That means grouping together studies based on the type of research they did and discussing the findings that emerged from each method.

If your topic involves a debate between different schools of thought, you can organize it theoretically .

That means comparing the different theories that have been developed and grouping together papers based on the position or perspective they take on the topic, as well as evaluating which arguments are most convincing.

Step 3: Write paragraphs with topic sentences

What sets a synthesis apart from a summary is that it combines various sources. The easiest way to think about this is that each paragraph should discuss a few different sources, and you should be able to condense the overall point of the paragraph into one sentence.

This is called a topic sentence , and it usually appears at the start of the paragraph. The topic sentence signals what the whole paragraph is about; every sentence in the paragraph should be clearly related to it.

A topic sentence can be a simple summary of the paragraph’s content:

“Early research on [x] focused heavily on [y].”

For an effective synthesis, you can use topic sentences to link back to the previous paragraph, highlighting a point of debate or critique:

“Several scholars have pointed out the flaws in this approach.” “While recent research has attempted to address the problem, many of these studies have methodological flaws that limit their validity.”

By using topic sentences, you can ensure that your paragraphs are coherent and clearly show the connections between the articles you are discussing.

As you write your paragraphs, avoid quoting directly from sources: use your own words to explain the commonalities and differences that you found in the literature.

Don’t try to cover every single point from every single source – the key to synthesizing is to extract the most important and relevant information and combine it to give your reader an overall picture of the state of knowledge on your topic.

Step 4: Revise, edit and proofread

Like any other piece of academic writing, synthesizing literature doesn’t happen all in one go – it involves redrafting, revising, editing and proofreading your work.

Checklist for Synthesis

•   Do I introduce the paragraph with a clear, focused topic sentence?
•   Do I discuss more than one source in the paragraph?
•   Do I mention only the most relevant findings, rather than describing every part of the studies?
•   Do I discuss the similarities or differences between the sources, rather than summarizing each source in turn?
•   Do I put the findings or arguments of the sources in my own words?
•   Is the paragraph organized around a single idea?
•   Is the paragraph directly relevant to my research question or topic?
•   Is there a logical transition from this paragraph to the next one?

Further Information

How to Synthesise: a Step-by-Step Approach

Help…I”ve Been Asked to Synthesize!

Learn how to Synthesise (combine information from sources)

How to write a Psychology Essay

Systematic Review and Evidence Synthesis

Acknowledgements.

This guide is directly informed by and selectively reuses, with permission, content from: 

• Systematic Reviews, Scoping Reviews, and other Knowledge Syntheses by Genevieve Gore and Jill Boruff, McGill University (CC-BY-NC-SA)
• A Guide to Evidence Synthesis , Cornell University Library Evidence Synthesis Service

Primary University of Minnesota Libraries authors are: Meghan Lafferty, Scott Marsalis, & Erin Reardon

Last updated: September 2022

Types of evidence synthesis

There are many types of evidence synthesis, and it is important to choose the right type of synthesis for your research questions. 

Types of evidence synthesis include (but are not limited to):

Systematic Review

Addresses a specific, answerable question of medical, scientific, policy, or management importance.

May be limited to relevant study designs depending on the type of question (e.g., intervention, prognosis, diagnosis).

Compares, critically evaluates, and synthesizes evidence.

Follows an established protocol and methodology.

May or may not include a meta-analysis of findings.

The most commonly referred-to type of evidence synthesis.

Time-intensive; can take months or longer than a year to complete.

At the top of the evidence pyramid

Meta-Analysis

Statistical technique for combining the findings from multiple quantitative studies.

Uses statistical methods to objectively evaluate, synthesize, and summarize results.

Scoping Review (or Evidence Map)

Addresses the scope of the existing literature on broad, complex, or exploratory research questions.

Many different study designs may be applicable.

Seeks to identify research gaps and opportunities for evidence synthesis.

May critically evaluate existing evidence, but does not attempt to synthesize results like a systematic review would.

Time-intensive, can take months or longer than a year to complete.

Rapid Review

Applies the methodology of a systematic review within a time-constrained setting.

Employs methodological “shortcuts” (e.g., limiting search terms) at the risk of introducing bias.

Useful for addressing issues that need a quick decision, such as developing policy recommendations or treatment recommendations for emergent conditions.

Umbrella Review

Reviews other systematic reviews on a topic.

Often attempts to answer a broader question than a systematic review typically would.

Useful when there are competing interventions to consider.

Literature (or “Narrative”) Review

A broad term that reviews with a wide scope and non-standardized methodology.

Search strategies, comprehensiveness, and time range covered may vary and do not follow an established protocol.

What Review Type?

Dr. Andrea Tricco, a leading evidence synthesis methodologist, and her team developed web-based tool to assist in selecting the right review type based on your answers to a brief list of questions. Although the tool assumes a health science topic, other disciplines may find it useful as well.

• Right Review

Main review types characterized by methods

This table summarizes the main characteristics of the 14 main review types as laid out in the seminal article on the topic. Please note that methodologies may have evolved since this article was written, so it is recommended that you review the more specific information on the following pages. Librarians can also work with you to determine the best review type for your needs.

Reproduced from: Grant, M. J. and Booth, A. (2009), A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal , 26: 91-108.  doi:10.1111/j.1471-1842.2009.00848.x  Table 1.

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Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy pp 1–15 Cite as

Methodological Approaches to Literature Review

• Dennis Thomas 2 ,
• Elida Zairina 3 &
• Johnson George 4  
• Living reference work entry
• First Online: 09 May 2023

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The literature review can serve various functions in the contexts of education and research. It aids in identifying knowledge gaps, informing research methodology, and developing a theoretical framework during the planning stages of a research study or project, as well as reporting of review findings in the context of the existing literature. This chapter discusses the methodological approaches to conducting a literature review and offers an overview of different types of reviews. There are various types of reviews, including narrative reviews, scoping reviews, and systematic reviews with reporting strategies such as meta-analysis and meta-synthesis. Review authors should consider the scope of the literature review when selecting a type and method. Being focused is essential for a successful review; however, this must be balanced against the relevance of the review to a broad audience.

• Literature review
• Systematic review
• Meta-analysis
• Scoping review
• Research methodology

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Akobeng AK. Principles of evidence based medicine. Arch Dis Child. 2005;90(8):837–40.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Alharbi A, Stevenson M. Refining Boolean queries to identify relevant studies for systematic review updates. J Am Med Inform Assoc. 2020;27(11):1658–66.

Article   PubMed   PubMed Central   Google Scholar  

Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32.

Article   Google Scholar  

Aromataris E MZE. JBI manual for evidence synthesis. 2020.

Google Scholar  

Aromataris E, Pearson A. The systematic review: an overview. Am J Nurs. 2014;114(3):53–8.

Article   PubMed   Google Scholar  

Aromataris E, Riitano D. Constructing a search strategy and searching for evidence. A guide to the literature search for a systematic review. Am J Nurs. 2014;114(5):49–56.

Babineau J. Product review: covidence (systematic review software). J Canad Health Libr Assoc Canada. 2014;35(2):68–71.

Baker JD. The purpose, process, and methods of writing a literature review. AORN J. 2016;103(3):265–9.

Bastian H, Glasziou P, Chalmers I. Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS Med. 2010;7(9):e1000326.

Bramer WM, Rethlefsen ML, Kleijnen J, Franco OH. Optimal database combinations for literature searches in systematic reviews: a prospective exploratory study. Syst Rev. 2017;6(1):1–12.

Brown D. A review of the PubMed PICO tool: using evidence-based practice in health education. Health Promot Pract. 2020;21(4):496–8.

Cargo M, Harris J, Pantoja T, et al. Cochrane qualitative and implementation methods group guidance series – paper 4: methods for assessing evidence on intervention implementation. J Clin Epidemiol. 2018;97:59–69.

Cook DJ, Mulrow CD, Haynes RB. Systematic reviews: synthesis of best evidence for clinical decisions. Ann Intern Med. 1997;126(5):376–80.

Article   CAS   PubMed   Google Scholar  

Counsell C. Formulating questions and locating primary studies for inclusion in systematic reviews. Ann Intern Med. 1997;127(5):380–7.

Cummings SR, Browner WS, Hulley SB. Conceiving the research question and developing the study plan. In: Cummings SR, Browner WS, Hulley SB, editors. Designing Clinical Research: An Epidemiological Approach. 4th ed. Philadelphia (PA): P Lippincott Williams & Wilkins; 2007. p. 14–22.

Eriksen MB, Frandsen TF. The impact of patient, intervention, comparison, outcome (PICO) as a search strategy tool on literature search quality: a systematic review. JMLA. 2018;106(4):420.

Ferrari R. Writing narrative style literature reviews. Medical Writing. 2015;24(4):230–5.

Flemming K, Booth A, Hannes K, Cargo M, Noyes J. Cochrane qualitative and implementation methods group guidance series – paper 6: reporting guidelines for qualitative, implementation, and process evaluation evidence syntheses. J Clin Epidemiol. 2018;97:79–85.

Grant MJ, Booth A. A typology of reviews: an analysis of 14 review types and associated methodologies. Health Inf Libr J. 2009;26(2):91–108.

Green BN, Johnson CD, Adams A. Writing narrative literature reviews for peer-reviewed journals: secrets of the trade. J Chiropr Med. 2006;5(3):101–17.

Gregory AT, Denniss AR. An introduction to writing narrative and systematic reviews; tasks, tips and traps for aspiring authors. Heart Lung Circ. 2018;27(7):893–8.

Harden A, Thomas J, Cargo M, et al. Cochrane qualitative and implementation methods group guidance series – paper 5: methods for integrating qualitative and implementation evidence within intervention effectiveness reviews. J Clin Epidemiol. 2018;97:70–8.

Harris JL, Booth A, Cargo M, et al. Cochrane qualitative and implementation methods group guidance series – paper 2: methods for question formulation, searching, and protocol development for qualitative evidence synthesis. J Clin Epidemiol. 2018;97:39–48.

Higgins J, Thomas J. In: Chandler J, Cumpston M, Li T, Page MJ, Welch VA, editors. Cochrane Handbook for Systematic Reviews of Interventions version 6.3, updated February 2022). Available from www.training.cochrane.org/handbook.: Cochrane; 2022.

International prospective register of systematic reviews (PROSPERO). Available from https://www.crd.york.ac.uk/prospero/ .

Khan KS, Kunz R, Kleijnen J, Antes G. Five steps to conducting a systematic review. J R Soc Med. 2003;96(3):118–21.

Landhuis E. Scientific literature: information overload. Nature. 2016;535(7612):457–8.

Lockwood C, Porritt K, Munn Z, Rittenmeyer L, Salmond S, Bjerrum M, Loveday H, Carrier J, Stannard D. Chapter 2: Systematic reviews of qualitative evidence. In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis. JBI; 2020. Available from https://synthesismanual.jbi.global . https://doi.org/10.46658/JBIMES-20-03 .

Chapter   Google Scholar  

Lorenzetti DL, Topfer L-A, Dennett L, Clement F. Value of databases other than medline for rapid health technology assessments. Int J Technol Assess Health Care. 2014;30(2):173–8.

Moher D, Liberati A, Tetzlaff J, Altman DG, the PRISMA Group. Preferred reporting items for (SR) and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;6:264–9.

Mulrow CD. Systematic reviews: rationale for systematic reviews. BMJ. 1994;309(6954):597–9.

Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18(1):143.

Munthe-Kaas HM, Glenton C, Booth A, Noyes J, Lewin S. Systematic mapping of existing tools to appraise methodological strengths and limitations of qualitative research: first stage in the development of the CAMELOT tool. BMC Med Res Methodol. 2019;19(1):1–13.

Murphy CM. Writing an effective review article. J Med Toxicol. 2012;8(2):89–90.

NHMRC. Guidelines for guidelines: assessing risk of bias. Available at https://nhmrc.gov.au/guidelinesforguidelines/develop/assessing-risk-bias . Last published 29 August 2019. Accessed 29 Aug 2022.

Noyes J, Booth A, Cargo M, et al. Cochrane qualitative and implementation methods group guidance series – paper 1: introduction. J Clin Epidemiol. 2018b;97:35–8.

Noyes J, Booth A, Flemming K, et al. Cochrane qualitative and implementation methods group guidance series – paper 3: methods for assessing methodological limitations, data extraction and synthesis, and confidence in synthesized qualitative findings. J Clin Epidemiol. 2018a;97:49–58.

Noyes J, Booth A, Moore G, Flemming K, Tunçalp Ö, Shakibazadeh E. Synthesising quantitative and qualitative evidence to inform guidelines on complex interventions: clarifying the purposes, designs and outlining some methods. BMJ Glob Health. 2019;4(Suppl 1):e000893.

Peters MD, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid Healthcare. 2015;13(3):141–6.

Polanin JR, Pigott TD, Espelage DL, Grotpeter JK. Best practice guidelines for abstract screening large-evidence systematic reviews and meta-analyses. Res Synth Methods. 2019;10(3):330–42.

Article   PubMed Central   Google Scholar  

Shea BJ, Grimshaw JM, Wells GA, et al. Development of AMSTAR: a measurement tool to assess the methodological quality of systematic reviews. BMC Med Res Methodol. 2007;7(1):1–7.

Shea BJ, Reeves BC, Wells G, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. Brit Med J. 2017;358

Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. Br Med J. 2016;355

Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. JAMA. 2000;283(15):2008–12.

Tawfik GM, Dila KAS, Mohamed MYF, et al. A step by step guide for conducting a systematic review and meta-analysis with simulation data. Trop Med Health. 2019;47(1):1–9.

The Critical Appraisal Program. Critical appraisal skills program. Available at https://casp-uk.net/ . 2022. Accessed 29 Aug 2022.

The University of Melbourne. Writing a literature review in Research Techniques 2022. Available at https://students.unimelb.edu.au/academic-skills/explore-our-resources/research-techniques/reviewing-the-literature . Accessed 29 Aug 2022.

The Writing Center University of Winconsin-Madison. Learn how to write a literature review in The Writer’s Handbook – Academic Professional Writing. 2022. Available at https://writing.wisc.edu/handbook/assignments/reviewofliterature/ . Accessed 29 Aug 2022.

Thompson SG, Sharp SJ. Explaining heterogeneity in meta-analysis: a comparison of methods. Stat Med. 1999;18(20):2693–708.

Tricco AC, Lillie E, Zarin W, et al. A scoping review on the conduct and reporting of scoping reviews. BMC Med Res Methodol. 2016;16(1):15.

Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73.

Yoneoka D, Henmi M. Clinical heterogeneity in random-effect meta-analysis: between-study boundary estimate problem. Stat Med. 2019;38(21):4131–45.

Yuan Y, Hunt RH. Systematic reviews: the good, the bad, and the ugly. Am J Gastroenterol. 2009;104(5):1086–92.

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Elida Zairina

Centre for Medicine Use and Safety, Monash Institute of Pharmaceutical Sciences, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia

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Thomas, D., Zairina, E., George, J. (2023). Methodological Approaches to Literature Review. In: Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy. Springer, Cham. https://doi.org/10.1007/978-3-030-50247-8_57-1

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Methods for the synthesis of qualitative research: a critical review

Elaine barnett-page.

1 Evidence for Policy and Practice Information and Co-ordinating (EPPI-) Centre, Social Science Research Unit, 18 Woburn Square, London WC1H 0NS, UK

James Thomas

Associated data.

In recent years, a growing number of methods for synthesising qualitative research have emerged, particularly in relation to health-related research. There is a need for both researchers and commissioners to be able to distinguish between these methods and to select which method is the most appropriate to their situation.

A number of methodological and conceptual links between these methods were identified and explored, while contrasting epistemological positions explained differences in approaches to issues such as quality assessment and extent of iteration. Methods broadly fall into 'realist' or 'idealist' epistemologies, which partly accounts for these differences.

Methods for qualitative synthesis vary across a range of dimensions. Commissioners of qualitative syntheses might wish to consider the kind of product they want and select their method – or type of method – accordingly.

The range of different methods for synthesising qualitative research has been growing over recent years [ 1 , 2 ], alongside an increasing interest in qualitative synthesis to inform health-related policy and practice [ 3 ]. While the terms 'meta-analysis' (a statistical method to combine the results of primary studies), or sometimes 'narrative synthesis', are frequently used to describe how quantitative research is synthesised, far more terms are used to describe the synthesis of qualitative research. This profusion of terms can mask some of the basic similarities in approach that the different methods share, and also lead to some confusion regarding which method is most appropriate in a given situation. This paper does not argue that the various nomenclatures are unnecessary, but rather seeks to draw together and review the full range of methods of synthesis available to assist future reviewers in selecting a method that is fit for their purpose. It also represents an attempt to guide the reader through some of the varied terminology to spring up around qualitative synthesis. Other helpful reviews of synthesis methods have been undertaken in recent years with slightly different foci to this paper. Two recent studies have focused on describing and critiquing methods for the integration of qualitative research with quantitative [ 4 , 5 ] rather than exclusively examining the detail and rationale of methods for the synthesis of qualitative research. Two other significant pieces of work give practical advice for conducting the synthesis of qualitative research, but do not discuss the full range of methods available [ 6 , 7 ]. We begin our Discussion by outlining each method of synthesis in turn, before comparing and contrasting characteristics of these different methods across a range of dimensions. Readers who are more familiar with the synthesis methods described here may prefer to turn straight to the 'dimensions of difference' analysis in the second part of the Discussion.

Overview of synthesis methods

Meta-ethnography.

In their seminal work of 1988, Noblit and Hare proposed meta-ethnography as an alternative to meta-analysis [ 8 ]. They cited Strike and Posner's [ 9 ] definition of synthesis as an activity in which separate parts are brought together to form a 'whole'; this construction of the whole is essentially characterised by some degree of innovation, so that the result is greater than the sum of its parts. They also borrowed from Turner's theory of social explanation [ 10 ], a key tenet of which was building 'comparative understanding' [[ 8 ], p22] rather than aggregating data.

To Noblit and Hare, synthesis provided an answer to the question of 'how to "put together" written interpretive accounts' [[ 8 ], p7], where mere integration would not be appropriate. Noblit and Hare's early work synthesised research from the field of education.

Three different methods of synthesis are used in meta-ethnography. One involves the 'translation' of concepts from individual studies into one another, thereby evolving overarching concepts or metaphors. Noblit and Hare called this process reciprocal translational analysis (RTA). Refutational synthesis involves exploring and explaining contradictions between individual studies. Lines-of-argument (LOA) synthesis involves building up a picture of the whole (i.e. culture, organisation etc) from studies of its parts. The authors conceptualised this latter approach as a type of grounded theorising.

Britten et al [ 11 ] and Campbell et al [ 12 ] have both conducted evaluations of meta-ethnography and claim to have succeeded, by using this method, in producing theories with greater explanatory power than could be achieved in a narrative literature review. While both these evaluations used small numbers of studies, more recently Pound et al [ 13 ] conducted both an RTA and an LOA synthesis using a much larger number of studies (37) on resisting medicines. These studies demonstrate that meta-ethnography has evolved since Noblit and Hare first introduced it. Campbell et al claim to have applied the method successfully to non-ethnographical studies. Based on their reading of Schutz [ 14 ], Britten et al have developed both second and third order constructs in their synthesis (Noblit and Hare briefly allude to the possibility of a 'second level of synthesis' [[ 8 ], p28] but do not demonstrate or further develop the idea).

In a more recent development, Sandelowski & Barroso [ 15 ] write of adapting RTA by using it to ' integrate findings interpretively, as opposed to comparing them interpretively' (p204). The former would involve looking to see whether the same concept, theory etc exists in different studies; the latter would involve the construction of a bigger picture or theory (i.e. LOA synthesis). They also talk about comparing or integrating imported concepts (e.g. from other disciplines) as well as those evolved 'in vivo'.

Grounded theory

Kearney [ 16 ], Eaves [ 17 ] and Finfgeld [ 18 ] have all adapted grounded theory to formulate a method of synthesis. Key methods and assumptions of grounded theory, as originally formulated and subsequently refined by Glaser and Strauss [ 19 ] and Strauss and Corbin [ 20 , 21 ], include: simultaneous phases of data collection and analysis; an inductive approach to analysis, allowing the theory to emerge from the data; the use of the constant comparison method; the use of theoretical sampling to reach theoretical saturation; and the generation of new theory. Eaves cited grounded theorists Charmaz [ 22 ] and Chesler [ 23 ], as well as Strauss and Corbin [ 20 ], as informing her approach to synthesis.

Glaser and Strauss [ 19 ] foresaw a time when a substantive body of grounded research should be pushed towards a higher, more abstract level. As a piece of methodological work, Eaves undertook her own synthesis of the synthesis methods used by these authors to produce her own clear and explicit guide to synthesis in grounded formal theory. Kearney stated that 'grounded formal theory', as she termed this method of synthesis, 'is suited to study of phenomena involving processes of contextualized understanding and action' [[ 24 ], p180] and, as such, is particularly applicable to nurses' research interests.

As Kearney suggested, the examples examined here were largely dominated by research in nursing. Eaves synthesised studies on care-giving in rural African-American families for elderly stroke survivors; Finfgeld on courage among individuals with long-term health problems; Kearney on women's experiences of domestic violence.

Kearney explicitly chose 'grounded formal theory' because it matches 'like' with 'like': that is, it applies the same methods that have been used to generate the original grounded theories included in the synthesis – produced by constant comparison and theoretical sampling – to generate a higher-level grounded theory. The wish to match 'like' with 'like' is also implicit in Eaves' paper. This distinguishes grounded formal theory from more recent applications of meta-ethnography, which have sought to include qualitative research using diverse methodological approaches [ 12 ].

Thematic Synthesis

Thomas and Harden [ 25 ] have developed an approach to synthesis which they term 'thematic synthesis'. This combines and adapts approaches from both meta-ethnography and grounded theory. The method was developed out of a need to conduct reviews that addressed questions relating to intervention need, appropriateness and acceptability – as well as those relating to effectiveness – without compromising on key principles developed in systematic reviews. They applied thematic synthesis in a review of the barriers to, and facilitators of, healthy eating amongst children.

Free codes of findings are organised into 'descriptive' themes, which are then further interpreted to yield 'analytical' themes. This approach shares characteristics with later adaptations of meta-ethnography, in that the analytical themes are comparable to 'third order interpretations' and that the development of descriptive and analytical themes using coding invoke reciprocal 'translation'. It also shares much with grounded theory, in that the approach is inductive and themes are developed using a 'constant comparison' method. A novel aspect of their approach is the use of computer software to code the results of included studies line-by-line, thus borrowing another technique from methods usually used to analyse primary research.

Textual Narrative Synthesis

Textual narrative synthesis is an approach which arranges studies into more homogenous groups. Lucas et al [ 26 ] comment that it has proved useful in synthesising evidence of different types (qualitative, quantitative, economic etc). Typically, study characteristics, context, quality and findings are reported on according to a standard format and similarities and differences are compared across studies. Structured summaries may also be developed, elaborating on and putting into context the extracted data [ 27 ].

Lucas et al [ 26 ] compared thematic synthesis with textual narrative synthesis. They found that 'thematic synthesis holds most potential for hypothesis generation' whereas textual narrative synthesis is more likely to make transparent heterogeneity between studies (as does meta-ethnography, with refutational synthesis) and issues of quality appraisal. This is possibly because textual narrative synthesis makes clearer the context and characteristics of each study, while the thematic approach organises data according to themes. However, Lucas et al found that textual narrative synthesis is 'less good at identifying commonality' (p2); the authors do not make explicit why this should be, although it may be that organising according to themes, as the thematic approach does, is comparatively more successful in revealing commonality.

Paterson et al [ 28 ] have evolved a multi-faceted approach to synthesis, which they call 'meta-study'. The sociologist Zhao [ 29 ], drawing on Ritzer's work [ 30 ], outlined three components of analysis, which they proposed should be undertaken prior to synthesis. These are meta-data-analysis (the analysis of findings), meta-method (the analysis of methods) and meta-theory (the analysis of theory). Collectively, these three elements of analysis, culminating in synthesis, make up the practice of 'meta-study'. Paterson et al pointed out that the different components of analysis may be conducted concurrently.

Paterson et al argued that primary research is a construction; secondary research is therefore a construction of a construction. There is need for an approach that recognises this, and that also recognises research to be a product of its social, historical and ideological context. Such an approach would be useful in accounting for differences in research findings. For Paterson et al, there is no such thing as 'absolute truth'.

Meta-study was developed to study the experiences of adults living with a chronic illness. Meta-data-analysis was conceived of by Paterson et al in similar terms to Noblit and Hare's meta-ethnography (see above), in that it is essentially interpretive and seeks to reveal similarities and discrepancies among accounts of a particular phenomenon. Meta-method involves the examination of the methodologies of the individual studies under review. Part of the process of meta-method is to consider different aspects of methodology such as sampling, data collection, research design etc, similar to procedures others have called 'critical appraisal' (CASP [ 31 ]). However, Paterson et al take their critique to a deeper level by establishing the underlying assumptions of the methodologies used and the relationship between research outcomes and methods used. Meta-theory involves scrutiny of the philosophical and theoretical assumptions of the included research papers; this includes looking at the wider context in which new theory is generated. Paterson et al described meta-synthesis as a process which creates a new interpretation which accounts for the results of all three elements of analysis. The process of synthesis is iterative and reflexive and the authors were unwilling to oversimplify the process by 'codifying' procedures for bringing all three components of analysis together.

Meta-narrative

Greenhalgh et al [ 32 ]'s meta-narrative approach to synthesis arose out of the need to synthesise evidence to inform complex policy-making questions and was assisted by the formation of a multi-disciplinary team. Their approach to review was informed by Thomas Kuhn's The Structure of Scientific Revolutions [ 33 ], in which he proposed that knowledge is produced within particular paradigms which have their own assumptions about theory, about what is a legitimate object of study, about what are legitimate research questions and about what constitutes a finding. Paradigms also tend to develop through time according to a particular set of stages, central to which is the stage of 'normal science', in which the particular standards of the paradigm are largely unchallenged and seen to be self-evident. As Greenhalgh et al pointed out, Kuhn saw paradigms as largely incommensurable: 'that is, an empirical discovery made using one set of concepts, theories, methods and instruments cannot be satisfactorily explained through a different paradigmatic lens' [[ 32 ], p419].

Greenhalgh et al synthesised research from a wide range of disciplines; their research question related to the diffusion of innovations in health service delivery and organisation. They thus identified a need to synthesise findings from research which contains many different theories arising from many different disciplines and study designs.

Based on Kuhn's work, Greenhalgh et al proposed that, across different paradigms, there were multiple – and potentially mutually contradictory – ways of understanding the concept at the heart of their review, namely the diffusion of innovation. Bearing this in mind, the reviewers deliberately chose to select key papers from a number of different research 'paradigms' or 'traditions', both within and beyond healthcare, guided by their multidisciplinary research team. They took as their unit of analysis the 'unfolding "storyline" of a research tradition over time' [[ 32 ], p417) and sought to understand diffusion of innovation as it was conceptualised in each of these traditions. Key features of each tradition were mapped: historical roots, scope, theoretical basis; research questions asked and methods/instruments used; main empirical findings; historical development of the body of knowledge (how have earlier findings led to later findings); and strengths and limitations of the tradition. The results of this exercise led to maps of 13 'meta-narratives' in total, from which seven key dimensions, or themes, were identified and distilled for the synthesis phase of the review.

Critical Interpretive Synthesis

Dixon-Woods et al [ 34 ] developed their own approach to synthesising multi-disciplinary and multi-method evidence, termed 'critical interpretive synthesis', while researching access to healthcare by vulnerable groups. Critical interpretive synthesis is an adaptation of meta-ethnography, as well as borrowing techniques from grounded theory. The authors stated that they needed to adapt traditional meta-ethnographic methods for synthesis, since these had never been applied to quantitative as well as qualitative data, nor had they been applied to a substantial body of data (in this case, 119 papers).

Dixon-Woods et al presented critical interpretive synthesis as an approach to the whole process of review, rather than to just the synthesis component. It involves an iterative approach to refining the research question and searching and selecting from the literature (using theoretical sampling) and defining and applying codes and categories. It also has a particular approach to appraising quality, using relevance – i.e. likely contribution to theory development – rather than methodological characteristics as a means of determining the 'quality' of individual papers [ 35 ]. The authors also stress, as a defining characteristic, critical interpretive synthesis's critical approach to the literature in terms of deconstructing research traditions or theoretical assumptions as a means of contextualising findings.

Dixon-Woods et al rejected reciprocal translational analysis (RTA) as this produced 'only a summary in terms that have already been used in the literature' [[ 34 ], p5], which was seen as less helpful when dealing with a large and diverse body of literature. Instead, Dixon-Woods et al adopted a lines-of-argument (LOA) synthesis, in which – rejecting the difference between first, second and third order constructs – they instead developed 'synthetic constructs' which were then linked with constructs arising directly from the literature.

The influence of grounded theory can be seen in particular in critical interpretive synthesis's inductive approach to formulating the review question and to developing categories and concepts, rejecting a 'stage' approach to systematic reviewing, and in selecting papers using theoretical sampling. Dixon-Woods et al also claim that critical interpretive synthesis is distinct in its 'explicit orientation towards theory generation' [[ 34 ], p9].

Ecological Triangulation

Jim Banning is the author of 'ecological triangulation' or 'ecological sentence synthesis', applying this method to the evidence for what works for youth with disabilities. He borrows from Webb et al [ 36 ] and Denzin [ 37 ] the concept of triangulation, in which phenomena are studied from a variety of vantage points. His rationale is that building an 'evidence base' of effectiveness requires the synthesis of cumulative, multi-faceted evidence in order to find out 'what intervention works for what kind of outcomes for what kind of persons under what kind of conditions' [[ 38 ], p1].

Ecological triangulation unpicks the mutually interdependent relationships between behaviour, persons and environments. The method requires that, for data extraction and synthesis, 'ecological sentences' are formulated following the pattern: 'With this intervention, these outcomes occur with these population foci and within these grades (ages), with these genders ... and these ethnicities in these settings' [[ 39 ], p1].

Framework Synthesis

Brunton et al [ 40 ] and Oliver et al [ 41 ] have applied a 'framework synthesis' approach in their reviews. Framework synthesis is based on framework analysis, which was outlined by Pope, Ziebland and Mays [ 42 ], and draws upon the work of Ritchie and Spencer [ 43 ] and Miles and Huberman [ 44 ]. Its rationale is that qualitative research produces large amounts of textual data in the form of transcripts, observational fieldnotes etc. The sheer wealth of information poses a challenge for rigorous analysis. Framework synthesis offers a highly structured approach to organising and analysing data (e.g. indexing using numerical codes, rearranging data into charts etc).

Brunton et al applied the approach to a review of children's, young people's and parents' views of walking and cycling; Oliver et al to an analysis of public involvement in health services research. Framework synthesis is distinct from the other methods outlined here in that it utilises an a priori 'framework' – informed by background material and team discussions – to extract and synthesise findings. As such, it is largely a deductive approach although, in addition to topics identified by the framework, new topics may be developed and incorporated as they emerge from the data. The synthetic product can be expressed in the form of a chart for each key dimension identified, which may be used to map the nature and range of the concept under study and find associations between themes and exceptions to these [ 40 ].

'Fledgling' approaches

There are three other approaches to synthesis which have not yet been widely used. One is an approach using content analysis [ 45 , 46 ] in which text is condensed into fewer content-related categories. Another is 'meta-interpretation' [ 47 ], featuring the following: an ideographic rather than pre-determined approach to the development of exclusion criteria; a focus on meaning in context; interpretations as raw data for synthesis (although this feature doesn't distinguish it from other synthesis methods); an iterative approach to the theoretical sampling of studies for synthesis; and a transparent audit trail demonstrating the trustworthiness of the synthesis.

In addition to the synthesis methods discussed above, Sandelowski and Barroso propose a method they call 'qualitative metasummary' [ 15 ]. It is mentioned here as a new and original approach to handling a collection of qualitative studies but is qualitatively different to the other methods described here since it is aggregative; that is, findings are accumulated and summarised rather than 'transformed'. Metasummary is a way of producing a 'map' of the contents of qualitative studies and – according to Sandelowski and Barroso – 'reflect [s] a quantitative logic' [[ 15 ], p151]. The frequency of each finding is determined and the higher the frequency of a particular finding, the greater its validity. The authors even discuss the calculation of 'effect sizes' for qualitative findings. Qualitative metasummaries can be undertaken as an end in themselves or may serve as a basis for a further synthesis.

Dimensions of difference

Having outlined the range of methods identified, we now turn to an examination of how they compare with one another. It is clear that they have come from many different contexts and have different approaches to understanding knowledge, but what do these differences mean in practice? Our framework for this analysis is shown in Additional file 1 : dimensions of difference [ 48 ]. We have examined the epistemology of each of the methods and found that, to some extent, this explains the need for different methods and their various approaches to synthesis.

Epistemology

The first dimension that we will consider is that of the researchers' epistemological assumptions. Spencer et al [ 49 ] outline a range of epistemological positions, which might be organised into a spectrum as follows:

Subjective idealism : there is no shared reality independent of multiple alternative human constructions

Objective idealism : there is a world of collectively shared understandings

Critical realism : knowledge of reality is mediated by our perceptions and beliefs

Scientific realism : it is possible for knowledge to approximate closely an external reality

Naïve realism : reality exists independently of human constructions and can be known directly [ 49 , 45 , 46 ].

Thus, at one end of the spectrum we have a highly constructivist view of knowledge and, at the other, an unproblematized 'direct window onto the world' view.

Nearly all of positions along this spectrum are represented in the range of methodological approaches to synthesis covered in this paper. The originators of meta-narrative synthesis, critical interpretive synthesis and meta-study all articulate what might be termed a 'subjective idealist' approach to knowledge. Paterson et al [ 28 ] state that meta-study shies away from creating 'grand theories' within the health or social sciences and assume that no single objective reality will be found. Primary studies, they argue, are themselves constructions; meta-synthesis, then, 'deals with constructions of constructions' (p7). Greenhalgh et al [ 32 ] also view knowledge as a product of its disciplinary paradigm and use this to explain conflicting findings: again, the authors neither seek, nor expect to find, one final, non-contestable answer to their research question. Critical interpretive synthesis is similar in seeking to place literature within its context, to question its assumptions and to produce a theoretical model of a phenomenon which – because highly interpretive – may not be reproducible by different research teams at alternative points in time [[ 34 ], p11].

Methods used to synthesise grounded theory studies in order to produce a higher level of grounded theory [ 24 ] appear to be informed by 'objective idealism', as does meta-ethnography. Kearney argues for the near-universal applicability of a 'ready-to-wear' theory across contexts and populations. This approach is clearly distinct from one which recognises multiple realities. The emphasis is on examining commonalities amongst, rather than discrepancies between, accounts. This emphasis is similarly apparent in most meta-ethnographies, which are conducted either according to Noblit and Hare's 'reciprocal translational analysis' technique or to their 'lines-of-argument' technique and which seek to provide a 'whole' which has a greater explanatory power. Although Noblit and Hare also propose 'refutational synthesis', in which contradictory findings might be explored, there are few examples of this having been undertaken in practice, and the aim of the method appears to be to explain and explore differences due to context, rather than multiple realities.

Despite an assumption of a reality which is perhaps less contestable than those of meta-narrative synthesis, critical interpretive synthesis and meta-study, both grounded formal theory and meta-ethnography place a great deal of emphasis on the interpretive nature of their methods. This still supposes a degree of constructivism. Although less explicit about how their methods are informed, it seems that both thematic synthesis and framework synthesis – while also involving some interpretation of data – share an even less problematized view of reality and a greater assumption that their synthetic products are reproducible and correspond to a shared reality. This is also implicit in the fact that such products are designed directly to inform policy and practice, a characteristic shared by ecological triangulation. Notably, ecological triangulation, according to Banning, can be either realist or idealist. Banning argues that the interpretation of triangulation can either be one in which multiple viewpoints converge on a point to produce confirming evidence (i.e. one definitive answer to the research question) or an idealist one, in which the complexity of multiple viewpoints is represented. Thus, although ecological triangulation views reality as complex, the approach assumes that it can be approximately knowable (at least when the realist view of ecological triangulation is adopted) and that interventions can and should be modelled according to the products of its syntheses.

While pigeonholing different methods into specific epistemological positions is a problematic process, we do suggest that the contrasting epistemologies of different researchers is one way of explaining why we have – and need – different methods for synthesis.

Variation in terms of the extent of iteration during the review process is another key dimension. All synthesis methods include some iteration but the degree varies. Meta-ethnography, grounded theory and thematic synthesis all include iteration at the synthesis stage; both framework synthesis and critical interpretive synthesis involve iterative literature searching – in the case of critical interpretive synthesis, it is not clear whether iteration occurs during the rest of the review process. Meta-narrative also involves iteration at every stage. Banning does not mention iteration in outlining ecological triangulation and neither do Lucas or Thomas and Harden for thematic narrative synthesis.

It seems that the more idealist the approach, the greater the extent of iteration. This might be because a large degree of iteration does not sit well with a more 'positivist' ideal of procedural objectivity; in particular, the notion that the robustness of the synthetic product depends in part on the reviewers stating up front in a protocol their searching strategies, inclusion/exclusion criteria etc, and being seen not to alter these at a later stage.

Quality assessment

Another dimension along which we can look at different synthesis methods is that of quality assessment. When the approaches to the assessment of the quality of studies retrieved for review are examined, there is again a wide methodological variation. It might be expected that the further towards the 'realism' end of the epistemological spectrum a method of synthesis falls, the greater the emphasis on quality assessment. In fact, this is only partially the case.

Framework synthesis, thematic narrative synthesis and thematic synthesis – methods which might be classified as sharing a 'critical realist' approach – all have highly specified approaches to quality assessment. The review in which framework synthesis was developed applied ten quality criteria: two on quality and reporting of sampling methods, four to the quality of the description of the sample in the study, two to the reliability and validity of the tools used to collect data and one on whether studies used appropriate methods for helping people to express their views. Studies which did not meet a certain number of quality criteria were excluded from contributing to findings. Similarly, in the example review for thematic synthesis, 12 criteria were applied: five related to reporting aims, context, rationale, methods and findings; four relating to reliability and validity; and three relating to the appropriateness of methods for ensuring that findings were rooted in participants' own perspectives. Studies which were deemed to have significant flaws were excluded and sensitivity analyses were used to assess the possible impact of study quality on the review's findings. Thomas and Harden's use of thematic narrative synthesis similarly applied quality criteria and developed criteria additional to those they found in the literature on quality assessment, relating to the extent to which people's views and perspectives had been privileged by researchers. It is worth noting not only that these methods apply quality criteria but that they are explicit about what they are: assessing quality is a key component in the review process for both of these methods. Likewise, Banning – the originator of ecological triangulation – sees quality assessment as important and adapts the Design and Implementation Assessment Device (DIAD) Version 0.3 (a quality assessment tool for quantitative research) for use when appraising qualitative studies [ 50 ]. Again, Banning writes of excluding studies deemed to be of poor quality.

Greenhalgh et al's meta-narrative review [ 32 ] modified a range of existing quality assessment tools to evaluate studies according to validity and robustness of methods; sample size and power; and validity of conclusions. The authors imply, but are not explicit, that this process formed the basis for the exclusion of some studies. Although not quite so clear about quality assessment methods as framework and thematic synthesis, it might be argued that meta-narrative synthesis shows a greater commitment to the concept that research can and should be assessed for quality than either meta-ethnography or grounded formal theory. The originators of meta-ethnography, Noblit and Hare [ 8 ], originally discussed quality in terms of quality of metaphor, while more recent use of this method has used amended versions of CASP (the Critical Appraisal Skills Programme tool, [ 31 ]), yet has only referred to studies being excluded on the basis of lack of relevance or because they weren't 'qualitative' studies [ 8 ]. In grounded theory, quality assessment is only discussed in terms of a 'personal note' being made on the context, quality and usefulness of each study. However, contrary to expectation, meta-narrative synthesis lies at the extreme end of the idealism/realism spectrum – as a subjective idealist approach – while meta-ethnography and grounded theory are classified as objective idealist approaches.

Finally, meta-study and critical interpretive synthesis – two more subjective idealist approaches – look to the content and utility of findings rather than methodology in order to establish quality. While earlier forms of meta-study included only studies which demonstrated 'epistemological soundness', in its most recent form [ 51 ] this method has sought to include all relevant studies, excluding only those deemed not to be 'qualitative' research. Critical interpretive synthesis also conforms to what we might expect of its approach to quality assessment: quality of research is judged as the extent to which it informs theory. The threshold of inclusion is informed by expertise and instinct rather than being articulated a priori.

In terms of quality assessment, it might be important to consider the academic context in which these various methods of synthesis developed. The reason why thematic synthesis, framework synthesis and ecological triangulation have such highly specified approaches to quality assessment may be that each of these was developed for a particular task, i.e. to conduct a multi-method review in which randomised controlled trials (RCTs) were included. The concept of quality assessment in relation to RCTs is much less contested and there is general agreement on criteria against which quality should be judged.

Problematizing the literature

Critical interpretive synthesis, the meta-narrative approach and the meta-theory element of meta-study all share some common ground in that their review and synthesis processes include examining all aspects of the context in which knowledge is produced. In conducting a review on access to healthcare by vulnerable groups, critical interpretive synthesis sought to question 'the ways in which the literature had constructed the problematics of access, the nature of the assumptions on which it drew, and what has influenced its choice of proposed solutions' [[ 34 ], p6]. Although not claiming to have been directly influenced by Greenhalgh et al's meta-narrative approach, Dixon-Woods et al do cite it as sharing similar characteristics in the sense that it critiques the literature it reviews.

Meta-study uses meta-theory to describe and deconstruct the theories that shape a body of research and to assess its quality. One aspect of this process is to examine the historical evolution of each theory and to put it in its socio-political context, which invites direct comparison with meta-narrative synthesis. Greenhalgh et al put a similar emphasis on placing research findings within their social and historical context, often as a means of seeking to explain heterogeneity of findings. In addition, meta-narrative shares with critical interpretive synthesis an iterative approach to searching and selecting from the literature.

Framework synthesis, thematic synthesis, textual narrative synthesis, meta-ethnography and grounded theory do not share the same approach to problematizing the literature as critical interpretive synthesis, meta-study and meta-narrative. In part, this may be explained by the extent to which studies included in the synthesis represented a broad range of approaches or methodologies. This, in turn, may reflect the broadness of the review question and the extent to which the concepts contained within the question are pre-defined within the literature. In the case of both the critical interpretive synthesis and meta-narrative reviews, terminology was elastic and/or the question formed iteratively. Similarly, both reviews placed great emphasis on employing multi-disciplinary research teams. Approaches which do not critique the literature in the same way tend to have more narrowly-focused questions. They also tend to include a more limited range of studies: grounded theory synthesis includes grounded theory studies, meta-ethnography (in its original form, as applied by Noblit and Hare) ethnographies. The thematic synthesis incorporated studies based on only a narrow range of qualitative methodologies (interviews and focus groups) which were informed by a similarly narrow range of epistemological assumptions. It may be that the authors of such syntheses saw no need for including such a critique in their review process.

Similarities and differences between primary studies

Most methods of synthesis are applicable to heterogeneous data (i.e. studies which use contrasting methodologies) apart from early meta-ethnography and synthesis informed by grounded theory. All methods of synthesis state that, at some level, studies are compared; many are not so explicit about how this is done, though some are. Meta-ethnography is one of the most explicit: it describes the act of 'translation' where terms and concepts which have resonance with one another are subsumed into 'higher order constructs'. Grounded theory, as represented by Eaves [ 17 ], is undertaken according to a long list of steps and sub-steps, includes the production of generalizations about concepts/categories, which comes from classifying these categories. In meta-narrative synthesis, comparable studies are grouped together at the appraisal phase of review.

Perhaps more interesting are the ways in which differences between studies are explored. Those methods with a greater emphasis on critical appraisal may tend (although this is not always made explicit) to use differences in method to explain differences in finding. Meta-ethnography proposes 'refutational synthesis' to explain differences, although there are few examples of this in the literature. Some synthesis methods – for example, thematic synthesis – look at other characteristics of the studies under review, whether types of participants and their context vary, and whether this can explain differences in perspective.

All of these methods, then, look within the studies to explain differences. Other methods look beyond the study itself to the context in which it was produced. Critical interpretive synthesis and meta-study look at differences in theory or in socio-economic context. Critical interpretive synthesis, like meta-narrative, also explores epistemological orientation. Meta-narrative is unique in concerning itself with disciplinary paradigm (i.e. the story of the discipline as it progresses). It is also distinctive in that it treats conflicting findings as 'higher order data' [[ 32 ], p420], so that the main emphasis of the synthesis appears to be on examining and explaining contradictions in the literature.

Going 'beyond' the primary studies

Synthesis is sometimes defined as a process resulting in a product, a 'whole', which is more than the sum of its parts. However, the methods reviewed here vary in the extent to which they attempt to 'go beyond' the primary studies and transform the data. Some methods – textual narrative synthesis, ecological triangulation and framework synthesis – focus on describing and summarising their primary data (often in a highly structured and detailed way) and translating the studies into one another. Others – meta-ethnography, grounded theory, thematic synthesis, meta-study, meta-narrative and critical interpretive synthesis – seek to push beyond the original data to a fresh interpretation of the phenomena under review. A key feature of thematic synthesis is its clear differentiation between these two stages.

Different methods have different mechanisms for going beyond the primary studies, although some are more explicit than others about what these entail. Meta-ethnography proposes a 'Line of Argument' (LOA) synthesis in which an interpretation is constructed to both link and explain a set of parts. Critical interpretive synthesis based its synthesis methods on those of meta-ethnography, developing an LOA using what the authors term 'synthetic constructs' (akin to 'third order constructs' in meta-ethnography) to create a 'synthesising argument'. Dixon-Woods et al claim that this is an advance on Britten et al's methods, in that they reject the difference between first, second and third order constructs.

Meta-narrative, as outlined above, focuses on conflicting findings and constructs theories to explain these in terms of differing paradigms. Meta study derives questions from each of its three components to which it subjects the dataset and inductively generates a number of theoretical claims in relation to it. According to Eaves' model of grounded theory [ 17 ], mini-theories are integrated to produce an explanatory framework. In ecological triangulation, the 'axial' codes – or second level codes evolved from the initial deductive open codes – are used to produce Banning's 'ecological sentence' [ 39 ].

The synthetic product

In overviewing and comparing different qualitative synthesis methods, the ultimate question relates to the utility of the synthetic product: what is it for? It is clear that some methods of synthesis – namely, thematic synthesis, textual narrative synthesis, framework synthesis and ecological triangulation – view themselves as producing an output that is directly applicable to policy makers and designers of interventions. The example of framework synthesis examined here (on children's, young people's and parents' views of walking and cycling) involved policy makers and practitioners in directing the focus of the synthesis and used the themes derived from the synthesis to infer what kind of interventions might be most effective in encouraging walking and cycling. Likewise, the products of the thematic synthesis took the form of practical recommendations for interventions (e.g. 'do not promote fruit and vegetables in the same way in the same intervention'). The extent to which policy makers and practitioners are involved in informing either synthesis or recommendation is less clear from the documents published on ecological triangulation, but the aim certainly is to directly inform practice.

The outputs of synthesis methods which have a more constructivist orientation – meta-study, meta-narrative, meta-ethnography, grounded theory, critical interpretive synthesis – tend to look rather different. They are generally more complex and conceptual, sometimes operating on the symbolic or metaphorical level, and requiring a further process of interpretation by policy makers and practitioners in order for them to inform practice. This is not to say, however, that they are not useful for practice, more that they are doing different work. However, it may be that, in the absence of further interpretation, they are more useful for informing other researchers and theoreticians.

Looking across dimensions

After examining the dimensions of difference of our included methods, what picture ultimately emerges? It seems clear that, while similar in some respects, there are genuine differences in approach to the synthesis of what is essentially textual data. To some extent, these differences can be explained by the epistemological assumptions that underpin each method. Our methods split into two broad camps: the idealist and the realist (see Table ​ Table1 1 for a summary). Idealist approaches generally tend to have a more iterative approach to searching (and the review process), have less a priori quality assessment procedures and are more inclined to problematize the literature. Realist approaches are characterised by a more linear approach to searching and review, have clearer and more well-developed approaches to quality assessment, and do not problematize the literature.

Summary table

N.B.: In terms of the above dimensions, it is generally a question of degree rather than of absolute distinctions.

Mapping the relationships between methods

What is interesting is the relationship between these methods of synthesis, the conceptual links between them, and the extent to which the originators cite – or, in some cases, don't cite – one another. Some methods directly build on others – framework synthesis builds on framework analysis, for example, while grounded theory and constant comparative analysis build on grounded theory. Others further develop existing methods – meta-study, critical interpretive synthesis and meta-narrative all adapt aspects of meta-ethnography, while also importing concepts from other theorists (critical interpretive synthesis also adapts grounded theory techniques).

Some methods share a clear conceptual link, without directly citing one another: for example, the analytical themes developed during thematic synthesis are comparable to the third order interpretations of meta-ethnography. The meta-theory aspect of meta-study is echoed in both meta-narrative synthesis and critical interpretive synthesis (see 'Problematizing the literature, above); however, the originators of critical interpretive synthesis only refer to the originators of meta-study in relation to their use of sampling techniques.

While methods for qualitative synthesis have many similarities, there are clear differences in approach between them, many of which can be explained by taking account of a given method's epistemology.

However, within the two broad idealist/realist categories, any differences between methods in terms of outputs appear to be small.

Since many systematic reviews are designed to inform policy and practice, it is important to select a method – or type of method – that will produce the kind of conclusions needed. However, it is acknowledged that this is not always simple or even possible to achieve in practice.

The approaches that result in more easily translatable messages for policy-makers and practitioners may appear to be more attractive than the others; but we do need to take account lessons from the more idealist end of the spectrum, that some perspectives are not universal.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

Both authors made substantial contributions, with EBP taking a lead on writing and JT on the analytical framework. Both authors read and approved the final manuscript.

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2288/9/59/prepub

Supplementary Material

Dimensions of difference . Ranging from subjective idealism through objective idealism and critical realism to scientific realism to naïve realism

Acknowledgements

The authors would like to acknowledge the helpful contributions of the following in commenting on earlier drafts of this paper: David Gough, Sandy Oliver, Angela Harden, Mary Dixon-Woods, Trisha Greenhalgh and Barbara L. Paterson. We would also like to thank the peer reviewers: Helen J Smith, Rosaline Barbour and Mark Rodgers for their helpful reviews. The methodological development was supported by the Department of Health (England) and the ESRC through the Methods for Research Synthesis Node of the National Centre for Research Methods (NCRM). An earlier draft of this paper currently appears as a working paper on the National Centre for Research Methods' website http://www.ncrm.ac.uk/ .

• Dixon-Woods M, Agarwhal S, Jones D, Young B, Sutton A. Synthesising qualitative and quantitative evidence: a review of possible methods. J Health Serv Res Pol. 2005; 10 (1):45–53b. doi: 10.1258/1355819052801804. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barbour RS, Barbour M. Evaluating and synthesizing qualitative research: the need to develop a distinctive approach. J Eval Clin Pract. 2003; 9 (2):179–186. doi: 10.1046/j.1365-2753.2003.00371.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mays N, Pope C, Popay J. Systematically reviewing qualitative and quantitative evidence to inform management and policy-making in the health field. J Health Serv Res Pol. 2005; 10 (Suppl 1):6–20. doi: 10.1258/1355819054308576. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dixon-Woods M, Bonas S, Booth A, Jones DR, Miller T, Shaw RL, Smith J, Sutton A, Young B. How can systematic reviews incorporate qualitative research? A critical perspective. Qual Res. 2006; 6 :27–44. doi: 10.1177/1468794106058867. [ CrossRef ] [ Google Scholar ]
• Pope C, Mays N, Popay J. Synthesizing Qualitative and Quantitative Health Evidence: a Guide to Methods. Maidenhead: Open University Press; 2007. [ Google Scholar ]
• Thorne S, Jenson L, Kearney MH, Noblit G, Sandelowski M. Qualitative metasynthesis: reflections on methodological orientation and ideological agenda. Qual Health Res. 2004; 14 :1342–1365. doi: 10.1177/1049732304269888. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Centre for Reviews and Dissemination. Systematic Reviews. CRD's Guidance for Undertaking Reviews in Health Care. York: CRD; 2008. [ Google Scholar ]
• Noblit GW, Hare RD. Meta-Ethnography: Synthesizing Qualitative Studies. London: Sage; 1988. [ Google Scholar ]
• Strike K, Posner G. In: Knowledge Structure and Use. Ward S, Reed L, editor. Philadelphia: Temple University Press; 1983. Types of synthesis and their criteria. [ Google Scholar ]
• Turner S. Sociological Explanation as Translation. New York: Cambridge University Press; 1980. [ Google Scholar ]
• Britten N, Campbell R, Pope C, Donovan J, Morgan M, Pill R. Using meta-ethnography to synthesis qualitative research: a worked example. J Health Serv Res. 2002; 7 :209–15. doi: 10.1258/135581902320432732. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Campbell R, Pound P, Pope C, Britten N, Pill R, Morgan M, Donovan J. Evaluating meta-ethnography: a synthesis of qualitative research on lay experiences of diabetes and diabetes care. Soc Sci Med. 2003; 65 :671–84. doi: 10.1016/S0277-9536(02)00064-3. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pound P, Britten N, Morgan M, Yardley L, Pope C, Daker-White G, Campbell R. Resisting medicines: a synthesis of qualitative studies of medicine taking. Soc Sci Med. 2005; 61 :133–155. doi: 10.1016/j.socscimed.2004.11.063. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schutz A. Collected Paper. Vol. 1. The Hague: Martinus Nijhoff; 1962. [ Google Scholar ]
• Sandelowski M, Barroso J. Handbook for Synthesizing Qualitative Research. New York: Springer Publishing Company; 2007. [ Google Scholar ]
• Kearney MH. Enduring love: a grounded formal theory of women's experience of domestic violence. Research Nurs Health. 2001; 24 :270–82. doi: 10.1002/nur.1029. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Eaves YD. A synthesis technique for grounded theory data analysis. J Adv Nurs. 2001; 35 :654–63. doi: 10.1046/j.1365-2648.2001.01897.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Finfgeld D. Courage as a process of pushing beyond the struggle. Qual Health Res. 1999; 9 :803–814. doi: 10.1177/104973299129122298. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Glaser BG, Strauss AL. The Discovery of Grounded Theory: Strategies for Qualitative Research. New York: Aldine De Gruyter; 1967. [ Google Scholar ]
• Strauss AL, Corbin J. Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Newbury Park, CA: Sage; 1990. [ Google Scholar ]
• Strauss AL, Corbin J. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Thousand Oaks, CA: Sage; 1998. [ Google Scholar ]
• Charmaz K. In: Contemporary Field Research: A Collection of Readings. Emerson RM, editor. Waveland Press: Prospect Heights, IL; 1983. The grounded theory method: an explication and interpretation; pp. 109–126. [ Google Scholar ]
• Chesler MA. Professionals' Views of the Dangers of Self-Help Groups: Explicating a Grounded Theoretical Approach. [Michigan]: Department of Sociology, University of Michigan, Ann Arbour Centre for Research on Social Organisation, Working Paper Series; 1987. [ Google Scholar ]
• Kearney MH. Ready-to-wear: discovering grounded formal theory. Res Nurs Health. 1988; 21 :179–186. doi: 10.1002/(SICI)1098-240X(199804)21:2<179::AID-NUR8>3.0.CO;2-G. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Thomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med Res Meth. 2008; 8 :45. doi: 10.1186/1471-2288-8-45. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lucas PJ, Arai L, Baird, Law C, Roberts HM. Worked examples of alternative methods for the synthesis of qualitative and quantitative research in systematic reviews. BMC Med Res Meth. 2007; 7 (4) [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Harden A, Garcia J, Oliver S, Rees R, Shepherd J, Brunton G, Oakley A. Applying systematic review methods to studies of people's views: an example from public health research. J Epidemiol Community H. 2004; 58 :794–800. doi: 10.1136/jech.2003.014829. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Paterson BL, Thorne SE, Canam C, Jillings C. Meta-Study of Qualitative Health Research. A Practical Guide to Meta-Analysis and Meta-Synthesis. Thousand Oaks, CA: Sage Publications; 2001. [ Google Scholar ]
• Zhao S. Metatheory, metamethod, meta-data-analysis: what, why and how? Sociol Perspect. 1991; 34 :377–390. [ Google Scholar ]
• Ritzer G. Metatheorizing in Sociology. Lexington, MA: Lexington Books; 1991. [ Google Scholar ]
• CASP (Critical Appraisal Skills Programme) http://www.phru.nhs.uk/Pages/PHD/CASP.htm date unknown.
• Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O, Peacock R. Storylines of research in diffusion of innovation: a meta-narrative approach to systematic review. Soc Sci Med. 2005; 61 :417–30. doi: 10.1016/j.socscimed.2004.12.001. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kuhn TS. The Structure of Scientific Revolutions. Chicago: University of Chicago Press; 1962. [ Google Scholar ]
• Dixon-Woods M, Cavers D, Agarwal S, Annandale E, Arthur A, Harvey J, Hsu R, Katbamna S, Olsen R, Smith L, Riley R, Sutton AJ. Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups. BMC Med Res Meth. 2006; 6 (35) [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gough D. In: Applied and Practice-based Research. 2. Furlong J, Oancea A, editor. Vol. 22. Special Edition of Research Papers in Education; 2007. Weight of evidence: a framework for the appraisal of the quality and relevance of evidence; pp. 213–228. [ Google Scholar ]
• Webb EJ, Campbell DT, Schwartz RD, Sechrest L. Unobtrusive Measures. Chicago: Rand McNally; 1966. [ Google Scholar ]
• Denzin NK. The Research Act: a Theoretical Introduction to Sociological Methods. New York: McGraw-Hill; 1978. [ Google Scholar ]
• Banning J. Ecological Triangulation. http://mycahs.colostate.edu/James.H.Banning/PDFs/Ecological%20Triangualtion.pdf
• Banning J. Ecological Sentence Synthesis. http://mycahs.colostate.edu/James.H.Banning/PDFs/Ecological%20Sentence%20Synthesis.pdf
• Brunton G, Oliver S, Oliver K, Lorenc T. A Synthesis of Research Addressing Children's, Young People's and Parents' Views of Walking and Cycling for Transport. London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London; 2006. [ Google Scholar ]
• Oliver S, Rees R, Clarke-Jones L, Milne R, Oakley A, Gabbay J, Stein K, Buchanan P, Gyte G. A multidimensional conceptual framework for analysing public involvement in health services research. Health Expect. 2008; 11 :72–84. doi: 10.1111/j.1369-7625.2007.00476.x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pope C, Ziebland S, Mays N. Qualitative research in health care: analysing qualitative data. BMJ. 2000; 320 :114–116. doi: 10.1136/bmj.320.7227.114. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ritchie J, Spencer L. In: Analysing Qualitative Data. Bryman A, Burgess R, editor. London: Routledge; 1993. Qualitative data analysis for applied policy research; pp. 173–194. [ Google Scholar ]
• Miles M, Huberman A. Qualitative Data Analysis. London: Sage; 1984. [ Google Scholar ]
• Evans D, Fitzgerald M. Reasons for physically restraining patients and residents: a systematic review and content analysis. Int J Nurs Stud. 2002; 39 :739–743. doi: 10.1016/S0020-7489(02)00015-9. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Suikkala A, Leino-Kilpi H. Nursing student-patient relationships: a review of the literature from 1984–1998. J Adv Nurs. 2000; 33 :42–50. doi: 10.1046/j.1365-2648.2001.01636.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Weed M. 'Meta-interpretation': a method for the interpretive synthesis of qualitative research. Forum: Qual Soc Res. 2005; 6 :Art 37. [ Google Scholar ]
• Gough D, Thomas J. Dimensions of difference in systematic reviews. http://www.ncrm.ac.uk/RMF2008/festival/programme/sys1
• Spencer L, Ritchie J, Lewis J, Dillon L. Quality in Qualitative Evaluation: a Framework for Assessing Research Evidence. London: Government Chief Social Researcher's Office; 2003. [ Google Scholar ]
• Banning J. Design and Implementation Assessment Device (DIAD) Version 0.3: A response from a qualitative perspective. http://mycahs.colostate.edu/James.H.Banning/PDFs/Design%20and%20Implementation%20Assessment%20Device.pdf
• Paterson BL. In: Reviewing Research Evidence for Nursing Practice. Webb C, Roe B, editor. [Oxford]: Blackwell Publishing Ltd; 2007. Coming out as ill: understanding self-disclosure in chronic illness from a meta-synthesis of qualitative research; pp. 73–83. [ Google Scholar ]

Literature Reviews & Evidence Syntheses

• Getting Started

Definitions & Examples of Evidence Syntheses

Literature (narrative) review, systematic review, scoping review, integrative review, meta-analysis, meta-synthesis.

• Literature Reviews
• Systematic Reviews

There are a number of types of evidence syntheses. Below, each of the most common types are defined, and an example from a published paper is included. Note that the example articles are available through Lavery Library subscriptions and may require you to sign into your Fisher account.

• A broad term referring to reviews with a wide scope and non-standardized methodology
• Search strategies, search comprehensiveness, and time range for selected sources will vary and do not follow an established protocol.
• Very common for use in undergraduate and graduate student assignments

Example from published literature

• Intentional integration of diversity ideals in academic libraries: A literature review This link opens in a new window.
• Systematic and transparent method used to collect and categorize existing evidence on a broad question of scientific, policy, or management importance
• Compares, evaluates, and synthesizes evidence to look for an effect of an intervention
• Takes time, in some cases up to a year or more, to complete
• Most commonly referred to as type of evidence synthesis; sometimes confused as a blanket term for other types of reviews
• Stakeholders’ Acceptability of Pharmacist-Led Screening in Community Pharmacies: A Systematic Review This link opens in a new window.
• Looking to identify research gaps and opportunities for evidence synthesis rather than searching for the effect of an intervention
• May critically evaluate evidence, but does not attempt to synthesize the results in the same way as systematic reviews (see reporting/writing guidelines from Environmental Evidence Journal's Systematic Map and Evidence-Based Forestry's Guidance on Systematic Maps )
• Takes time to complete, potentially longer than a systematic review
• To learn more about scoping reviews read Arksey & O'Malley's 2007 paper: Scoping Studies: Towards a methodological framework
• The use of digital stories as a health promotion intervention: a scoping review This link opens in a new window.
• Answers questions about practice by evaluating the quality of each study included in the review and interprets and synthesizes studies into meaningful conclusions
• Differs from a systematic review in that they look "more broadly at a phenomenon of interest . . .and allows for diverse research, which may contain theoretical and methodological literature" (Toronto & Remington, 2020, p. 2)
• Change fatigue in nursing: An integrative review This link opens in a new window.

Toronto, C. E., & Remington, R. (Eds.). (2020). A step-by-step guide to conducting an integrative review . Springer International Publishing AG.

• Studies for a Meta-analysis should be selected using systematic review searching methods
• Statistical technique for combining the findings from quantitative studies
• Results are portrayed with a Forest Plot to show strength and direction of effect
• Effect of Home Exercise Training in Patients with Nonspecific Low-Back Pain: A Systematic Review and Meta-Analysis
• Studies for a Meta-synthesis should be selected using systematic review searching methods
• An approach to analyze data across qualitative studies
• Uses established methodologies to triangulate, code and translate study data. See  Guidance on choosing qualitative evidence synthesis methods for use in health technology assessments of complex interventions p.16 for more information on Qualitative Synthesis Methods
• ‘Into the Wild’: A meta-synthesis of talking therapy in natural outdoor spaces
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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
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• 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
• Evidence Synthesis Institute for Librarians
• Open Access Evidence Synthesis Resources

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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Systematic reviews & evidence synthesis methods.

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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
• Open Access Evidence Synthesis Resources

Evidence synthesis refers to any method of identifying, selecting, and combining results from multiple studies. For help selecting a methodology, please refer to:

• A Typology of Reviews: An Analysis of 14 Review Types and Associated Methodologies For help differentiating between the various types of review, consult this article by Grant & Booth, 2009.
• Methodology Decision Tree From our colleagues at Cornell, a decision tree with questions leading to various review types.

Types of evidence synthesis include: 

​​Systematic Review

• Systematically and transparently collect and categorize existing evidence on a broad question of scientific, policy or management importance.
• Compares, evaluates, and synthesizes evidence in a search for the effect of an intervention.
• Time-intensive and often take months to a year or more to complete.
• The most commonly referred to type of evidence synthesis. Sometimes confused as a blanket term for other types of reviews.

​​Literature (Narrative) Review

• A broad term referring to reviews with a wide scope and non-standardized methodology.
• Search strategies, comprehensiveness, and time range covered will vary and do not follow an established protocol.

​Scoping Review or Evidence Map

• Systematically and transparently collect and categorize existing evidence on a broad question of scientific, policy or management importance.
• Seeks to identify research gaps and opportunities for evidence synthesis rather than searching for the effect of an intervention.
• May critically evaluate existing evidence, but does not attempt to synthesize the results in the way a systematic review would.(see EE Journal and CIFOR )
• May take longer than a systematic review.
• See Arksey and O'Malley (2005)  or Peters et al (2020) for methodological guidance.

​Rapid Review

• Applies Systematic Review methodology within a time-constrained setting.
• Employs methodological "shortcuts" (limiting search terms for example) at the risk of introducing bias.
• Useful for addressing issues needing quick decisions, such as developing policy recommendations.
• See Evidence Summaries: The Evolution of a Rapid Review Approach

Umbrella Review

• Reviews other systematic reviews on a topic.
• Often defines a broader question than is typical of a traditional systematic review.
• Most useful when there are competing interventions to consider.

Meta-analysis

• Statistical technique for combining the findings from disparate quantitative studies.
• Uses statistical methods to objectively evaluate, synthesize, and summarize results.
• Conducted as an additional step of a systematic review.

Video: Exploring different review methodologies (3:25 minutes)

Methodology Decision Tree

From Cornell University Library's Evidence Synthesis guide .

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Capstone and PICO Project Toolkit

• Starting a Project: Overview
• Developing a Research Question
• Selecting Databases
• Expanding a Search
• Refining/Narrowing a Search
• Saving Searches
• Critical Appraisal & Levels of Evidence
• Citing & Managing References
• Database Tutorials
• Types of Literature Reviews
• Finding Full Text
• Term Glossary

Choosing a Review Type

For guidance related to choosing a review type, see:

• "What Type of Review is Right for You?" - Decision Tree (PDF) This decision tree, from Cornell University Library, highlights key difference between narrative, systematic, umbrella, scoping and rapid reviews.
• Reviewing the literature: choosing a review design Noble, H., & Smith, J. (2018). Reviewing the literature: Choosing a review design. Evidence Based Nursing, 21(2), 39–41. https://doi.org/10.1136/eb-2018-102895
• What synthesis methodology should I use? A review and analysis of approaches to research synthesis Schick-Makaroff, K., MacDonald, M., Plummer, M., Burgess, J., & Neander, W. (2016). What synthesis methodology should I use? A review and analysis of approaches to research synthesis. AIMS Public Health, 3 (1), 172-215. doi:10.3934/publichealth.2016.1.172 More information less... ABSTRACT: Our purpose is to present a comprehensive overview and assessment of the main approaches to research synthesis. We use "research synthesis" as a broad overarching term to describe various approaches to combining, integrating, and synthesizing research findings.
• Right Review - Decision Support Tool Not sure of the most suitable review method? Answer a few questions and be guided to suitable knowledge synthesis methods. Updated in 2022 and featured in the Journal of Clinical Epidemiology 10.1016/j.jclinepi.2022.03.004

Types of Evidence Synthesis / Literature Reviews

Literature reviews are are comprehensive summaries and syntheses of the previous research on a given topic.  While narrative reviews are common across all academic disciplines, reviews that focus on appraising and synthesizing research evidence are increasingly important in the health and social sciences.  

Most evidence synthesis methods use formal and explicit methods to identify, select and combine results from multiple studies, making evidence synthesis a form of meta-research.  

The review purpose, methods used and the results produced vary among different kinds of literature reviews; some of the common types of literature review are detailed below.

Common Types of Literature Reviews 1

Narrative (literature) review.

• A broad term referring to reviews with a wide scope and non-standardized methodology
• Search strategies, comprehensiveness of literature search, time range covered and method of synthesis will vary and do not follow an established protocol

Integrative Review

• A type of literature review based on a systematic, structured literature search
• Often has a broadly defined purpose or review question
• Seeks to generate or refine and theory or hypothesis and/or develop a holistic understanding of a topic of interest
• Relies on diverse sources of data (e.g. empirical, theoretical or methodological literature; qualitative or quantitative studies)

Systematic Review

• Systematically and transparently collects and categorize existing evidence on a question of scientific, policy or management importance
• Follows a research protocol that is established a priori
• Some sub-types of systematic reviews include: SRs of intervention effectiveness, diagnosis, prognosis, etiology, qualitative evidence, economic evidence, and more.
• Time-intensive and often takes months to a year or more to complete 
• The most commonly referred to type of evidence synthesis; sometimes confused as a blanket term for other types of reviews

Meta-Analysis

• Statistical technique for combining the findings from disparate quantitative studies
• Uses statistical methods to objectively evaluate, synthesize, and summarize results
• Often conducted as part of a systematic review

Scoping Review

• Systematically and transparently collects and categorizes existing evidence on a broad question of scientific, policy or management importance
• Seeks to identify research gaps, identify key concepts and characteristics of the literature and/or examine how research is conducted on a topic of interest
• Useful when the complexity or heterogeneity of the body of literature does not lend itself to a precise systematic review
• Useful if authors do not have a single, precise review question
• May critically evaluate existing evidence, but does not attempt to synthesize the results in the way a systematic review would 
• May take longer than a systematic review

Rapid Review

• Applies a systematic review methodology within a time-constrained setting
• Employs methodological "shortcuts" (e.g., limiting search terms and the scope of the literature search), at the risk of introducing bias
• Useful for addressing issues requiring quick decisions, such as developing policy recommendations

Umbrella Review

• Reviews other systematic reviews on a topic
• Often defines a broader question than is typical of a traditional systematic review
• Most useful when there are competing interventions to consider

1. Adapted from:

Eldermire, E. (2021, November 15). A guide to evidence synthesis: Types of evidence synthesis. Cornell University LibGuides. https://guides.library.cornell.edu/evidence-synthesis/types

Nolfi, D. (2021, October 6). Integrative Review: Systematic vs. Scoping vs. Integrative. Duquesne University LibGuides. https://guides.library.duq.edu/c.php?g=1055475&p=7725920

Delaney, L. (2021, November 24). Systematic reviews: Other review types. UniSA LibGuides. https://guides.library.unisa.edu.au/SystematicReviews/OtherReviewTypes

Further Reading: Exploring Different Types of Literature Reviews

• A typology of reviews: An analysis of 14 review types and associated methodologies Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26 (2), 91-108. doi:10.1111/j.1471-1842.2009.00848.x More information less... ABSTRACT: The expansion of evidence-based practice across sectors has lead to an increasing variety of review types. However, the diversity of terminology used means that the full potential of these review types may be lost amongst a confusion of indistinct and misapplied terms. The objective of this study is to provide descriptive insight into the most common types of reviews, with illustrative examples from health and health information domains.
• Clarifying differences between review designs and methods Gough, D., Thomas, J., & Oliver, S. (2012). Clarifying differences between review designs and methods. Systematic Reviews, 1 , 28. doi:10.1186/2046-4053-1-28 More information less... ABSTRACT: This paper argues that the current proliferation of types of systematic reviews creates challenges for the terminology for describing such reviews....It is therefore proposed that the most useful strategy for the field is to develop terminology for the main dimensions of variation.
• Are we talking the same paradigm? Considering methodological choices in health education systematic review Gordon, M. (2016). Are we talking the same paradigm? Considering methodological choices in health education systematic review. Medical Teacher, 38 (7), 746-750. doi:10.3109/0142159X.2016.1147536 More information less... ABSTRACT: Key items discussed are the positivist synthesis methods meta-analysis and content analysis to address questions in the form of "whether and what" education is effective. These can be juxtaposed with the constructivist aligned thematic analysis and meta-ethnography to address questions in the form of "why." The concept of the realist review is also considered. It is proposed that authors of such work should describe their research alignment and the link between question, alignment and evidence synthesis method selected.
• Meeting the review family: Exploring review types and associated information retrieval requirements Sutton, A., Clowes, M., Preston, L., & Booth, A. (2019). Meeting the review family: Exploring review types and associated information retrieval requirements. Health Information & Libraries Journal, 36(3), 202–222. doi: 10.1111/hir.12276

Integrative Reviews

"The integrative review method is an approach that allows for the inclusion of diverse methodologies (i.e. experimental and non-experimental research)." (Whittemore & Knafl, 2005, p. 547).

• The integrative review: Updated methodology Whittemore, R., & Knafl, K. (2005). The integrative review: Updated methodology. Journal of Advanced Nursing, 52 (5), 546–553. doi:10.1111/j.1365-2648.2005.03621.x More information less... ABSTRACT: The aim of this paper is to distinguish the integrative review method from other review methods and to propose methodological strategies specific to the integrative review method to enhance the rigour of the process....An integrative review is a specific review method that summarizes past empirical or theoretical literature to provide a more comprehensive understanding of a particular phenomenon or healthcare problem....Well-done integrative reviews present the state of the science, contribute to theory development, and have direct applicability to practice and policy.

• Conducting integrative reviews: A guide for novice nursing researchers Dhollande, S., Taylor, A., Meyer, S., & Scott, M. (2021). Conducting integrative reviews: A guide for novice nursing researchers. Journal of Research in Nursing, 26(5), 427–438. https://doi.org/10.1177/1744987121997907
• Rigour in integrative reviews Whittemore, R. (2007). Rigour in integrative reviews. In C. Webb & B. Roe (Eds.), Reviewing Research Evidence for Nursing Practice (pp. 149–156). John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470692127.ch11

Scoping Reviews

Scoping reviews are evidence syntheses that are conducted systematically, but begin with a broader scope of question than traditional systematic reviews, allowing the research to 'map' the relevant literature on a given topic.

• Scoping studies: Towards a methodological framework Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8 (1), 19-32. doi:10.1080/1364557032000119616 More information less... ABSTRACT: We distinguish between different types of scoping studies and indicate where these stand in relation to full systematic reviews. We outline a framework for conducting a scoping study based on our recent experiences of reviewing the literature on services for carers for people with mental health problems.
• Scoping studies: Advancing the methodology Levac, D., Colquhoun, H., & O'Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implementation Science, 5 (1), 69. doi:10.1186/1748-5908-5-69 More information less... ABSTRACT: We build upon our experiences conducting three scoping studies using the Arksey and O'Malley methodology to propose recommendations that clarify and enhance each stage of the framework.
• Methodology for JBI scoping reviews Peters, M. D. J., Godfrey, C. M., McInerney, P., Baldini Soares, C., Khalil, H., & Parker, D. (2015). The Joanna Briggs Institute reviewers’ manual: Methodology for JBI scoping reviews [PDF]. Retrieved from The Joanna Briggs Institute website: http://joannabriggs.org/assets/docs/sumari/Reviewers-Manual_Methodology-for-JBI-Scoping-Reviews_2015_v2.pdf More information less... ABSTRACT: Unlike other reviews that address relatively precise questions, such as a systematic review of the effectiveness of a particular intervention based on a precise set of outcomes, scoping reviews can be used to map the key concepts underpinning a research area as well as to clarify working definitions, and/or the conceptual boundaries of a topic. A scoping review may focus on one of these aims or all of them as a set.

Systematic vs. Scoping Reviews: What's the Difference? 

YouTube Video 4 minutes, 45 seconds

Rapid Reviews

Rapid reviews are systematic reviews that are undertaken under a tighter timeframe than traditional systematic reviews. 

• Evidence summaries: The evolution of a rapid review approach Khangura, S., Konnyu, K., Cushman, R., Grimshaw, J., & Moher, D. (2012). Evidence summaries: The evolution of a rapid review approach. Systematic Reviews, 1 (1), 10. doi:10.1186/2046-4053-1-10 More information less... ABSTRACT: Rapid reviews have emerged as a streamlined approach to synthesizing evidence - typically for informing emergent decisions faced by decision makers in health care settings. Although there is growing use of rapid review "methods," and proliferation of rapid review products, there is a dearth of published literature on rapid review methodology. This paper outlines our experience with rapidly producing, publishing and disseminating evidence summaries in the context of our Knowledge to Action (KTA) research program.
• What is a rapid review? A methodological exploration of rapid reviews in Health Technology Assessments Harker, J., & Kleijnen, J. (2012). What is a rapid review? A methodological exploration of rapid reviews in Health Technology Assessments. International Journal of Evidence‐Based Healthcare, 10 (4), 397-410. doi:10.1111/j.1744-1609.2012.00290.x More information less... ABSTRACT: In recent years, there has been an emergence of "rapid reviews" within Health Technology Assessments; however, there is no known published guidance or agreed methodology within recognised systematic review or Health Technology Assessment guidelines. In order to answer the research question "What is a rapid review and is methodology consistent in rapid reviews of Health Technology Assessments?", a study was undertaken in a sample of rapid review Health Technology Assessments from the Health Technology Assessment database within the Cochrane Library and other specialised Health Technology Assessment databases to investigate similarities and/or differences in rapid review methodology utilised.
• Rapid Review Guidebook Dobbins, M. (2017). Rapid review guidebook. Hamilton, ON: National Collaborating Centre for Methods and Tools.
• NCCMT Summary and Tool for Dobbins' Rapid Review Guidebook National Collaborating Centre for Methods and Tools. (2017). Rapid review guidebook. Hamilton, ON: McMaster University. Retrieved from http://www.nccmt.ca/knowledge-repositories/search/308
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Evidence Synthesis

• Identifying
• Collecting & Combining Data
• Explaining the Synthesis & Analysis
• Summarizing
• Types of Reviews
• Protocols & Registries
• Critical Appraisal
• Data Extraction
• Library Resources, Support, & Training

Review Types

• Systematic review
• Scoping review
• Rapid review
• Narrative Review
• Meta-analysis
• Other review types

Adapted from Grant, M.J. and Booth, A. (2009), A typology of reviews: an analysis of 14 review types and associated methodologies . Health Information & Libraries Journal, 26: 91-108. https://doi.org/10.1111/j.1471-1842.2009.00848.x

Also useful Sutton, Clowes, M., Preston, L., & Booth, A. (2019). Meeting the review family: exploring review types and associated information retrieval requirements . Health Information and Libraries Journal, 36(3), 202–222. https://doi.org/10.1111/hir.12276

Adapted from Grant, M.J. and Booth, A. (2009), A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26: 91-108. https://doi.org/10.1111/j.1471-1842.2009.00848.x

Meta analysis is a statistical technique that can be used to aggregate the results of individual quantitative studies.

• Fixed effect if the factor is assumed constant across studies and variation among studies is random
• Weighted based on precision of estimates
• Inverse variance weighting (sample size/measurement error)
• Usually a “forest plot” – point estimate and confidence interval of effect in each study
• Cochran’s Q, Higgins I2
• Subgroup analysis, stratified analysis
• graphical (Galbraith plot, scatterplot of effect size vs factor)
• meta regression (I2, adjusted R2, Tau2)
• Funnel plot of each study’s SE against estimated effect
• Tests based on funnel plot (Begg, Egger)

Glossary of Terms

Analysis - assesses the strength of the evidence for drawing conclusions based on the synthesis. The trends and patterns can be used in comparisons, to discover explanatory or confounding variables, to develop themes or frameworks, to inform best practices, etc.

Bias - systematic error in research studies that can lead to erroneous conclusions. Can occur in clinical trials, systematic reviews, and all types of research.

Forrest Plot - also known as a blobogram. A graphical display designed to illustrate the relative strength of effects in multiple studies

Funnel plot - a graphical device for for exploring possible publication bias by plotting study size vs. effect size

Which Type of Review is Right for You?

Long Description of Infographic for Web Accessibility

Image re-mixed with permission from Yale Harvey Cushing/John Hay Whitney Medical Library .

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What Makes a Systematic Review Different from Other Types of Reviews?

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Reproduced from Grant, M. J. and Booth, A. (2009), A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26: 91–108. doi:10.1111/j.1471-1842.2009.00848.x

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Nursing Literature and Other Types of Reviews

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• Starting Your Search
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About Literature Tables and Writing a Synthesis

A literature table is a way to organize the articles you've selected for inclusion in your publication. There are many different types of literature tables-the main thing is to determine the important pieces that help draw out the comparisons and contrasts between the articles included in your review. The first few columns should include the basic info about the article (title, authors, journal), publication year, and the purpose of the paper.

While the table is a step to help you organize the articles you've selected for your research, the literature synthesis can take many forms and can have multiple parts. This largely depends on what type of review you've undertaken. Look back at the examples under Literature and Other Types of Reviews to see examples of different types of reviews.

• Example of Literature Table

Examples of Literature Tables

Camak, D.J. (2015), Addressing the burden of stroke caregivers: a literature review. J Clin Nurs, 24: 2376-2382. doi: 10.1111/jocn.12884

Balcombe, L., Miller, C., & McGuiness, W. (2017). Approaches to the application and removal of compression therapy: A literature review. British Journal of Community Nursing , 22 , S6–S14. https://doi-org.proxy1.cl.msu.edu/10.12968/bjcn.2017.22.Sup10.S6

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• Choosing a Type of Review

Literature Reviews: Choosing a Type of Review

Selecting a review type.

You'll want to think about the kind of review you are doing. Is it a selective or comprehensive review? Is the review part of a larger work or a stand-alone work ?

For example, if you're writing the Literature Review section of a journal article, that's a selective review which is part of a larger work. Alternatively, if you're writing a review article, that's a comprehensive review which is a stand-alone work. Thinking about this will help you develop the scope of the review.

Defining the Scope of Your Review

This exercise will help define the scope of your Literature Review, setting the boundaries for which literature to include and which to exclude.

A FEW GENERAL CONSIDERATIONS WHEN DEFINING SCOPE

• Which populations to investigate — this can include gender, age, socio-economic status, race, geographic location, etc., if the research area includes humans.
• What years to include — if researching the legalization of medicinal cannabis, you might only look at the previous 20 years; but if researching dolphin mating practices, you might extend many more decades.
• Which subject areas — if researching artificial intelligence, subject areas could be computer science, robotics, or health sciences
• How many sources  — a selective review for a class assignment might only need ten, while a comprehensive review for a dissertation might include hundreds. There is no one right answer.
• There will be many other considerations that are more specific to your topic. 

Most databases will allow you to limit years and subject areas, so look for those tools while searching. See the Searching Tips tab for information on how use these tools.

Four Common Types of Reviews

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• Published: 06 April 2024

Large language model enhanced corpus of CO 2 reduction electrocatalysts and synthesis procedures

• Xueqing Chen   ORCID: orcid.org/0009-0008-8926-9626 1 , 2   na1 ,
• Yang Gao   ORCID: orcid.org/0000-0002-3451-1904 3   na1 ,
• Ludi Wang   ORCID: orcid.org/0000-0002-9346-6250 1   na1 ,
• Wenjuan Cui   ORCID: orcid.org/0000-0002-1858-8194 1 ,
• Jiamin Huang 3 ,
• Yi Du   ORCID: orcid.org/0000-0003-3121-8937 1 , 2 , 4 &
• Bin Wang   ORCID: orcid.org/0000-0001-9576-2646 3  

Scientific Data volume  11 , Article number:  347 ( 2024 ) Cite this article

Metrics details

• Electrocatalysis
• Photocatalysis

CO 2 electroreduction has garnered significant attention from both the academic and industrial communities. Extracting crucial information related to catalysts from domain literature can help scientists find new and effective electrocatalysts. Herein, we used various advanced machine learning, natural language processing techniques and large language models (LLMs) approaches to extract relevant information about the CO 2 electrocatalytic reduction process from scientific literature. By applying the extraction pipeline, we present an open-source corpus for electrocatalytic CO 2 reduction. The database contains two types of corpus: (1) the benchmark corpus, which is a collection of 6,985 records extracted from 1,081 publications by catalysis postgraduates; and (2) the extended corpus, which consists of content extracted from 5,941 documents using traditional NLP techniques and LLMs techniques. The Extended Corpus I and II contain 77,016 and 30,283 records, respectively. Furthermore, several domain literature fine-tuned LLMs were developed. Overall, this work will contribute to the exploration of new and effective electrocatalysts by leveraging information from domain literature using cutting-edge computer techniques.

Background & Summary

CO 2 electroreduction has garnered significant attention from both the academic and industrial communities, owing to its potential to effectively mitigate greenhouse gas emissions while simultaneously producing fuels and chemicals 1 , 2 , 3 . Its widespread adoption relies heavily on the development of efficient and reliable electrocatalysts. Over the past three decades, scientists have invested substantial efforts in the development of CO 2 reduction electrocatalysts 4 , 5 ; However, this trial-and-error approach has proven to be time-consuming and labor-intensive. Consequently, it becomes pivotal in accelerating catalyst development to establish a comprehensive database for CO 2 electroreduction, which should encompass various information pertaining to the composition, synthesis, regulation, and performance of catalysts. Given the substantial workload involved, the manual annotation method by domain experts is deemed unreasonable. In recent years, emerging artificial intelligence (AI) technologies have exhibited tremendous potential in facilitating the construction of realm-specific datasets 6 , 7 . Extracting crucial information related to catalysts from domain literature is the initial step toward accelerating catalyst development using AI technologies. Traditionally, Named Entity Recognition (NER) methods have been employed for text mining and information retrieval 8 , 9 , 10 , 11 . However, NER often necessitates the establishment of algorithms tailored to specific tasks, which are typically undertaken by scientists or engineers with expertise in coding, data structures, and computer algorithms. Therefore, this approach is labor-intensive. Furthermore, NER algorithms are closely tied to their assigned tasks, lacking generalizable ability and thus making direct transfer to other tasks challenging. Additionally, extracted information tends to be intricate, heterogeneous, and diverse in the field of catalysis, leading to unsatisfied NER performance and reduced accuracy 12 . Therefore, the development and utilization of more general and robust methods for extracting domain knowledge are becoming increasingly imperative.

Recently, the emergence of large language models (LLMs), especially the widely acclaimed ChatGPT, has brought new prospects to the field of NER tasks 13 . It can be effectively operated by domain scientists who may not be well-versed in computer algorithms. However, ChatGPT is susceptible to information hallucinations, a glaring issue that significantly undermines its reliability in scientific domains 14 , 15 , 16 . Prompt engineering has proven to be a potential solution to mitigate the problem of artificial hallucinations 17 , 18 , 19 . For instance, Zheng et al . employed prompt engineering to guide ChatGPT in automating text mining for the synthesis conditions of metal-organic frameworks 17 . Nevertheless, the utility of this approach for more diverse and complex tasks within the catalytic science domain remains an area warranting further exploration. Moreover, the high demand for computing resources in LLMs also limits their application in various fields. The training and application of LLMs usually require a tremendous amount of computational power, which are not only expensive to purchase but also consume substantial amounts of electricity.

In recent work, our team has developed a text-mining pipeline to construct a dataset describing the CO 2 reduction process catalyzed by copper-based electrocatalysts, which specifically includes material, regulation method, product, Faradaic efficiency and relevant conditions 12 . In the current work, we built a more advanced extraction pipeline based on the knowledge system of CO 2 electrocatalytic reduction (Fig.  1 ), which uses various advanced machine learning, natural language processing techniques and large language models (LLMs) approaches to extract relevant information about the CO 2 electrocatalytic reduction process from scientific literature. In addition, for the purpose of providing a more detailed and complete guidance scheme for materials scientists to develop new catalysts, we designed a set of synthesis actions with predefined properties and a deep-learning sequence to sequence model based on the transformer architecture, which converts unstructured experimental procedure text into structured action sequences. By applying the extraction pipeline, we present an open-source corpus for electrocatalytic CO 2 reduction. The database contains two types of corpus: (1) the benchmark corpus, which is a collection of 6,086 records extracted from 1,081 publications by catalysis postgraduates; and (2) the extended corpus, which consists of content extracted from the abstract of 5,941 documents using traditional natural language processing techniques and large language models techniques. Respectively, the Extended Corpus I contains 77,016 records and the Extended Corpus II contains 30,283 records. In addition, we extracted 476 synthesis procedures for catalytic materials from 2,176 full-text documents, and the extracted information includes target and preparation materials, synthesis operations and the quantity of materials involved in them, and operation properties. The Extended Corpus was evaluated and revised by domain experts. This work provides a valuable resource to accelerate research into CO 2 reduction by supplying structured information and datasets ready for further analysis and hypothesis generation. The tools and datasets created could significantly reduce the time and resources required for literature review and data gathering, allowing scientists to focus on innovation and experimentation.

The schematic overview of dataset construction pipeline. ( a ) The process of literature search filtering and paragraph classification. ( b ) The top panel shows the schematic diagram of the standard text mining process: <i> expert annotation to build a baseline corpus; <ii> extraction of critical information from the literature text and construction of an extended corpus; <iii> store in a database for future data mining. The bottom panel shows an example of converting a synthesis sentence into action sequences. The key components of an action sequence such as starting and target material, synthesis steps and their conditions are found and extracted from the paragraph by different text mining algorithms (see Methods). ( c ) The entity types and their relationships extracted from the literature. The final constructed dataset can provide guidance for practical experimental work.

The schematic overview of the extraction pipeline is shown in Fig.  1 . We first searched the literature related to the electrocatalytic CO 2 reduction process following a series of filtering criteria. For scientific article retrieval and preprocessing, the raw archived corpus was parsed and organized in paragraphs. After paragraph classification, the paragraphs related to the concrete synthesis procedures were automatically selected. The extracted information includes the materials, the target products, their quantities as well as the synthesis operations and their attributes. We then constructed action sequences for each synthesis action in a predefined format. Finally based on the the system of knowledge defined by domain experts, we published a manually annotated baseline corpus and an automatically annotated extended corpus. The final generated dataset can be used for domain data mining and further downstream NLP tasks, as well as provide guidance to material domain scientists for practical experimental work.

Content acquisition

Scientific publications used in this work are journal articles published by Elsevier, the Royal Society of Chemistry, American Chemical Society, Wiley, Acta Physico-Chimica Sinica & University Chemistry Editorial Office (Peking University), MDPI, the Electrochemical Society, Springer Nature, etc. For each publisher, the journals relevant to materials science were manually selected. We used regular expression matching 20 to obtain the dois of relevant literature in the field of CO 2 electrocatalytic reduction. Specifically, we searched and exported metadata for more than 27,000 articles by using the keywords “CO 2 ”, “Reduction”, and “Electro*” as subject indexes on the Web of Science website. The exported literature metadata was then filtered step by step according to expert-defined rules. The title of every article was queried for words “CO 2 ”, “carbon dioxide” or “CO(2)”, which yielded 9,850 articles. The abstract of every article was queried for words “electroc” or “electror”, which yielded 6,973 articles. Finally the domain experts performed manual filtration to exclude articles whose titles contained words that were not relevant to the topic, including: “photoc”, “light”, “visible”, “solar”, “microbial”, “bacteria”, “culture”, etc. we eventually obtained 5,941 summary texts of the literature related to the work on CO 2 electrocatalytic reduction and scraped the full text of 2,776 papers from the web. We finally acquired the literature in PDF format and used the PyMuPDF tool, a PDF parsing tool 21 , to automatically process these literature data to obtain their metadata such as title, authors, abstract, etc. and the full text in json format. Since the processed document contains irrelevant tags, we developed a data cleaning method for parsing the article tag strings into consistently formatted text paragraphs while retaining the same chapter and paragraph structure as the original paper.

Paragraph classification

We used the Transformers Bidirectional Encoder Representation (BERT) model to identify paragraphs containing descriptions of synthesis methods. MatBERT is a BERT model 22 specifically for material science texts, pre-trained on over 2 million papers in a self-supervised manner, i.e. by predicting masked words based on the context around the target sentence. After training the BERT model, we used a paragraph classification method based on semi-supervised learning 23 . First we applied latent Dirichlet allocation (LDA) 24 on the 12,643 articles in the field of photoelectrocatalysis to identify the experimental steps implicit in sentences. Then we collected all the paragraphs from the literature and manually labelled the paragraphs describing the synthesis protocol. The training data ultimately included 760 training examples, with 228 positive examples and 532 negative examples. We applied the random decision forest (RF) algorithm 25 , a supervised machine learning method, to binary classify the training data. This step yielded 476 synthesis paragraphs from a total of 2,776 articles.

Entity annotation

In order to improve the quality of the training data based on the automatically extracted models, we generated a higher-quality dataset, also known as a gold standard corpus 26 , by manually annotating a portion of the sentences from the abstracts and body of literature related to CO 2 electroreduction. We developed an annotation framework based on the doccano annotation tool 27 . Annotators can open the framework in a web browser and browse through the sentences of the material literature. The page displays the sentence to be annotated along with predefined entity types and related descriptions. The annotator can add new entities, reorder them or edit them by opening a separate view. To ensure consistency between annotators, detailed annotation guidelines are provided.

Entity extraction

In our previous study, we extracted nine types of entities in the literature based on the constructed electrocatalytic reduction system, including material, regulation method, product, faradaic efficiency, cell setup, electrolyte, synthesis method, current density, and voltage. Some of these entity labels are provided with more detailed labelling subclasses to ensure that materials scientists have access to more complete information. In the current construction of the CO 2 electrocatalysis literature dataset, We have updated the categories of the tag subcategories according to the new knowledge system. In addition, we added information on the material synthesis process, which converted unstructured scientific paragraphs describing catalytic materials synthesis into pre-defined “coded recipes” of synthesis. The recipes includes not only the starting materials and final target products but also the synthesis actions and their attributes.

Construction of extended corpus

Traditional entity extraction methods follow the pattern of “expert annotation, model training, model application” and use automatic extraction models to build a wider and larger corpus of lower quality, also known as a silver standard corpus (SSC) 26 . The Large Language Models (LLMs) such as GPT-3, GPT-3.5, and GPT-4 have been used for this purpose 28 , 29 , 30 . Its emergency provides a new paradigm for natural language processing modelling, i.e., building prompts with a small amount of expert annotation to directly fine-tune GPT models that have been pre-trained on large-scale data. Traditional NER methods are less general, but have higher domain confidence, while large models may produce uncontrollable illusions. Herein, in this paper, we used two model training approaches separately to generate an extended corpus based on the construction standard of the silver standard corpus (SSC).

Entity extraction using traditional NER methods

Regarding the hierarchical structure of entity labelling, we designed a two-step entity recognition model which consists of coarse-grained entity recognition and fine-grained entity classification. In the first step, we used the SciBERT model 31 to convert each word token into an embedding vector. The embedding vector was then passed to a bi-directional long-short-term memory neural network with a conditional random-field top layer(BiLSTM-CRF) 32 , 33 to identify which class of entity labels the corresponding token was. Considering that the representations of some entities usually have regularities, such as the chemical formula expressions of material entities and the numerical expressions of faradaic efficiency entities, we proposed a regular rule-based approach to assist the deep learning model 34 . The results of the two models were selected using a voting scheme 26 . In the second step, each coarse-grained type entity was classified into finer-grained entity classes using a classification algorithm combining dictionary and maximum entropy model. The dictionary-based recognizers used lists of words built on expert-annotated data 35 . For data that cannot be matched, the word embedding vectors, context vectors, word cluster clustering information and coarse-grained entity category information for each entity were passed through a simple mapping function. The final mapping results were used as entity features for classification probability prediction through a maximum entropy model.

A typical synthesis procedure in the electrocatalytic reduction literature contains information on the prepared and target materials, synthesis operations and operating conditions. These items are organized into material synthesis “recipes” and are extracted from the synthesis paragraph as shown in Fig.  2 . Our extraction process consists of multiple algorithms that analyze the passages and identify the relevant materials, the synthesis actions performed, and the condition information associated with those synthetic actions. The method used in each step of the extraction process is described in detail below.

Schematic diagram of the process of converting a synthetic paragraph into action sequences.

Step 1: Materials entity recognition . The first step is the labelling of the preparation material. The synthesis of the target material involves the names of all the reagents that need to be prepared. We used pattern matching against a database of common reagent names and then used a plain Bayesian classifier to determine whether a candidate phrase is a reagent name, excluding some specific phrases 36 . Through iterative trials, we eventually chose reagent names from the Reaxys database and non-reagent-name texts from the Brown English language corpus to train the classifier.

Step 2: Synthesis actions To identify and classify synthesis actions described in passages, we implemented an algorithm that combines Recurrent Neural Networks (RNN) and rule-based sentence dependency tree parsing 22 . The neural network labelled the sentences in the synthetic passages into nine categories: NOT OPERATION, ADDING, HEATING, CURING, ELECTROCHEMICAL ANODIZATION, FILTERING, DRYING, DIPPING and REACITON , which are the main operations in catalytic materials synthesis. We used ChemDataExtractor’s ChemWordTokenizer 37 to tokenize the lemmatized sentences. For each synthesis action obtained, we used the SpaCy library 38 to parse the syntactic information of the dependency subtree for linguistic features of the tokens, such as their lexical properties and their dependency on the root token.

Step 3: Synthesis action conditions For each synthesis action, we used dependency tree parsing and rule-based regular expression methods 39 to extract the relevant attributes of the synthesis action, such as heating time, heating temperature, and potential voltage values. In addition, if there were materials involved such as ADDING and REACTION operations, we used pattern-matching techniques to extract the names and corresponding quantities of the reagents involved. For example, one of the patterns used for finding solutions is “a/an XX solution containing Reagent” in which “Reagent” represents a phrase previously tagged as a reagent. An example phrase that would be matched by this pattern is “an aqueous solution containing HAuCl 4 (10 mol, 125 mL)”. The contents of the parentheses are regularly matched to the corresponding quantities of the reagents.

Entity extraction using LLMs

In previous study, we attempted to construct a corpus using an NLP model, but the accuracy of the intelligent model is easily affected by the volume of training data. Herein, we demonstrate that LLMs, including original LLMs and fine-tuned LLMs, can act as assistants to collaborate with human researchers, facilitating entity recognition and text mining to accelerate the research process.

In the realm of catalyst-related tasks, LLM’s performance can be significantly enhanced by employing prompt engineering (PE) which can steer LLMs toward generating precise and pertinent information. Although LLMs, including fine-tuned LLMs, can answer general questions, their knowledge depth, accuracy and timeliness are limited in vertical domain filed. To solve this problem, we use vector databases to enhance the reasoning ability of LLMs in vertical domains. Vector databases can transform literature and data into vector representations by embedding vectors. Sci-BERT 31 was used as embedding model for construct the vector database.

Figure  3 shows the process of knowledge extraction using LLMs and vector database. Firstly, we processed and cleaned the full text of 12,643 photoelectrocatalytic scientific literature, and used them for LLMs fine-tuning. In this step, we chose Vicuna-33b-v1.3 as the basic LLMs. Secondly, we extracted the title, abstract and doi from articles associated with standard corpus, then we use Sci-BERT as the embedding model to transform title and abstract into vector. When performing entity recognition, user first input the text to be extracted, embedding model transform it into vectors. Then the similar articles will be obtained by calculating the vector distance, and will be used to generate precise and pertinent information, which be shown in Fig.  4 . The prompt will be input to the fine-tuned LLMs for entity recognition.

The schematic overview of extraction using LLMs and vector database.

The prompt using in the entity extraction.

Data Records

The both types of datasets constructed in this work are available in ScienceDB, a public, general-purpose data repository designed to serve data to researchers, research projects/teams, journals, institutions, universities, and others. The metadata contained in the article dataset includes: article DOI, year of publication, and title. Each record corresponds to the process of CO 2 electrocatalytic reduction and its metadata includes: the entity extracted from the paper, the label of the entity, and the sentence in which the entity is located. In addition, the datasets for the catalytic material synthesis methods are available as a single json. Each record corresponds to a synthesis procedure extracted from a paragraph and is represented as a separate json object. The metadata for each reaction includes the DOI of the paper from which the reaction is extracted as well as a fragment of the corresponding synthesis paragraph, the target product, the preparative material used in the reaction, and a tree of seven types of synthesis operations and their corresponding conditions. Table  1 gives extended details of all the dataset format.

The sequence of synthesis steps for the reaction (if specified in a paragraph) is listed as a data structure with the following fields: the original paragraph in the text ( synthesis_paragraph ), its type ( operation_string ) specified by the classification algorithm (see Methods), and the conditions associated with this operation step ( conditions ). We classified the types of operations involved in the synthesis of catalyst materials into eight categories and give detailed descriptions of the types of operations and condition attributes in Table  2 .

The corpus is publicly available at Science Data Bank (ScienceDB), which is a public, general-purpose data repository aiming to provide data services for researchers, research projects/teams, journals, institutions, universities, etc. The benchmark corpus is publicly available at https://doi.org/10.57760/sciencedb.13290 40 . The extended corpus I and extended corpus II are publicly available at https://doi.org/10.57760/sciencedb.13292 41 , where include other extendedcorpuscorpus exacted by LLM model. The two types of Corpus are provided as a file in CSV format, and the details of them are shown in Table  3 . A complete dataset of 476 catalytic material synthesis processes is publicly available at https://doi.org/10.57760/sciencedb.13293 42 .

Technical Validation

Extraction accuracy.

To demonstrate the utility of the extended corpus, we first evaluated the model against other current state-of-the-art traditional entity extraction methods. We selected several generic neural network tagging models, including bi-directional LSTM layers with conditional random field (CRF) layer 33 , 43 , 44 , bi-directional recurrent neural network Bi-GRU 45 , and BERT model with CRF layer. We then chose a multi-feature based maximum entropy machine learning model 46 using two types of features, Parts-of-Speech features generated by GENIA Parts-of-Speech Tagger 47 and lexical features. Table  4 shows the results of the experimental comparison. We found that our constructed entity extraction model consistently outperforms other methods, achieving an overall F1 score of 85.16 in recognizing four coarse-grained categories of entities. This also demonstrated an advantage in the subsequent classification of fine-grained entities.

To estimate the quality of the synthesis process dataset, we had a human expert test 100 randomly selected entries. The human expert manually extracted the information provided in the synthesis paragraphs and compared the results with those extracted by the pipeline. Table  5 presents the accuracy statistics, which include the precision, recall, and F1 scores calculated from the test entries.

We also validated the entity recognition results of the LLMs in this paper. We validate the answers of the LLMs by an expert with 160 randomly selected entries, and ensure that each category has 20 test data. The evaluation result is shown in Table  6 . The Count means the total amount of samples from different categories, the Correct means the number of correctly identified entities, and the Existence means the number of entities of this type does exist in the text input to the large model. It is worth mentioning that if there is indeed no corresponding entity in the text input to the large model, the situation where the large model answers empty should also be considered as correct recognition. Therefore, we use Modified Correct to remove the above influence. Ultimately, we utilize Modified Correct and Count to calculate the evaluation of LLMs, which is Modified accuracy . Using large models for entity recognition also causes significant time loss. We used two NVIDIA A100 GPU graphics processing units for entity recognition, and cost almost 10 hours to process 5,941 literature abstracts.

From the results, we can see that the LLMs perform better in entity extraction for numerical classes (faradaic efficiency, potential, etc.), but perform poorly in entity extraction for descriptive classes. This may be due to the objectivity of data entities, which reduces the possibility of hallucinations in large models.

Dataset mining

To present the recent trends in the development of CO 2 reduction electrocatalysts, we showcased and analyzed the information in the database. Firstly, we demonstrated the publication trends of CO 2 reduction electrocatalysts over the past 30 years. As depicted in Fig.  5a , articles on CO 2 reduction electrocatalysts have experienced a rapid surge since 2010, indicating the burgeoning interest of scientists in this field. Figure  5b illustrates the proportional distribution of various types of CO 2 reduction electrocatalysts. It is evident that the current research predominantly focuses on E (single metal), E/C (metal-carbon composites), E-M (binary or ternary metal systems), and EO x (metal oxides), with a notable increase in attention toward E/C in recent years.

( a ) Histograms of the number of publications of CO 2 reduction electrocatalysts over the past thirty years. ( b ) Stacked histograms of the percentage of CO 2 reduction electrocatalysts in the last ten years.

In addition to the overall development of electrocatalysts, another intriguing aspect lies in the correlation between catalysts and products, which is crucial for product-oriented catalyst design. Figure  6 presents an alluvial plot illustrating the intricate associations between catalysts and products. Notably, for clarity, less reported catalyst categories have not been included. E/C and E-M are favorable choices for generating CO, while E-M and EO x exhibit the capability for formic acid production. For C 2 products, such as C 2 H 4 and C 2 H 5 OH, both E and EO x are viable options. Furthermore, Fig.  6 also reveals some potential research topics that warrant further exploration. For instance, although a few catalysts demonstrate the ability to produce C 3 products, such as n-propanol and acetone, the optimal catalysts have yet to be well-established. While composite systems are gaining increasing attention, their advantages over individual compounds remain to be fully elucidated.

Alluvial plot illustrating the relationships between catalysts and products.

Moreover, the type of metal, particularly the presence of Cu, is crucial for the performance of catalysts in CO 2 electroreduction. Therefore, we annotated whether the catalysts contained Cu in the database. To illustrate this contrast clearly, we generated doughnut charts to display the percentage of different products from several types of catalysts with or without Cu. As shown in Fig.  7a , the majority of the products for E/C are CO, while Cu/C can generate various C 1 and C 2 products. For single metal systems (Fig.  7b ), the primary products of E are C 1 products, whereas Cu yields predominantly C 2 products. In the case of binary or ternary metal systems, Cu-M exhibits a stronger capability for producing C 2 products compared to E-M. Regarding metal oxides, the products of EO x are predominantly formic acid, while CuO x yields primarily C 2 H 4 . These findings underscore the significant impact of the presence of Cu on the selectivity of C 2 products for catalysts.

Doughnut charts showing the percentage of different products of catalysts with or without Cu.

The choice of synthesis method also has a significant impact on the performance of catalysts, so we analyzed the correlation between catalysts and synthesis methods. As shown in Fig.  8 , thermal treatment and solvothermal methods are the two most widely used material synthesis methods. In addition, different catalysts also have their conventional synthesis methods. For example, the synthesis of Cu/C, which usually refers to carbon-coated metal nanoparticles or anchored single atoms, is mainly through thermal treatment. The synthesis of E and E-M is mainly electrochemical methods, especially electrochemical reduction treatment. For EO x and its composites, the solvothermal method, wet chemical method, and electrochemical method are commonly used methods. This analysis is helpful for the screening of target catalyst synthesis methods.

Heatmap showing the number of publications of CO 2 electrocatalysts with different synthesis methods.

The database encompasses various catalyst types and diverse regulation strategies, which can be utilized to guide the design and optimization of novel catalysts. One feasible approach involves integrating multiple strategies by drawing inspiration from well-performing catalysts and regulation methods in the literature, thus facilitating the development of highly efficient catalysts. For example, CuS serves as a potential efficient catalyst for C 2 H 4 production, while nano-sized polymer coatings can enhance the selectivity of C 2 H 4 . Consequently, CuS nanoparticles coated with an a-few-nm-thick polymer layer represent an effective method for selectively producing C 2 H 4 . Similarly, coupling Cu 2 O nanocrystals with (111) facets with functionalized graphene nanosheets can be employed for C 2 H 5 OH production. Furthermore, utilizing fine-tuned domain LLMs is also a viable strategy for developing novel catalysts, and further efforts are required in fine-tuning LLMs and prompt engineering.

Code availability

The scripts utilized to parse articles and extract entities are home-written codes which are publicly available at the github repository https://github.com/cxqwindy/CO2_reduction_electrocatalysts_db . The underlying machine-learning libraries used in this project are all open-source: rxn4chemistry( rxn4chemistry ), ChemDataExtractor ( chemdataextractor.org ) 37 , gensim ( radimrehurek.com ) 48 , PyMuPDF( PyMuPDF ), Pytorch ( www.pytorch.org ) and scikit-learn ( scikit-learn.org ) 49 .

Birdja, Y. Y. et al . Advances and challenges in understanding the electrocatalytic conversion of carbon dioxide to fuels. Nat. Energy 4 , 732–745 (2019).

Article   ADS   CAS   Google Scholar  

Zhong, M. et al . Accelerated discovery of CO 2 electrocatalysts using active machine learning. Nature 581 , 178–183 (2020).

Article   ADS   CAS   PubMed   Google Scholar  

Gao, Y., Wang, L., Chen, X., Du, Y. & Wang, B. Revisiting electrocatalyst design by a knowledge graph of Cu-based catalysts for CO 2 reduction. ACS Catal.   13 , 8525–8534 (2023).

Article   CAS   Google Scholar  

Qiao, J., Liu, Y., Hong, F. & Zhang, J. A review of catalysts for the electroreduction of carbon dioxide to produce low-carbon fuels. Chem. Soc. Rev. 43 , 631–675 (2014).

Article   CAS   PubMed   Google Scholar  

Zheng, T., Jiang, K. & Wang, H. Recent advances in electrochemical CO 2 -to-CO conversion on heterogeneous catalysts. Adv. Mater. 30 , 1802066 (2018).

Article   Google Scholar  

Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559 , 547–555 (2018).

Peng, J. et al . Human- and machine-centred designs of molecules and materials for sustainability and decarbonization. Nat. Rev. Mater. 7 , 991–1009 (2022).

Article   ADS   Google Scholar  

He, T. et al . Similarity of precursors in solid-state synthesis as text-mined from scientific literature. Chem. Mater. 32 , 7861–7873 (2020).

Huang, S. & Cole, J. M. A database of battery materials auto-generated using ChemDataExtractor. Sci. Data 7 , 260 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Paula, A. J. et al . Machine learning and natural language processing enable a data-oriented experimental design approach for producing biochar and hydrochar from biomass. Chem. Mater. 34 , 979–990 (2022).

Kononova, O. et al . Text-mined dataset of inorganic materials synthesis recipes. Sci. data 6 , 203 (2019).

Wang, L. et al . A corpus of CO 2 electrocatalytic reduction process extracted from the scientific literature. Sci. Data 10 , 175 (2023).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Wang, S. et al . GPT-NER: Named entity recognition via large language models. arXiv preprint arXiv:2304.10428 (2023).

Alkaissi, H. & McFarlane, S. I. Artificial hallucinations in ChatGPT: Implications in scientific writing. Cureus 15 (2023).

Bang, Y. et al . A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) , 675-718 (2023).

Azamfirei, R., Kudchadkar, S. R. & Fackler, J. Large language models and the perils of their hallucinations. Crit. Care   27 , 1–2 (2023).

Zheng, Z., Zhang, O., Borgs, C., Chayes, J. T. & Yaghi, O. M. ChatGPT chemistry assistant for text mining and the prediction of MOF synthesis. J. Am. Chem. Soc. 145 , 18048–18062 (2023).

Kumar, K. Geotechnical parrot tales (GPT): Overcoming GPT hallucinations with prompt engineering for geotechnical applications. arXiv preprint arXiv:2304.02138 (2023).

Polak, M. P. & Morgan, D. Extracting accurate materials data from research papers with conversational language models and prompt engineering . arXiv preprint arXiv:2303.05352 (2023).

Hiszpanski, A. M. et al . Nanomaterial synthesis insights from machine learning of scientific articles by extracting, structuring, and visualizing knowledge. J. Chem. Inf. Model. 60 , 2876–2887 (2020).

Liu, R. & McKie, J. Pymupdf. Available at http://pymupdf.readthedocs.io/en/latest/ (2018).

Cruse, K. et al . Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities. Sci. Data 9 , 234 (2022).

Huo, H. et al . Semi-supervised machine-learning classification of materials synthesis procedures. npj Comput. Mater.   5 , 62 (2019).

Blei, D. M., Ng, A. Y. & Jordan, M. I. Latent dirichlet allocation. J. Mach. Learn. Res. 3 , 993–1022 (2003).

Google Scholar  

Breiman, L. Random forests. Mach. Learn. 45 , 5–32 (2001).

Rebholz-Schuhmann, D. et al . The calbc silver standard corpus for biomedical named entities-a study in harmonizing the contributions from four independent named entity taggers. In LREC 2010-7th International Conference on Language Resources and Evaluation (CELI Language & Informat Technol; European Media Lab GmBH; Quaero; META, 2010).

Nakayama, H., Kubo, T., Kamura, J., Taniguchi, Y. & Liang, X. doccano: Text annotation tool for human. Software available from https://github.com/doccano/doccano 34 (2018).

Brown, T. et al . Language models are few-shot learners. Advances in neural information processing systems 33 , 1877–1901 (2020).

Radford, A. et al . Language models are unsupervised multitask learners. OpenAI blog 1 , 9 (2019).

Radford, A. et al . Improving language understanding by generative pre-training. (2018).

Beltagy, I., Lo, K. & Cohan, A. SciBERT: A pretrained language model for scientific text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) ,3615-3620 (2019).

Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9 , 1735–1780 (1997).

Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K. & Dyer, C. Neural architectures for named entity recognition. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ,260-270 (2016).

Corbett, P. & Murray-Rust, P. High-throughput identification of chemistry in life science texts. In International Symposium on Computational Life Science , 107–118 (Springer, 2006).

Hettne, K. M. et al . A dictionary to identify small molecules and drugs in free text. Bioinformatics 25 , 2983–2991 (2009).

Vaucher, A. C. et al . Automated extraction of chemical synthesis actions from experimental procedures. Nat. Commun.  11 , 3601 (2020).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Swain, M. C. & Cole, J. M. ChemDataExtractor: a toolkit for automated extraction of chemical information from the scientific literature. J. Chem. Inf. Model. 56 , 1894–1904 (2016).

Honnibal, M. & Johnson, M. An improved non-monotonic transition system for dependency parsing. In Proceedings of the 2015 conference on empirical methods in natural language processing , 1373–1378 (2015).

Teller, V. Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition (2000).

Wang, L. et al . Benchmark corpus of CO 2 reduction electrocatalysts and synthesis procedures, ScienceDB , https://doi.org/10.57760/sciencedb.13290 (2023).

Wang, L. et al . The extended corpus of CO 2 reduction electrocatalysts and synthesis procedures, ScienceDB , https://doi.org/10.57760/sciencedb.13292 (2023).

Wang, L. et al . A complete dataset of 476 catalytic material synthesis processes. ScienceDB at https://doi.org/10.57760/sciencedb.13293 (2023).

Ma, X. & Hovy, E. End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. arXiv preprint arXiv:1603.01354 (2016).

Plank, B., Søgaard, A. & Goldberg, Y. Multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss. arXiv preprint arXiv:1604.05529 (2016).

Chung, J., Gulcehre, C., Cho, K. & Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014).

Borthwick, A. E. A maximum entropy approach to named entity recognition (New York University, 1999).

Tsuruoka, Y. & Tsujii, J. Bidirectional inference with the easiest-first strategy for tagging sequence data. In proceedings of human language technology conference and conference on empirical methods in natural language processing , 467–474 (2005).

Řehřek, R. & Sojka, P. Software framework for topic modelling with large corpora . (2010).

Pedregosa, F. et al . Scikit-learn: Machine learning in python. J. Mach. Learn. Res.   12 , 2825–2830 (2011).

MathSciNet   Google Scholar  

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Acknowledgements

This work was supported by the National Key Research and Development Plan of China under Grant No. 2022YFF0712200, 2022YFF0711900 and 2021YFA1202802, the Natural Science Foundation of China under Grant No. T2322027, Information Science Database in National Basic Science Data Center under Grant No.NBSDC-DB-25, the Young Elite Scientists Sponsorship Program by Beijing Association for Science and Technology (BYESS2023410), the CAS Pioneer Hundred Talents Program and Youth Innovation Promotion Association CAS.

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These authors contributed equally: Xueqing Chen, Yang Gao, Ludi Wang.

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Laboratory of Big Data Knowledge, Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China

Xueqing Chen, Ludi Wang, Wenjuan Cui & Yi Du

University of Chinese Academy of Sciences, Beijing, 100049, China

Xueqing Chen & Yi Du

CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology (NCNST), Beijing, 100190, China

Yang Gao, Jiamin Huang & Bin Wang

Hangzhou Institute for Advanced Study, UCAS, Hangzhou, 310000, China

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All authors contributed substantively to the work presented in this paper. Conception and Supervision: B. Wang, Y. Du; Data acquisition: L. Wang, X. Chen, Y. Du; Data validation: Y. Gao, B. Wang, J. Huang; Technical validation: L. Wang, X. Chen; Dataset mining: Y. Gao, B. Wang; Writing and Proof reading: L. Wang, Y. Gao, X. Chen, B. Wang, Y. Du.

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Chen, X., Gao, Y., Wang, L. et al. Large language model enhanced corpus of CO 2 reduction electrocatalysts and synthesis procedures. Sci Data 11 , 347 (2024). https://doi.org/10.1038/s41597-024-03180-9

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• Published: 12 December 2023

Examining the role of community resilience and social capital on mental health in public health emergency and disaster response: a scoping review

• C. E. Hall 1 , 2 ,
• H. Wehling 1 ,
• J. Stansfield 3 ,
• J. South 3 ,
• S. K. Brooks 2 ,
• N. Greenberg 2 , 4 ,
• R. Amlôt 1 &
• D. Weston 1  

BMC Public Health volume  23 , Article number:  2482 ( 2023 ) Cite this article

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The ability of the public to remain psychologically resilient in the face of public health emergencies and disasters (such as the COVID-19 pandemic) is a key factor in the effectiveness of a national response to such events. Community resilience and social capital are often perceived as beneficial and ensuring that a community is socially and psychologically resilient may aid emergency response and recovery. This review presents a synthesis of literature which answers the following research questions: How are community resilience and social capital quantified in research?; What is the impact of community resilience on mental wellbeing?; What is the impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, What types of interventions enhance community resilience and social capital?

A scoping review procedure was followed. Searches were run across Medline, PsycInfo, and EMBASE, with search terms covering both community resilience and social capital, public health emergencies, and mental health. 26 papers met the inclusion criteria.

The majority of retained papers originated in the USA, used a survey methodology to collect data, and involved a natural disaster. There was no common method for measuring community resilience or social capital. The association between community resilience and social capital with mental health was regarded as positive in most cases. However, we found that community resilience, and social capital, were initially negatively impacted by public health emergencies and enhanced by social group activities.

Several key recommendations are proposed based on the outcomes from the review, which include: the need for a standardised and validated approach to measuring both community resilience and social capital; that there should be enhanced effort to improve preparedness to public health emergencies in communities by gauging current levels of community resilience and social capital; that community resilience and social capital should be bolstered if areas are at risk of disasters or public health emergencies; the need to ensure that suitable short-term support is provided to communities with high resilience in the immediate aftermath of a public health emergency or disaster; the importance of conducting robust evaluation of community resilience initiatives deployed during the COVID-19 pandemic.

Peer Review reports

For the general population, public health emergencies and disasters (e.g., natural disasters; infectious disease outbreaks; Chemical, Biological, Radiological or Nuclear incidents) can give rise to a plethora of negative outcomes relating to both health (e.g. increased mental health problems [ 1 , 2 , 3 , 4 ]) and the economy (e.g., increased unemployment and decreased levels of tourism [ 4 , 5 , 6 ]). COVID-19 is a current, and ongoing, example of a public health emergency which has affected over 421 million individuals worldwide [ 7 ]. The long term implications of COVID-19 are not yet known, but there are likely to be repercussions for physical health, mental health, and other non-health related outcomes for a substantial time to come [ 8 , 9 ]. As a result, it is critical to establish methods which may inform approaches to alleviate the longer-term negative consequences that are likely to emerge in the aftermath of both COVID-19 and any future public health emergency.

The definition of resilience often differs within the literature, but ultimately resilience is considered a dynamic process of adaptation. It is related to processes and capabilities at the individual, community and system level that result in good health and social outcomes, in spite of negative events, serious threats and hazards [ 10 ]. Furthermore, Ziglio [ 10 ] refers to four key types of resilience capacity: adaptive, the ability to withstand and adjust to unfavourable conditions and shocks; absorptive, the ability to withstand but also to recover and manage using available assets and skills; anticipatory, the ability to predict and minimize vulnerability; and transformative, transformative change so that systems better cope with new conditions.

There is no one settled definition of community resilience (CR). However, it generally relates to the ability of a community to withstand, adapt and permit growth in adverse circumstances due to social structures, networks and interdependencies within the community [ 11 ]. Social capital (SC) is considered a major determinant of CR [ 12 , 13 ], and reflects strength of a social network, community reciprocity, and trust in people and institutions [ 14 ]. These aspects of community are usually conceptualised primarily as protective factors that enable communities to cope and adapt collectively to threats. SC is often broken down into further categories [ 15 ], for example: cognitive SC (i.e. perceptions of community relations, such as trust, mutual help and attachment) and structural SC (i.e. what actually happens within the community, such as participation, socialising) [ 16 ]; or, bonding SC (i.e. connections among individuals who are emotionally close, and result in bonds to a particular group [ 17 ]) and bridging SC (i.e. acquaintances or individuals loosely connected that span different social groups [ 18 ]). Generally, CR is perceived to be primarily beneficial for multiple reasons (e.g. increased social support [ 18 , 19 ], protection of mental health [ 20 , 21 ]), and strengthening community resilience is a stated health goal of the World Health Organisation [ 22 ] when aiming to alleviate health inequalities and protect wellbeing. This is also reflected by organisations such as Public Health England (now split into the UK Health Security Agency and the Office for Health Improvement and Disparities) [ 23 ] and more recently, CR has been targeted through the endorsement of Community Champions (who are volunteers trained to support and to help improve health and wellbeing. Community Champions also reflect their local communities in terms of population demographics for example age, ethnicity and gender) as part of the COVID-19 response in the UK (e.g. [ 24 , 25 ]).

Despite the vested interest in bolstering communities, the research base establishing: how to understand and measure CR and SC; the effect of CR and SC, both during and following a public health emergency (such as the COVID-19 pandemic); and which types of CR or SC are the most effective to engage, is relatively small. Given the importance of ensuring resilience against, and swift recovery from, public health emergencies, it is critically important to establish and understand the evidence base for these approaches. As a result, the current review sought to answer the following research questions: (1) How are CR and SC quantified in research?; (2) What is the impact of community resilience on mental wellbeing?; (3) What is the impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, (4) What types of interventions enhance community resilience and social capital?

By collating research in order to answer these research questions, the authors have been able to propose several key recommendations that could be used to both enhance and evaluate CR and SC effectively to facilitate the long-term recovery from COVID-19, and also to inform the use of CR and SC in any future public health disasters and emergencies.

A scoping review methodology was followed due to the ease of summarising literature on a given topic for policy makers and practitioners [ 26 ], and is detailed in the following sections.

Identification of relevant studies

An initial search strategy was developed by authors CH and DW and included terms which related to: CR and SC, given the absence of a consistent definition of CR, and the link between CR and SC, the review focuses on both CR and SC to identify as much relevant literature as possible (adapted for purpose from Annex 1: [ 27 ], as well as through consultation with review commissioners); public health emergencies and disasters [ 28 , 29 , 30 , 31 ], and psychological wellbeing and recovery (derived a priori from literature). To ensure a focus on both public health and psychological research, the final search was carried across Medline, PsycInfo, and EMBASE using OVID. The final search took place on the 18th of May 2020, the search strategy used for all three databases can be found in Supplementary file 1 .

Selection criteria

The inclusion and exclusion criteria were developed alongside the search strategy. Initially the criteria were relatively inclusive and were subject to iterative development to reflect the authors’ familiarisation with the literature. For example, the decision was taken to exclude research which focused exclusively on social support and did not mention communities as an initial title/abstract search suggested that the majority of this literature did not meet the requirements of our research question.

The full and final inclusion and exclusion criteria used can be found in Supplementary file 2 . In summary, authors decided to focus on the general population (i.e., non-specialist, e.g. non-healthcare worker or government official) to allow the review to remain community focused. The research must also have assessed the impact of CR and/or SC on mental health and wellbeing, resilience, and recovery during and following public health emergencies and infectious disease outbreaks which affect communities (to ensure the research is relevant to the review aims), have conducted primary research, and have a full text available or provided by the first author when contacted.

Charting the data

All papers were first title and abstract screened by CH or DW. Papers then were full text reviewed by CH to ensure each paper met the required eligibility criteria, if unsure about a paper it was also full text reviewed by DW. All papers that were retained post full-text review were subjected to a standardised data extraction procedure. A table was made for the purpose of extracting the following data: title, authors, origin, year of publication, study design, aim, disaster type, sample size and characteristics, variables examined, results, restrictions/limitations, and recommendations. Supplementary file 3 details the charting the data process.

Analytical method

Data was synthesised using a Framework approach [ 32 ], a common method for analysing qualitative research. This method was chosen as it was originally used for large-scale social policy research [ 33 ] as it seeks to identify: what works, for whom, in what conditions, and why [ 34 ]. This approach is also useful for identifying commonalities and differences in qualitative data and potential relationships between different parts of the data [ 33 ]. An a priori framework was established by CH and DW. Extracted data was synthesised in relation to each research question, and the process was iterative to ensure maximum saturation using the available data.

Study selection

The final search strategy yielded 3584 records. Following the removal of duplicates, 2191 records remained and were included in title and abstract screening. A PRISMA flow diagram is presented in Fig.  1 .

PRISMA flow diagram

At the title and abstract screening stage, the process became more iterative as the inclusion criteria were developed and refined. For the first iteration of screening, CH or DW sorted all records into ‘include,’ ‘exclude,’ and ‘unsure’. All ‘unsure’ papers were re-assessed by CH, and a random selection of ~ 20% of these were also assessed by DW. Where there was disagreement between authors the records were retained, and full text screened. The remaining papers were reviewed by CH, and all records were categorised into ‘include’ and ‘exclude’. Following full-text screening, 26 papers were retained for use in the review.

Study characteristics

This section of the review addresses study characteristics of those which met the inclusion criteria, which comprises: date of publication, country of origin, study design, study location, disaster, and variables examined.

Date of publication

Publication dates across the 26 papers spanned from 2008 to 2020 (see Fig.  2 ). The number of papers published was relatively low and consistent across this timescale (i.e. 1–2 per year, except 2010 and 2013 when none were published) up until 2017 where the number of papers peaked at 5. From 2017 to 2020 there were 15 papers published in total. The amount of papers published in recent years suggests a shift in research and interest towards CR and SC in a disaster/ public health emergency context.

Graph to show retained papers date of publication

Country of origin

The locations of the first authors’ institutes at the time of publication were extracted to provide a geographical spread of the retained papers. The majority originated from the USA [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ], followed by China [ 42 , 43 , 44 , 45 , 46 ], Japan [ 47 , 48 , 49 , 50 ], Australia [ 51 , 52 , 53 ], The Netherlands [ 54 , 55 ], New Zealand [ 56 ], Peru [ 57 ], Iran [ 58 ], Austria [ 59 ], and Croatia [ 60 ].

There were multiple methodological approaches carried out across retained papers. The most common formats included surveys or questionnaires [ 36 , 37 , 38 , 42 , 46 , 47 , 48 , 49 , 50 , 53 , 54 , 55 , 57 , 59 ], followed by interviews [ 39 , 40 , 43 , 51 , 52 , 60 ]. Four papers used both surveys and interviews [ 35 , 41 , 45 , 58 ], and two papers conducted data analysis (one using open access data from a Social Survey [ 44 ] and one using a Primary Health Organisations Register [ 56 ]).

Study location

The majority of the studies were carried out in Japan [ 36 , 42 , 44 , 47 , 48 , 49 , 50 ], followed by the USA [ 35 , 37 , 38 , 39 , 40 , 41 ], China [ 43 , 45 , 46 , 53 ], Australia [ 51 , 52 ], and the UK [ 54 , 55 ]. The remaining studies were carried out in Croatia [ 60 ], Peru [ 57 ], Austria [ 59 ], New Zealand [ 56 ] and Iran [ 58 ].

Multiple different types of disaster were researched across the retained papers. Earthquakes were the most common type of disaster examined [ 45 , 47 , 49 , 50 , 53 , 56 , 57 , 58 ], followed by research which assessed the impact of two disastrous events which had happened in the same area (e.g. Hurricane Katrina and the Deepwater Horizon oil spill in Mississippi, and the Great East Japan earthquake and Tsunami; [ 36 , 37 , 38 , 42 , 44 , 48 ]). Other disaster types included: flooding [ 51 , 54 , 55 , 59 , 60 ], hurricanes [ 35 , 39 , 41 ], infectious disease outbreaks [ 43 , 46 ], oil spillage [ 40 ], and drought [ 52 ].

Variables of interest examined

Across the 26 retained papers: eight referred to examining the impact of SC [ 35 , 37 , 39 , 41 , 46 , 49 , 55 , 60 ]; eight examined the impact of cognitive and structural SC as separate entities [ 40 , 42 , 45 , 48 , 50 , 54 , 57 , 59 ]; one examined bridging and bonding SC as separate entities [ 58 ]; two examined the impact of CR [ 38 , 56 ]; and two employed a qualitative methodology but drew findings in relation to bonding and bridging SC, and SC generally [ 51 , 52 ]. Additionally, five papers examined the impact of the following variables: ‘community social cohesion’ [ 36 ], ‘neighbourhood connectedness’ [ 44 ], ‘social support at the community level’ [ 47 ], ‘community connectedness’ [ 43 ] and ‘sense of community’ [ 53 ]. Table  1 provides additional details on this.

How is CR and SC measured or quantified in research?

The measures used to examine CR and SC are presented Table  1 . It is apparent that there is no uniformity in how SC or CR is measured across the research. Multiple measures are used throughout the retained studies, and nearly all are unique. Additionally, SC was examined at multiple different levels (e.g. cognitive and structural, bonding and bridging), and in multiple different forms (e.g. community connectedness, community cohesion).

What is the association between CR and SC on mental wellbeing?

To best compare research, the following section reports on CR, and facets of SC separately. Please see Supplementary file 4  for additional information on retained papers methods of measuring mental wellbeing.

• Community resilience

CR relates to the ability of a community to withstand, adapt and permit growth in adverse circumstances due to social structures, networks and interdependencies within the community [ 11 ].

The impact of CR on mental wellbeing was consistently positive. For example, research indicated that there was a positive association between CR and number of common mental health (i.e. anxiety and mood) treatments post-disaster [ 56 ]. Similarly, other research suggests that CR is positively related to psychological resilience, which is inversely related to depressive symptoms) [ 37 ]. The same research also concluded that CR is protective of psychological resilience and is therefore protective of depressive symptoms [ 37 ].

• Social capital

SC reflects the strength of a social network, community reciprocity, and trust in people and institutions [ 14 ]. These aspects of community are usually conceptualised primarily as protective factors that enable communities to cope and adapt collectively to threats.

There were inconsistencies across research which examined the impact of abstract SC (i.e. not refined into bonding/bridging or structural/cognitive) on mental wellbeing. However, for the majority of cases, research deems SC to be beneficial. For example, research has concluded that, SC is protective against post-traumatic stress disorder [ 55 ], anxiety [ 46 ], psychological distress [ 50 ], and stress [ 46 ]. Additionally, SC has been found to facilitate post-traumatic growth [ 38 ], and also to be useful to be drawn upon in times of stress [ 52 ], both of which could be protective of mental health. Similarly, research has also found that emotional recovery following a disaster is more difficult for those who report to have low levels of SC [ 51 ].

Conversely, however, research has also concluded that when other situational factors (e.g. personal resources) were controlled for, a positive relationship between community resources and life satisfaction was no longer significant [ 60 ]. Furthermore, some research has concluded that a high level of SC can result in a community facing greater stress immediately post disaster. Indeed, one retained paper found that high levels of SC correlate with higher levels of post-traumatic stress immediately following a disaster [ 39 ]. However, in the later stages following a disaster, this relationship can reverse, with SC subsequently providing an aid to recovery [ 41 ]. By way of explanation, some researchers have suggested that communities with stronger SC carry the greatest load in terms of helping others (i.e. family, friends and neighbours) as well as themselves immediately following the disaster, but then as time passes the communities recover at a faster rate as they are able to rely on their social networks for support [ 41 ].

Cognitive and structural social capital

Cognitive SC refers to perceptions of community relations, such as trust, mutual help and attachment, and structural SC refers to what actually happens within the community, such as participation, socialising [ 16 ].

Cognitive SC has been found to be protective [ 49 ] against PTSD [ 54 , 57 ], depression [ 40 , 54 ]) mild mood disorder; [ 48 ]), anxiety [ 48 , 54 ] and increase self-efficacy [ 59 ].

For structural SC, research is again inconsistent. On the one hand, structural SC has been found to: increase perceived self-efficacy, be protective of depression [ 40 ], buffer the impact of housing damage on cognitive decline [ 42 ] and provide support during disasters and over the recovery period [ 59 ]. However, on the other hand, it has been found to have no association with PTSD [ 54 , 57 ] or depression, and is also associated with a higher prevalence of anxiety [ 54 ]. Similarly, it is also suggested by additional research that structural SC can harm women’s mental health, either due to the pressure of expectations to help and support others or feelings of isolation [ 49 ].

Bonding and bridging social capital

Bonding SC refers to connections among individuals who are emotionally close, and result in bonds to a particular group [ 17 ], and bridging SC refers to acquaintances or individuals loosely connected that span different social groups [ 18 ].

One research study concluded that both bonding and bridging SC were protective against post-traumatic stress disorder symptoms [ 58 ]. Bridging capital was deemed to be around twice as effective in buffering against post-traumatic stress disorder than bonding SC [ 58 ].

Other community variables

Community social cohesion was significantly associated with a lower risk of post-traumatic stress disorder symptom development [ 35 ], and this was apparent even whilst controlling for depressive symptoms at baseline and disaster impact variables (e.g. loss of family member or housing damage) [ 36 ]. Similarly, sense of community, community connectedness, social support at the community level and neighbourhood connectedness all provided protective benefits for a range of mental health, wellbeing and recovery variables, including: depression [ 53 ], subjective wellbeing (in older adults only) [ 43 ], psychological distress [ 47 ], happiness [ 44 ] and life satisfaction [ 53 ].

Research has also concluded that community level social support is protective against mild mood and anxiety disorder, but only for individuals who have had no previous disaster experience [ 48 ]. Additionally, a study which separated SC into social cohesion and social participation concluded that at a community level, social cohesion is protective against depression [ 49 ] whereas social participation at community level is associated with an increased risk of depression amongst women [ 49 ].

What is the impact of Infectious disease outbreaks / disasters and emergencies on community resilience?

From a cross-sectional perspective, research has indicated that disasters and emergencies can have a negative effect on certain types of SC. Specifically, cognitive SC has been found to be impacted by disaster impact, whereas structural SC has gone unaffected [ 45 ]. Disaster impact has also been shown to have a negative effect on community relationships more generally [ 52 ].

Additionally, of the eight studies which collected data at multiple time points [ 35 , 36 , 41 , 42 , 47 , 49 , 56 , 60 ], three reported the effect of a disaster on the level of SC within a community [ 40 , 42 , 49 ]. All three of these studies concluded that disasters may have a negative impact on the levels of SC within a community. The first study found that the Deepwater Horizon oil spill had a negative effect on SC and social support, and this in turn explained an overall increase in the levels of depression within the community [ 40 ]. A possible explanation for the negative effect lays in ‘corrosive communities’, known for increased social conflict and reduced social support, that are sometimes created following oil spills [ 40 ]. It is proposed that corrosive communities often emerge due to a loss of natural resources that bring social groups together (e.g., for recreational activities), as well as social disparity (e.g., due to unequal distribution of economic impact) becoming apparent in the community following disaster [ 40 ]. The second study found that SC (in the form of social cohesion, informal socialising and social participation) decreased after the 2011 earthquake and tsunami in Japan; it was suggested that this change correlated with incidence of cognitive decline [ 42 ]. However, the third study reported more mixed effects based on physical circumstances of the communities’ natural environment: Following an earthquake, those who lived in mountainous areas with an initial high level of pre-community SC saw a decrease in SC post disaster [ 49 ]. However, communities in flat areas (which were home to younger residents and had a higher population density) saw an increase in SC [ 49 ]. It was proposed that this difference could be due to the need for those who lived in mountainous areas to seek prolonged refuge due to subsequent landslides [ 49 ].

What types of intervention enhance CR and SC and protect survivors?

There were mixed effects across the 26 retained papers when examining the effect of CR and SC on mental wellbeing. However, there is evidence that an increase in SC [ 56 , 57 ], with a focus on cognitive SC [ 57 ], namely by: building social networks [ 45 , 51 , 53 ], enhancing feelings of social cohesion [ 35 , 36 ] and promoting a sense of community [ 53 ], can result in an increase in CR and potentially protect survivors’ wellbeing and mental health following a disaster. An increase in SC may also aid in decreasing the need for individual psychological interventions in the aftermath of a disaster [ 55 ]. As a result, recommendations and suggested methods to bolster CR and SC from the retained papers have been extracted and separated into general methods, preparedness and policy level implementation.

General methods

Suggested methods to build SC included organising recreational activity-based groups [ 44 ] to broaden [ 51 , 53 ] and preserve current social networks [ 42 ], introducing initiatives to increase social cohesion and trust [ 51 ], and volunteering to increase the number of social ties between residents [ 59 ]. Research also notes that it is important to take a ‘no one left behind approach’ when organising recreational and social community events, as failure to do so could induce feelings of isolation for some members of the community [ 49 ]. Furthermore, gender differences should also be considered as research indicates that males and females may react differently to community level SC (as evidence suggests males are instead more impacted by individual level SC; in comparison to women who have larger and more diverse social networks [ 49 ]). Therefore, interventions which aim to raise community level social participation, with the aim of expanding social connections and gaining support, may be beneficial [ 42 , 47 ].

Preparedness

In order to prepare for disasters, it may be beneficial to introduce community-targeted methods or interventions to increase levels of SC and CR as these may aid in ameliorating the consequences of a public health emergency or disaster [ 57 ]. To indicate which communities have low levels of SC, one study suggests implementing a 3-item scale of social cohesion to map areas and target interventions [ 42 ].

It is important to consider that communities with a high level of SC may have a lower level of risk perception, due to the established connections and supportive network they have with those around them [ 61 ]. However, for the purpose of preparedness, this is not ideal as perception of risk is a key factor when seeking to encourage behavioural adherence. This could be overcome by introducing communication strategies which emphasise the necessity of social support, but also highlights the need for additional measures to reduce residual risk [ 59 ]. Furthermore, support in the form of financial assistance to foster current community initiatives may prove beneficial to rural areas, for example through the use of an asset-based community development framework [ 52 ].

Policy level

At a policy level, the included papers suggest a range of ways that CR and SC could be bolstered and used. These include: providing financial support for community initiatives and collective coping strategies, (e.g. using asset-based community development [ 52 ]); ensuring policies for long-term recovery focus on community sustainable development (e.g. community festival and community centre activities) [ 44 ]; and development of a network amongst cooperative corporations formed for reconstruction and to organise self-help recovery sessions among residents of adjacent areas [ 58 ].

This scoping review sought to synthesise literature concerning the role of SC and CR during public health emergencies and disasters. Specifically, in this review we have examined: the methods used to measure CR and SC; the impact of CR and SC on mental wellbeing during disasters and emergencies; the impact of disasters and emergencies on CR and SC; and the types of interventions which can be used to enhance CR. To do this, data was extracted from 26 peer-reviewed journal articles. From this synthesis, several key themes have been identified, which can be used to develop guidelines and recommendations for deploying CR and SC in a public health emergency or disaster context. These key themes and resulting recommendations are summarised below.

Firstly, this review established that there is no consistent or standardised approach to measuring CR or SC within the general population. This finding is consistent with a review conducted by the World Health Organization which concludes that despite there being a number of frameworks that contain indicators across different determinants of health, there is a lack of consensus on priority areas for measurement and no widely accepted indicator [ 27 ]. As a result, there are many measures of CR and SC apparent within the literature (e.g., [ 62 , 63 ]), an example of a developed and validated measure is provided by Sherrieb, Norris and Galea [ 64 ]. Similarly, the definitions of CR and SC differ widely between researchers, which created a barrier to comparing and summarising information. Therefore, future research could seek to compare various interpretations of CR and to identify any overlapping concepts. However, a previous systemic review conducted by Patel et al. (2017) concludes that there are nine core elements of CR (local knowledge, community networks and relationships, communication, health, governance and leadership, resources, economic investment, preparedness, and mental outlook), with 19 further sub-elements therein [ 30 ]. Therefore, as CR is a multi-dimensional construct, the implications from the findings are that multiple aspects of social infrastructure may need to be considered.

Secondly, our synthesis of research concerning the role of CR and SC for ensuring mental health and wellbeing during, or following, a public health emergency or disaster revealed mixed effects. Much of the research indicates either a generally protective effect on mental health and wellbeing, or no effect; however, the literature demonstrates some potential for a high level of CR/SC to backfire and result in a negative effect for populations during, or following, a public health emergency or disaster. Considered together, our synthesis indicates that cognitive SC is the only facet of SC which was perceived as universally protective across all retained papers. This is consistent with a systematic review which also concludes that: (a) community level cognitive SC is associated with a lower risk of common mental disorders, while; (b) community level structural SC had inconsistent effects [ 65 ].

Further examination of additional data extracted from studies which found that CR/SC had a negative effect on mental health and wellbeing revealed no commonalities that might explain these effects (Please see Supplementary file 5 for additional information)

One potential explanation may come from a retained paper which found that high levels of SC result in an increase in stress level immediately post disaster [ 41 ]. This was suggested to be due to individuals having greater burdens due to wishing to help and support their wide networks as well as themselves. However, as time passes the levels of SC allow the community to come together and recover at a faster rate [ 41 ]. As this was the only retained paper which produced this finding, it would be beneficial for future research to examine boundary conditions for the positive effects of CR/SC; that is, to explore circumstances under which CR/SC may be more likely to put communities at greater risk. This further research should also include additional longitudinal research to validate the conclusions drawn by [ 41 ] as resilience is a dynamic process of adaption.

Thirdly, disasters and emergencies were generally found to have a negative effect on levels of SC. One retained paper found a mixed effect of SC in relation to an earthquake, however this paper separated participants by area in which they lived (i.e., mountainous vs. flat), which explains this inconsistent effect [ 49 ]. Dangerous areas (i.e. mountainous) saw a decrease in community SC in comparison to safer areas following the earthquake (an effect the authors attributed to the need to seek prolonged refuge), whereas participants from the safer areas (which are home to younger residents with a higher population density) saw an increase in SC [ 49 ]. This is consistent with the idea that being able to participate socially is a key element of SC [ 12 ]. Overall, however, this was the only retained paper which produced a variable finding in relation to the effect of disaster on levels of CR/SC.

Finally, research identified through our synthesis promotes the idea of bolstering SC (particularly cognitive SC) and cohesion in communities likely to be affected by disaster to improve levels of CR. This finding provides further understanding of the relationship between CR and SC; an association that has been reported in various articles seeking to provide conceptual frameworks (e.g., [ 66 , 67 ]) as well as indicator/measurement frameworks [ 27 ]. Therefore, this could be done by creating and promoting initiatives which foster SC and create bonds within the community. Papers included in the current review suggest that recreational-based activity groups and volunteering are potential methods for fostering SC and creating community bonds [ 44 , 51 , 59 ]. Similarly, further research demonstrates that feelings of social cohesion are enhanced by general social activities (e.g. fairs and parades [ 18 ]). Also, actively encouraging activities, programs and interventions which enhance connectedness and SC have been reported to be desirable to increase CR [ 68 ]. This suggestion is supported by a recent scoping review of literature [ 67 ] examined community champion approaches for the COVID-19 pandemic response and recovery and established that creating and promoting SC focused initiatives within the community during pandemic response is highly beneficial [ 67 ]. In terms of preparedness, research states that it may be beneficial for levels of SC and CR in communities at risk to be assessed, to allow targeted interventions where the population may be at most risk following an incident [ 42 , 44 ]. Additionally, from a more critical perspective, we acknowledge that ‘resilience’ can often be perceived as a focus on individual capacity to adapt to adversity rather than changing or mitigating the causes of adverse conditions [ 69 , 70 ]. Therefore, CR requires an integrated system approach across individual, community and structural levels [ 17 ]. Also, it is important that community members are engaged in defining and agreeing how community resilience is measured [ 27 ] rather than it being imposed by system leads or decision-makers.

In the aftermath of the pandemic, is it expected that there will be long-term repercussions both from an economic [ 8 ] and a mental health perspective [ 71 ]. Furthermore, the findings from this review suggest that although those in areas with high levels of SC may be negatively affected in the acute stage, as time passes, they have potential to rebound at a faster rate than those with lower levels of SC. Ongoing evaluation of the effectiveness of current initiatives as the COVID-19 pandemic progresses into a recovery phase will be invaluable for supplementing the evidence base identified through this review.

• Recommendations

As a result of this review, a number of recommendations are suggested for policy and practice during public health emergencies and recovery.

Future research should seek to establish a standardised and validated approach to measuring and defining CR and SC within communities. There are ongoing efforts in this area, for example [ 72 ]. Additionally, community members should be involved in the process of defining how CR is measured.

There should be an enhanced effort to improve preparedness for public health emergencies and disasters in local communities by gauging current levels of SC and CR within communities using a standardised measure. This approach could support specific targeting of populations with low levels of CR/SC in case of a disaster or public health emergency, whilst also allowing for consideration of support for those with high levels of CR (as these populations can be heavily impacted initially following a disaster). By distinguishing levels of SC and CR, tailored community-centred approaches could be implemented, such as those listed in a guide released by PHE in 2015 [ 73 ].

CR and SC (specifically cognitive SC) should be bolstered if communities are at risk of experiencing a disaster or public health emergency. This can be achieved by using interventions which aim to increase a sense of community and create new social ties (e.g., recreational group activities, volunteering). Additionally, when aiming to achieve this, it is important to be mindful of the risk of increased levels of CR/SC to backfire, as well as seeking to advocate an integrated system approach across individual, community and structural levels.

It is necessary to be aware that although communities with high existing levels of resilience / SC may experience short-term negative consequences following a disaster, over time these communities might be able to recover at a faster rate. It is therefore important to ensure that suitable short-term support is provided to these communities in the immediate aftermath of a public health emergency or disaster.

Robust evaluation of the community resilience initiatives deployed during the COVID-19 pandemic response is essential to inform the evidence base concerning the effectiveness of CR/ SC. These evaluations should continue through the response phase and into the recovery phase to help develop our understanding of the long-term consequences of such interventions.

Limitations

Despite this review being the first in this specific topic area, there are limitations that must be considered. Firstly, it is necessary to note that communities are generally highly diverse and the term ‘community’ in academic literature is a subject of much debate (see: [ 74 ]), therefore this must be considered when comparing and collating research involving communities. Additionally, the measures of CR and SC differ substantially across research, including across the 26 retained papers used in the current review. This makes the act of comparing and collating research findings very difficult. This issue is highlighted as a key outcome from this review, and suggestions for how to overcome this in future research are provided. Additionally, we acknowledge that there will be a relationship between CR & SC even where studies measure only at individual or community level. A review [ 75 ] on articulating a hypothesis of the link to health inequalities suggests that wider structural determinants of health need to be accounted for. Secondly, despite the final search strategy encompassing terms for both CR and SC, only one retained paper directly measured CR; thus, making the research findings more relevant to SC. Future research could seek to focus on CR to allow for a comparison of findings. Thirdly, the review was conducted early in the COVID-19 pandemic and so does not include more recent publications focusing on resilience specifically in the context of COVID-19. Regardless of this fact, the synthesis of, and recommendations drawn from, the reviewed studies are agnostic to time and specific incident and contain critical elements necessary to address as the pandemic moves from response to recovery. Further research should review the effectiveness of specific interventions during the COVID-19 pandemic for collation in a subsequent update to this current paper. Fourthly, the current review synthesises findings from countries with individualistic and collectivistic cultures, which may account for some variation in the findings. Lastly, despite choosing a scoping review method for ease of synthesising a wide literature base for use by public health emergency researchers in a relatively tight timeframe, there are disadvantages of a scoping review approach to consider: (1) quality appraisal of retained studies was not carried out; (2) due to the broad nature of a scoping review, more refined and targeted reviews of literature (e.g., systematic reviews) may be able to provide more detailed research outcomes. Therefore, future research should seek to use alternative methods (e.g., empirical research, systematic reviews of literature) to add to the evidence base on CR and SC impact and use in public health practice.

This review sought to establish: (1) How CR and SC are quantified in research?; (2) The impact of community resilience on mental wellbeing?; (3) The impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, (4) What types of interventions enhance community resilience and social capital?. The chosen search strategy yielded 26 relevant papers from which we were able extract information relating to the aims of this review.

Results from the review revealed that CR and SC are not measured consistently across research. The impact of CR / SC on mental health and wellbeing during emergencies and disasters is mixed (with some potential for backlash), however the literature does identify cognitive SC as particularly protective. Although only a small number of papers compared CR or SC before and after a disaster, the findings were relatively consistent: SC or CR is negatively impacted by a disaster. Methods suggested to bolster SC in communities were centred around social activities, such as recreational group activities and volunteering. Recommendations for both research and practice (with a particular focus on the ongoing COVID-19 pandemic) are also presented.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Social Capital

Zortea TC, Brenna CT, Joyce M, McClelland H, Tippett M, Tran MM, et al. The impact of infectious disease-related public health emergencies on suicide, suicidal behavior, and suicidal thoughts. Crisis. 2020;42(6):474–87.

Article   PubMed   PubMed Central   Google Scholar  

Davis JR, Wilson S, Brock-Martin A, Glover S, Svendsen ER. The impact of disasters on populations with health and health care disparities. Disaster Med Pub Health Prep. 2010;4(1):30.

Article   Google Scholar  

Francescutti LH, Sauve M, Prasad AS. Natural disasters and healthcare: lessons to be learned. Healthc Manage Forum. 2017;30(1):53–5.

Article   PubMed   Google Scholar  

Jones L, Palumbo D, Brown D. Coronavirus: How the pandemic has changed the world economy. BBC News; 2021. Accessible at: https://www.bbc.co.uk/news/business-51706225 .

Below R, Wallemacq P. Annual disaster statistical review 2017. Brussels: CRED, Centre for Research on the Epidemiology of Disasters; 2018.

Google Scholar  

Qiu W, Chu C, Mao A, Wu J. The impacts on health, society, and economy of SARS and H7N9 outbreaks in China: a case comparison study. J Environ Public Health. 2018;2018:2710185.

Worldometer. COVID-19 coronavirus pandemic. 2021.

Harari D, Keep M. Coronavirus: economic impact house of commons library. Briefing Paper (Number 8866); 2021. Accessible at: https://commonslibrary.parliament.uk/research-briefings/cbp-8866/ .

Nabavi N. Covid-19: pandemic will cast a long shadow on mental health, warns England’s CMO. BMJ. 2021;373:n1655.

Ziglio E. Strengthening resilience: a priority shared by health 2020 and the sustainable development goals. No. WHO/EURO: 2017-6509-46275-66939. World Health Organization; Regional Office for Europe; 2017.

Asadzadeh A, Kotter T, Salehi P, Birkmann J. Operationalizing a concept: the systematic review of composite indicator building for measuring community disaster resilience. Int J Disaster Risk Reduct. 2017;25:147.

Sherrieb K, Norris F, Galea S. Measuring capacities for community resilience. Soc Indicators Res. 2010;99(2):227.

Poortinga W. Community resilience and health: the role of bonding, bridging, and linking aspects of social capital. Health Place. 2011;18(2):286–95.

Ferlander S. The importance of different forms of social capital for health. Acta Sociol. 2007;50(2):115–28.

Nakagawa Y, Shaw R. Social capital: a missing link to disaster recovery. Int J Mass Emerge Disasters. 2004;22(1):5–34.

Grootaert C, Narayan D, Jones VN, Woolcock M. Measuring social capital: an integrated questionnaire. Washington, DC: World Bank Working Paper, No. 18; 2004.

Adler PS, Kwon SW. Social capital: prospects for a new concept. Acad Manage Rev. 2002;27(1):17–40.

Aldrich DP, Meyer MA. Social capital and community resilience. Am Behav Sci. 2015;59(2):254–69.

Rodriguez-Llanes JM, Vos F, Guha-Sapir D. Measuring psychological resilience to disasters: are evidence-based indicators an achievable goal? Environ Health. 2013;12(1):115.

De Silva MJ, McKenzie K, Harpham T, Huttly SR. Social capital and mental Illness: a systematic review. J Epidemiol Community Health. 2005;59(8):619–27.

Bonanno GA, Galea S, Bucciarelli A, Vlahov D. Psychological resilience after disaster: New York City in the aftermath of the september 11th terrorist attack. Psychol Sci. 2006;17(3):181.

World Health Organization. Health 2020: a European policy framework and strategy for the 21st century. World Health Organization. Regional Office for Europe; 2013.

Public Health England. Community-Centred Public Health: Taking a Whole System Approach. 2020.

SPI-B. The role of Community Champion networks to increase engagement in the context of COVID19: Evidence and best practice. 2021.

Public Health England. Community champions: A rapid scoping review of community champion approaches for the pandemic response and recovery. 2021.

Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32.

World Health Organisation. WHO health evidence network synthesis report: what quantitative and qualitative methods have been developed to measure health-related community resilience at a national and local level. 2018.

Hall C, Williams N, Gauntlett L, Carter H, Amlôt R, Peterson L et al. Findings from systematic review of public perceptions and responses. PROACTIVE EU. Deliverable 1.1. 2019. Accessible at: https://proactive-h2020.eu/wp-content/uploads/2021/04/PROACTIVE_20210312_D1.1_V5_PHE_Systematic-Review-of-Public-Perceptions-and-Responses_revised.pdf .

Weston D, Ip A, Amlôt R. Examining the application of behaviour change theories in the context of Infectious disease outbreaks and emergency response: a review of reviews. BMC Public Health. 2020;20(1):1483.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Patel SS, Rogers MB, Amlôt R, Rubin GJ. What do we mean by ‘community resilience’? A systematic literature review of how it is defined in the literature. PLoS Curr. 2017;9:ecurrents.dis.db775aff25efc5ac4f0660ad9c9f7db2.

PubMed   PubMed Central   Google Scholar  

Brooks SK, Weston D, Wessely S, Greenberg N. Effectiveness and acceptability of brief psychoeducational interventions after potentially traumatic events: a systematic review. Eur J Psychotraumatology. 2021;12(1):1923110.

Pawson R, Greenhalgh T, Harvey G, Walshe K. Realist review-a new method of systematic review designed for complex policy interventions. J Health Serv Res Policy. 2005;10(1_suppl):21–34.

Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol. 2013;13:1–8.

Bearman M, Dawson P. Qualitative synthesis and systematic review in health professions education. Med Educ. 2013;47(3):252–60.

Heid AR, Pruchno R, Cartwright FP, Wilson-Genderson M. Exposure to Hurricane Sandy, neighborhood collective efficacy, and post-traumatic stress symptoms in older adults. Aging Ment Health. 2017;21(7):742–50.

Hikichi H, Aida J, Tsuboya T, Kondo K, Kawachi I. Can community social cohesion prevent posttraumatic stress disorder in the aftermath of a disaster? A natural experiment from the 2011 Tohoku Earthquake and tsunami. Am J Epidemiol. 2016;183(10):902–10.

Lee J, Blackmon BJ, Cochran DM, Kar B, Rehner TA, Gunnell MS. Community resilience, psychological resilience, and depressive symptoms: an examination of the Mississippi Gulf Coast 10 years after Hurricane Katrina and 5 years after the Deepwater Horizon oil spill. Disaster med. 2018;12(2):241–8.

Lee J, Blackmon BJ, Lee JY, Cochran DM Jr, Rehner TA. An exploration of posttraumatic growth, loneliness, depression, resilience, and social capital among survivors of Hurricane Katrina and the deepwater Horizon oil spill. J Community Psychol. 2019;47(2):356–70.

Lowe SR, Sampson L, Gruebner O, Galea S. Psychological resilience after Hurricane Sandy: the influence of individual- and community-level factors on mental health after a large-scale natural disaster. PLoS One. 2015;10(5):e0125761.

Rung AL, Gaston S, Robinson WT, Trapido EJ, Peters ES. Untangling the disaster-depression knot: the role of social ties after deepwater Horizon. Soc Sci Med. 2017;177:19–26.

Weil F, Lee MR, Shihadeh ES. The burdens of social capital: how socially-involved people dealt with stress after Hurricane Katrina. Soc Sci Res. 2012;41(1):110–9.

Hikichi H, Aida J, Matsuyama Y, Tsuboya T, Kondo K, Kawachi I. Community-level social capital and cognitive decline after a Natural Disaster: a natural experiment from the 2011 Great East Japan Earthquake and Tsunami. Soc Sci Med. 2018;257:111981.

Lau AL, Chi I, Cummins RA, Lee TM, Chou KL, Chung LW. The SARS (severe acute respiratory syndrome) pandemic in Hong Kong: effects on the subjective wellbeing of elderly and younger people. Aging Ment Health. 2008;12(6):746–60.

Sun Y, Yan T. The use of public health indicators to assess individual happiness in post-disaster recovery. Int J Environ Res Public Health. 2019;16(21):4101.

Wong H, Huang Y, Fu Y, Zhang Y. Impacts of structural social capital and cognitive social capital on the psychological status of survivors of the yaan Earthquake. Appl Res Qual Life. 2018;14:1411–33.

Xiao H, Zhang Y, Kong D, Li S, Yang N. Social capital and sleep quality in individuals who self-isolated for 14 days during the coronavirus disease 2019 (COVID-19) outbreak in January 2020 in China. Med Sci Monit. 2020;26:e923921.

PubMed   PubMed Central   CAS   Google Scholar  

Matsuyama Y, Aida J, Hase A, Sato Y, Koyama S, Tsuboya T, et al. Do community- and individual-level social relationships contribute to the mental health of disaster survivors? A multilevel prospective study after the great East Japan earthquake. Soc Sci Med. 2016;151:187–95.

Ozaki A, Horiuchi S, Kobayashi Y, Inoue M, Aida J, Leppold C, Yamaoka K. Beneficial roles of social support for mental health vary in the Japanese population depending on disaster experience: a nationwide cross-sectional study. Tohoku J Exp Med. 2018;246(4):213–23.

Sato K, Amemiya A, Haseda M, Takagi D, Kanamori M, Kondo K, et al. Post-disaster changes in Social Capital and Mental Health: a natural experiment from the 2016 Kumamoto Earthquake. Am J Epidemiol. 2020;189(9):910–21.

Tsuchiya N, Nakaya N, Nakamura T, Narita A, Kogure M, Aida J, Tsuji I, Hozawa A, Tomita H. Impact of social capital on psychological distress and interaction with house destruction and displacement after the great East Japan earthquake of 2011. J Neuropsychiatry Clin Neurosci. 2017;71(1):52–60.

Brockie L, Miller E. Understanding older adults’ resilience during the Brisbane floods: social capital, life experience, and optimism. Disaster Med Pub Health Prep. 2017;11(1):72–9.

Caldwell K, Boyd CP. Coping and resilience in farming families affected by drought. Rural Remote Health. 2009;9(2):1088.

PubMed   Google Scholar  

Huang Y, Tan NT, Liu J. Support, sense of community, and psychological status in the survivors of the Yaan earthquake. J Community Psychol. 2016;44(7):919–36.

Wind T, Fordham M, Komproe H. Social capital and post-disaster mental health. Glob Health Action. 2011;4(1):6351.

Wind T, Komproe IH. The mechanisms that associate community social capital with post-disaster mental health: a multilevel model. Soc Sci Med. 2012;75(9):1715–20.

Hogg D, Kingham S, Wilson TM, Ardagh M. The effects of spatially varying earthquake impacts on mood and anxiety symptom treatments among long-term Christchurch residents following the 2010/11 Canterbury Earthquakes, New Zealand. Health Place. 2016;41:78–88.

Flores EC, Carnero AM, Bayer AM. Social capital and chronic post-traumatic stress disorder among survivors of the 2007 earthquake in Pisco, Peru. Soc Sci Med. 2014;101:9–17.

Rafiey H, Alipour F, LeBeau R, Salimi Y, Ahmadi S. Exploring the buffering role of social capital in the development of posttraumatic stress symptoms among Iranian earthquake survivors. Psychol Trauma. 2019;14(6):1040–6.

Babcicky P, Seebauer S. The two faces of social capital in private Flood mitigation: opposing effects on risk perception, self-efficacy and coping capacity. J Risk Res. 2017;20(8):1017–37.

Bakic H, Ajdukovic D. Stability and change post-disaster: dynamic relations between individual, interpersonal and community resources and psychosocial functioning. Eur J Psychotraumatol. 2019;10(1):1614821.

Rogers RW. A protection motivation theory of fear appeals and attitude change. J Psychol. 1975;91(1):93–114.

Lindberg K, Swearingen T. A reflective thrive-oriented community resilience scale. Am J Community Psychol. 2020;65(3–4):467–78.

Leykin D, Lahad M, Cohen O, Goldberg A, Aharonson-Daniel L. Conjoint community resiliency assessment measure-28/10 items (CCRAM28 and CCRAM10): a self-report tool for assessing community resilience. Am J Community Psychol. 2013;52:313–23.

Sherrieb K, Norris FH, Galea S. Measuring capacities for community resilience. Soc Indic Res. 2010;99:227–47.

Ehsan AM, De Silva MJ. Social capital and common mental disorder: a systematic review. J Epidemiol Community Health. 2015;69(10):1021–8.

Pfefferbaum B, Van Horn RL, Pfefferbaum RL. A conceptual framework to enhance community resilience using social capital. Clin Soc Work J. 2017;45(2):102–10.

Carmen E, Fazey I, Ross H, Bedinger M, Smith FM, Prager K, et al. Building community resilience in a context of climate change: the role of social capital. Ambio. 2022;51(6):1371–87.

Humbert C, Joseph J. Introduction: the politics of resilience: problematising current approaches. Resilience. 2019;7(3):215–23.

Tanner T, Bahadur A, Moench M. Challenges for resilience policy and practice. Working paper: 519. 2017.

Vadivel R, Shoib S, El Halabi S, El Hayek S, Essam L, Bytyçi DG. Mental health in the post-COVID-19 era: challenges and the way forward. Gen Psychiatry. 2021;34(1):e100424.

Article   CAS   Google Scholar  

Pryor M. Social Capital Harmonised Standard. London: Government Statistical Service. 2021. Accessible at: https://gss.civilservice.gov.uk/policystore/social-capital/ .

Public Health England NE. A guide to community-centred approaches for health and wellbeing. 2015.

Hawe P. Capturing the meaning of ‘community’ in community intervention evaluation: some contributions from community psychology. Health Promot Int. 1994;9(3):199–210.

Uphoff EP, Pickett KE, Cabieses B, Small N, Wright J. A systematic review of the relationships between social capital and socioeconomic inequalities in health: a contribution to understanding the psychosocial pathway of health inequalities. Int J Equity Health. 2013;12:1–12.

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This study was supported by the National Institute for Health Research Research Unit (NIHR HPRU) in Emergency Preparedness and Response, a partnership between Public Health England, King’s College London and the University of East Anglia. The views expressed are those of the author(s) and not necessarily those of the NIHR, Public Health England, the UK Health Security Agency or the Department of Health and Social Care [Grant number: NIHR20008900]. Part of this work has been funded by the Office for Health Improvement and Disparities, Department of Health and Social Care, as part of a Collaborative Agreement with Leeds Beckett University.

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Hall, C.E., Wehling, H., Stansfield, J. et al. Examining the role of community resilience and social capital on mental health in public health emergency and disaster response: a scoping review. BMC Public Health 23 , 2482 (2023). https://doi.org/10.1186/s12889-023-17242-x

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