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Systematic Review | Definition, Example, & Guide

Systematic Review | Definition, Example & Guide

Published on June 15, 2022 by Shaun Turney . Revised on November 20, 2023.

A systematic review is a type of review that uses repeatable methods to find, select, and synthesize all available evidence. It answers a clearly formulated research question and explicitly states the methods used to arrive at the answer.

They answered the question “What is the effectiveness of probiotics in reducing eczema symptoms and improving quality of life in patients with eczema?”

In this context, a probiotic is a health product that contains live microorganisms and is taken by mouth. Eczema is a common skin condition that causes red, itchy skin.

What is a systematic review, systematic review vs. meta-analysis, systematic review vs. literature review, systematic review vs. scoping review, when to conduct a systematic review, pros and cons of systematic reviews, step-by-step example of a systematic review, other interesting articles, frequently asked questions about systematic reviews.

A review is an overview of the research that’s already been completed on a topic.

What makes a systematic review different from other types of reviews is that the research methods are designed to reduce bias . The methods are repeatable, and the approach is formal and systematic:

Formulate a research question
Develop a protocol
Search for all relevant studies
Apply the selection criteria
Extract the data
Synthesize the data
Write and publish a report

Although multiple sets of guidelines exist, the Cochrane Handbook for Systematic Reviews is among the most widely used. It provides detailed guidelines on how to complete each step of the systematic review process.

Systematic reviews are most commonly used in medical and public health research, but they can also be found in other disciplines.

Systematic reviews typically answer their research question by synthesizing all available evidence and evaluating the quality of the evidence. Synthesizing means bringing together different information to tell a single, cohesive story. The synthesis can be narrative ( qualitative ), quantitative , or both.

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Systematic reviews often quantitatively synthesize the evidence using a meta-analysis . A meta-analysis is a statistical analysis, not a type of review.

A meta-analysis is a technique to synthesize results from multiple studies. It’s a statistical analysis that combines the results of two or more studies, usually to estimate an effect size .

A literature review is a type of review that uses a less systematic and formal approach than a systematic review. Typically, an expert in a topic will qualitatively summarize and evaluate previous work, without using a formal, explicit method.

Although literature reviews are often less time-consuming and can be insightful or helpful, they have a higher risk of bias and are less transparent than systematic reviews.

Similar to a systematic review, a scoping review is a type of review that tries to minimize bias by using transparent and repeatable methods.

However, a scoping review isn’t a type of systematic review. The most important difference is the goal: rather than answering a specific question, a scoping review explores a topic. The researcher tries to identify the main concepts, theories, and evidence, as well as gaps in the current research.

Sometimes scoping reviews are an exploratory preparation step for a systematic review, and sometimes they are a standalone project.

A systematic review is a good choice of review if you want to answer a question about the effectiveness of an intervention , such as a medical treatment.

To conduct a systematic review, you’ll need the following:

A precise question , usually about the effectiveness of an intervention. The question needs to be about a topic that’s previously been studied by multiple researchers. If there’s no previous research, there’s nothing to review.
If you’re doing a systematic review on your own (e.g., for a research paper or thesis ), you should take appropriate measures to ensure the validity and reliability of your research.
Access to databases and journal archives. Often, your educational institution provides you with access.
Time. A professional systematic review is a time-consuming process: it will take the lead author about six months of full-time work. If you’re a student, you should narrow the scope of your systematic review and stick to a tight schedule.
Bibliographic, word-processing, spreadsheet, and statistical software . For example, you could use EndNote, Microsoft Word, Excel, and SPSS.

A systematic review has many pros .

They minimize research bias by considering all available evidence and evaluating each study for bias.
Their methods are transparent , so they can be scrutinized by others.
They’re thorough : they summarize all available evidence.
They can be replicated and updated by others.

Systematic reviews also have a few cons .

They’re time-consuming .
They’re narrow in scope : they only answer the precise research question.

The 7 steps for conducting a systematic review are explained with an example.

Step 1: Formulate a research question

Formulating the research question is probably the most important step of a systematic review. A clear research question will:

Allow you to more effectively communicate your research to other researchers and practitioners
Guide your decisions as you plan and conduct your systematic review

A good research question for a systematic review has four components, which you can remember with the acronym PICO :

Population(s) or problem(s)
Intervention(s)
Comparison(s)

You can rearrange these four components to write your research question:

What is the effectiveness of I versus C for O in P ?

Sometimes, you may want to include a fifth component, the type of study design . In this case, the acronym is PICOT .

Type of study design(s)
The population of patients with eczema
The intervention of probiotics
In comparison to no treatment, placebo , or non-probiotic treatment
The outcome of changes in participant-, parent-, and doctor-rated symptoms of eczema and quality of life
Randomized control trials, a type of study design

Their research question was:

What is the effectiveness of probiotics versus no treatment, a placebo, or a non-probiotic treatment for reducing eczema symptoms and improving quality of life in patients with eczema?

Step 2: Develop a protocol

A protocol is a document that contains your research plan for the systematic review. This is an important step because having a plan allows you to work more efficiently and reduces bias.

Your protocol should include the following components:

Background information : Provide the context of the research question, including why it’s important.
Research objective (s) : Rephrase your research question as an objective.
Selection criteria: State how you’ll decide which studies to include or exclude from your review.
Search strategy: Discuss your plan for finding studies.
Analysis: Explain what information you’ll collect from the studies and how you’ll synthesize the data.

If you’re a professional seeking to publish your review, it’s a good idea to bring together an advisory committee . This is a group of about six people who have experience in the topic you’re researching. They can help you make decisions about your protocol.

It’s highly recommended to register your protocol. Registering your protocol means submitting it to a database such as PROSPERO or ClinicalTrials.gov .

Step 3: Search for all relevant studies

Searching for relevant studies is the most time-consuming step of a systematic review.

To reduce bias, it’s important to search for relevant studies very thoroughly. Your strategy will depend on your field and your research question, but sources generally fall into these four categories:

Databases: Search multiple databases of peer-reviewed literature, such as PubMed or Scopus . Think carefully about how to phrase your search terms and include multiple synonyms of each word. Use Boolean operators if relevant.
Handsearching: In addition to searching the primary sources using databases, you’ll also need to search manually. One strategy is to scan relevant journals or conference proceedings. Another strategy is to scan the reference lists of relevant studies.
Gray literature: Gray literature includes documents produced by governments, universities, and other institutions that aren’t published by traditional publishers. Graduate student theses are an important type of gray literature, which you can search using the Networked Digital Library of Theses and Dissertations (NDLTD) . In medicine, clinical trial registries are another important type of gray literature.
Experts: Contact experts in the field to ask if they have unpublished studies that should be included in your review.

At this stage of your review, you won’t read the articles yet. Simply save any potentially relevant citations using bibliographic software, such as Scribbr’s APA or MLA Generator .

Databases: EMBASE, PsycINFO, AMED, LILACS, and ISI Web of Science
Handsearch: Conference proceedings and reference lists of articles
Gray literature: The Cochrane Library, the metaRegister of Controlled Trials, and the Ongoing Skin Trials Register
Experts: Authors of unpublished registered trials, pharmaceutical companies, and manufacturers of probiotics

Step 4: Apply the selection criteria

Applying the selection criteria is a three-person job. Two of you will independently read the studies and decide which to include in your review based on the selection criteria you established in your protocol . The third person’s job is to break any ties.

To increase inter-rater reliability , ensure that everyone thoroughly understands the selection criteria before you begin.

If you’re writing a systematic review as a student for an assignment, you might not have a team. In this case, you’ll have to apply the selection criteria on your own; you can mention this as a limitation in your paper’s discussion.

You should apply the selection criteria in two phases:

Based on the titles and abstracts : Decide whether each article potentially meets the selection criteria based on the information provided in the abstracts.
Based on the full texts: Download the articles that weren’t excluded during the first phase. If an article isn’t available online or through your library, you may need to contact the authors to ask for a copy. Read the articles and decide which articles meet the selection criteria.

It’s very important to keep a meticulous record of why you included or excluded each article. When the selection process is complete, you can summarize what you did using a PRISMA flow diagram .

Next, Boyle and colleagues found the full texts for each of the remaining studies. Boyle and Tang read through the articles to decide if any more studies needed to be excluded based on the selection criteria.

When Boyle and Tang disagreed about whether a study should be excluded, they discussed it with Varigos until the three researchers came to an agreement.

Step 5: Extract the data

Extracting the data means collecting information from the selected studies in a systematic way. There are two types of information you need to collect from each study:

Information about the study’s methods and results . The exact information will depend on your research question, but it might include the year, study design , sample size, context, research findings , and conclusions. If any data are missing, you’ll need to contact the study’s authors.
Your judgment of the quality of the evidence, including risk of bias .

You should collect this information using forms. You can find sample forms in The Registry of Methods and Tools for Evidence-Informed Decision Making and the Grading of Recommendations, Assessment, Development and Evaluations Working Group .

Extracting the data is also a three-person job. Two people should do this step independently, and the third person will resolve any disagreements.

They also collected data about possible sources of bias, such as how the study participants were randomized into the control and treatment groups.

Step 6: Synthesize the data

Synthesizing the data means bringing together the information you collected into a single, cohesive story. There are two main approaches to synthesizing the data:

Narrative ( qualitative ): Summarize the information in words. You’ll need to discuss the studies and assess their overall quality.
Quantitative : Use statistical methods to summarize and compare data from different studies. The most common quantitative approach is a meta-analysis , which allows you to combine results from multiple studies into a summary result.

Generally, you should use both approaches together whenever possible. If you don’t have enough data, or the data from different studies aren’t comparable, then you can take just a narrative approach. However, you should justify why a quantitative approach wasn’t possible.

Boyle and colleagues also divided the studies into subgroups, such as studies about babies, children, and adults, and analyzed the effect sizes within each group.

Step 7: Write and publish a report

The purpose of writing a systematic review article is to share the answer to your research question and explain how you arrived at this answer.

Your article should include the following sections:

Abstract : A summary of the review
Introduction : Including the rationale and objectives
Methods : Including the selection criteria, search method, data extraction method, and synthesis method
Results : Including results of the search and selection process, study characteristics, risk of bias in the studies, and synthesis results
Discussion : Including interpretation of the results and limitations of the review
Conclusion : The answer to your research question and implications for practice, policy, or research

To verify that your report includes everything it needs, you can use the PRISMA checklist .

Once your report is written, you can publish it in a systematic review database, such as the Cochrane Database of Systematic Reviews , and/or in a peer-reviewed journal.

In their report, Boyle and colleagues concluded that probiotics cannot be recommended for reducing eczema symptoms or improving quality of life in patients with eczema. Note Generative AI tools like ChatGPT can be useful at various stages of the writing and research process and can help you to write your systematic review. However, we strongly advise against trying to pass AI-generated text off as your own work.

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

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

Research bias

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

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.

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other academic texts , with an introduction , a main body, and a conclusion .

An annotated bibliography is a list of source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a paper .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

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How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-Analyses, and Meta-Syntheses

Affiliations.

1 Behavioural Science Centre, Stirling Management School, University of Stirling, Stirling FK9 4LA, United Kingdom; email: [email protected].
2 Department of Psychological and Behavioural Science, London School of Economics and Political Science, London WC2A 2AE, United Kingdom.
3 Department of Statistics, Northwestern University, Evanston, Illinois 60208, USA; email: [email protected].
PMID: 30089228
DOI: 10.1146/annurev-psych-010418-102803

Systematic reviews are characterized by a methodical and replicable methodology and presentation. They involve a comprehensive search to locate all relevant published and unpublished work on a subject; a systematic integration of search results; and a critique of the extent, nature, and quality of evidence in relation to a particular research question. The best reviews synthesize studies to draw broad theoretical conclusions about what a literature means, linking theory to evidence and evidence to theory. This guide describes how to plan, conduct, organize, and present a systematic review of quantitative (meta-analysis) or qualitative (narrative review, meta-synthesis) information. We outline core standards and principles and describe commonly encountered problems. Although this guide targets psychological scientists, its high level of abstraction makes it potentially relevant to any subject area or discipline. We argue that systematic reviews are a key methodology for clarifying whether and how research findings replicate and for explaining possible inconsistencies, and we call for researchers to conduct systematic reviews to help elucidate whether there is a replication crisis.

Keywords: evidence; guide; meta-analysis; meta-synthesis; narrative; systematic review; theory.

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A-Z Publications

Annual Review of Psychology

Volume 70, 2019, review article, how to do a systematic review: a best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses.

Andy P. Siddaway 1 , Alex M. Wood 2 , and Larry V. Hedges 3
View Affiliations Hide Affiliations Affiliations: 1 Behavioural Science Centre, Stirling Management School, University of Stirling, Stirling FK9 4LA, United Kingdom; email: [email protected] 2 Department of Psychological and Behavioural Science, London School of Economics and Political Science, London WC2A 2AE, United Kingdom 3 Department of Statistics, Northwestern University, Evanston, Illinois 60208, USA; email: [email protected]
Vol. 70:747-770 (Volume publication date January 2019) https://doi.org/10.1146/annurev-psych-010418-102803
First published as a Review in Advance on August 08, 2018
Copyright © 2019 by Annual Reviews. All rights reserved

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Article Type: Review Article

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How to write the methods section of a systematic review

Home | Blog | How To | How to write the methods section of a systematic review

Covidence breaks down how to write a methods section

The methods section of your systematic review describes what you did, how you did it, and why. Readers need this information to interpret the results and conclusions of the review. Often, a lot of information needs to be distilled into just a few paragraphs. This can be a challenging task, but good preparation and the right tools will help you to set off in the right direction 🗺️🧭.

Systematic reviews are so-called because they are conducted in a way that is rigorous and replicable. So it’s important that these methods are reported in a way that is thorough, clear, and easy to navigate for the reader – whether that’s a patient, a healthcare worker, or a researcher.

Like most things in a systematic review, the methods should be planned upfront and ideally described in detail in a project plan or protocol. Reviews of healthcare interventions follow the PRISMA guidelines for the minimum set of items to report in the methods section. But what else should be included? It’s a good idea to consider what readers will want to know about the review methods and whether the journal you’re planning to submit the work to has expectations on the reporting of methods. Finding out in advance will help you to plan what to include.

Describe what happened

While the research plan sets out what you intend to do, the methods section is a write-up of what actually happened. It’s not a simple case of rewriting the plan in the past tense – you will also need to discuss and justify deviations from the plan and describe the handling of issues that were unforeseen at the time the plan was written. For this reason, it is useful to make detailed notes before, during, and after the review is completed. Relying on memory alone risks losing valuable information and trawling through emails when the deadline is looming can be frustrating and time consuming!

Keep it brief

The methods section should be succinct but include all the noteworthy information. This can be a difficult balance to achieve. A useful strategy is to aim for a brief description that signposts the reader to a separate section or sections of supporting information. This could include datasets, a flowchart to show what happened to the excluded studies, a collection of search strategies, and tables containing detailed information about the studies.This separation keeps the review short and simple while enabling the reader to drill down to the detail as needed. And if the methods follow a well-known or standard process, it might suffice to say so and give a reference, rather than describe the process at length.

Follow a structure

A clear structure provides focus. Use of descriptive headings keeps the writing on track and helps the reader get to key information quickly. What should the structure of the methods section look like? As always, a lot depends on the type of review but it will certainly contain information relating to the following areas:

Selection criteria ⭕
Data collection and analysis 👩‍💻
Study quality and risk of bias ⚖️

Let’s look at each of these in turn.

1. Selection criteria ⭕

The criteria for including and excluding studies are listed here. This includes detail about the types of studies, the types of participants, the types of interventions and the types of outcomes and how they were measured.

2. Search 🕵🏾‍♀️

Comprehensive reporting of the search is important because this means it can be evaluated and replicated. The search strategies are included in the review, along with details of the databases searched. It’s also important to list any restrictions on the search (for example, language), describe how resources other than electronic databases were searched (for example, non-indexed journals), and give the date that the searches were run. The PRISMA-S extension provides guidance on reporting literature searches.

Systematic reviewer pro-tip:

Copy and paste the search strategy to avoid introducing typos

3. Data collection and analysis 👩‍💻

This section describes:

how studies were selected for inclusion in the review
how study data were extracted from the study reports
how study data were combined for analysis and synthesis

To describe how studies were selected for inclusion , review teams outline the screening process. Covidence uses reviewers’ decision data to automatically populate a PRISMA flow diagram for this purpose. Covidence can also calculate Cohen’s kappa to enable review teams to report the level of agreement among individual reviewers during screening.

To describe how study data were extracted from the study reports , reviewers outline the form that was used, any pilot-testing that was done, and the items that were extracted from the included studies. An important piece of information to include here is the process used to resolve conflict among the reviewers. Covidence’s data extraction tool saves reviewers’ comments and notes in the system as they work. This keeps the information in one place for easy retrieval ⚡.

To describe how study data were combined for analysis and synthesis, reviewers outline the type of synthesis (narrative or quantitative, for example), the methods for grouping data, the challenges that came up, and how these were dealt with. If the review includes a meta-analysis, it will detail how this was performed and how the treatment effects were measured.

4. Study quality and risk of bias ⚖️

Because the results of systematic reviews can be affected by many types of bias, reviewers make every effort to minimise it and to show the reader that the methods they used were appropriate. This section describes the methods used to assess study quality and an assessment of the risk of bias across a range of domains.

Steps to assess the risk of bias in studies include looking at how study participants were assigned to treatment groups and whether patients and/or study assessors were blinded to the treatment given. Reviewers also report their assessment of the risk of bias due to missing outcome data, whether that is due to participant drop-out or non-reporting of the outcomes by the study authors.

Covidence’s default template for assessing study quality is Cochrane’s risk of bias tool but it is also possible to start from scratch and build a tool with a set of custom domains if you prefer.

Careful planning, clear writing, and a structured approach are key to a good methods section. A methodologist will be able to refer review teams to examples of good methods reporting in the literature. Covidence helps reviewers to screen references, extract data and complete risk of bias tables quickly and efficiently. Sign up for a free trial today!

Laura Mellor. Portsmouth, UK

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What is a Systematic Literature Review?

A systematic literature review (SLR) is an independent academic method that aims to identify and evaluate all relevant literature on a topic in order to derive conclusions about the question under consideration. "Systematic reviews are undertaken to clarify the state of existing research and the implications that should be drawn from this." (Feak & Swales, 2009, p. 3) An SLR can demonstrate the current state of research on a topic, while identifying gaps and areas requiring further research with regard to a given research question. A formal methodological approach is pursued in order to reduce distortions caused by an overly restrictive selection of the available literature and to increase the reliability of the literature selected (Tranfield, Denyer & Smart, 2003). A special aspect in this regard is the fact that a research objective is defined for the search itself and the criteria for determining what is to be included and excluded are defined prior to conducting the search. The search is mainly performed in electronic literature databases (such as Business Source Complete or Web of Science), but also includes manual searches (reviews of reference lists in relevant sources) and the identification of literature not yet published in order to obtain a comprehensive overview of a research topic.

An SLR protocol documents all the information gathered and the steps taken as part of an SLR in order to make the selection process transparent and reproducible. The PRISMA flow-diagram support you in making the selection process visible.

In an ideal scenario, experts from the respective research discipline, as well as experts working in the relevant field and in libraries, should be involved in setting the search terms . As a rule, the literature is selected by two or more reviewers working independently of one another. Both measures serve the purpose of increasing the objectivity of the literature selection. An SLR must, then, be more than merely a summary of a topic (Briner & Denyer, 2012). As such, it also distinguishes itself from “ordinary” surveys of the available literature. The following table shows the differences between an SLR and an “ordinary” literature review.

Charts of BSWL workshop (pdf, 2.88 MB)
Listen to the interview (mp4, 12.35 MB)

Differences to "common" literature reviews

Characteristic	SLR	common literature overview
Independent research method	yes	no
Explicit formulation of the search objectives	yes	no
Identification of all publications on a topic	yes	no
Defined criteria for inclusion and exclusion of publications	yes	no
Description of search procedure	yes	no
Literature selection and information extraction by several persons	yes	no
Transparent quality evaluation of publications	yes	no

What are the objectives of SLRs?

Avoidance of research redundancies despite a growing amount of publications
Identification of research areas, gaps and methods
Input for evidence-based management, which allows to base management decisions on scientific methods and findings
Identification of links between different areas of researc

Process steps of an SLR

A SLR has several process steps which are defined differently in the literature (Fink 2014, p. 4; Guba 2008, Transfield et al. 2003). We distinguish the following steps which are adapted to the economics and management research area:

1. Defining research questions

Briner & Denyer (2009, p. 347ff.) have developed the CIMO scheme to establish clearly formulated and answerable research questions in the field of economic sciences:

C – CONTEXT: Which individuals, relationships, institutional frameworks and systems are being investigated?

I – Intervention: The effects of which event, action or activity are being investigated?

M – Mechanisms: Which mechanisms can explain the relationship between interventions and results? Under what conditions do these mechanisms take effect?

O – Outcomes: What are the effects of the intervention? How are the results measured? What are intended and unintended effects?

The objective of the systematic literature review is used to formulate research questions such as “How can a project team be led effectively?”. Since there are numerous interpretations and constructs for “effective”, “leadership” and “project team”, these terms must be particularized.

With the aid of the scheme, the following concrete research questions can be derived with regard to this example:

Under what conditions (C) does leadership style (I) influence the performance of project teams (O)?

Which constructs have an effect upon the influence of leadership style (I) on a project team’s performance (O)?

Research questions do not necessarily need to follow the CIMO scheme, but they should:

... be formulated in a clear, focused and comprehensible manner and be answerable;
... have been determined prior to carrying out the SLR;
... consist of general and specific questions.

As early as this stage, the criteria for inclusion and exclusion are also defined. The selection of the criteria must be well-grounded. This may include conceptual factors such as a geographical or temporal restrictions, congruent definitions of constructs, as well as quality criteria (journal impact factor > x).

2. Selecting databases and other research sources

The selection of sources must be described and explained in detail. The aim is to find a balance between the relevance of the sources (content-related fit) and the scope of the sources.

In the field of economic sciences, there are a number of literature databases that can be searched as part of an SLR. Some examples in this regard are:

Business Source Complete
ProQuest One Business
EconBiz

Our video " Selecting the right databases " explains how to find relevant databases for your topic.

Literature databases are an important source of research for SLRs, as they can minimize distortions caused by an individual literature selection (selection bias), while offering advantages for a systematic search due to their data structure. The aim is to find all database entries on a topic and thus keep the retrieval bias low (tutorial on retrieval bias ). Besides articles from scientific journals, it is important to inlcude working papers, conference proceedings, etc to reduce the publication bias ( tutorial on publication bias ).

Our online self-study course " Searching economic databases " explains step 2 und 3.

3. Defining search terms

Once the literature databases and other research sources have been selected, search terms are defined. For this purpose, the research topic/questions is/are divided into blocks of terms of equal ranking. This approach is called the block-building method (Guba 2008, p. 63). The so-called document-term matrix, which lists topic blocks and search terms according to a scheme, is helpful in this regard. The aim is to identify as many different synonyms as possible for the partial terms. A precisely formulated research question facilitates the identification of relevant search terms. In addition, keywords from particularly relevant articles support the formulation of search terms.

A document-term matrix for the topic “The influence of management style on the performance of project teams” is shown in this example .

Identification of headwords and keywords

When setting search terms, a distinction must be made between subject headings and keywords, both of which are described below:

appear in the title, abstract and/or text
sometimes specified by the author, but in most cases automatically generated
non-standardized
different spellings and forms (singular/plural) must be searched separately

Subject headings

describe the content
are generated by an editorial team
are listed in a standardized list (thesaurus)
may comprise various keywords
include different spellings
database-specific

Subject headings are a standardized list of words that are generated by the specialists in charge of some databases. This so-called index of subject headings (thesaurus) helps searchers find relevant articles, since the headwords indicate the content of a publication. By contrast, an ordinary keyword search does not necessarily result in a content-related fit, since the database also displays articles in which, for example, a word appears once in the abstract, even though the article’s content does not cover the topic.

Nevertheless, searches using both headwords and keywords should be conducted, since some articles may not yet have been assigned headwords, or errors may have occurred during the assignment of headwords.

To add headwords to your search in the Business Source Complete database, please select the Thesaurus tab at the top. Here you can find headwords in a new search field and integrate them into your search query. In the search history, headwords are marked with the addition DE (descriptor).

The EconBiz database of the German National Library of Economics (ZBW – Leibniz Information Centre for Economics), which also contains German-language literature, has created its own index of subject headings with the STW Thesaurus for Economics . Headwords are integrated into the search by being used in the search query.

Since the indexes of subject headings divide terms into synonyms, generic terms and sub-aspects, they facilitate the creation of a document-term matrix. For this purpose it is advisable to specify in the document-term matrix the origin of the search terms (STW Thesaurus for Economics, Business Source Complete, etc.).

Searching in literature databases

Once the document-term matrix has been defined, the search in literature databases begins. It is recommended to enter each word of the document-term matrix individually into the database in order to obtain a good overview of the number of hits per word. Finally, all the words contained in a block of terms are linked with the Boolean operator OR and thereby a union of all the words is formed. The latter are then linked with each other using the Boolean operator AND. In doing so, each block should be added individually in order to see to what degree the number of hits decreases.

Since the search query must be set up separately for each database, tools such as LitSonar have been developed to enable a systematic search across different databases. LitSonar was created by Professor Dr. Ali Sunyaev (Institute of Applied Informatics and Formal Description Methods – AIFB) at the Karlsruhe Institute of Technology.

Advanced search

Certain database-specific commands can be used to refine a search, for example, by taking variable word endings into account (*) or specifying the distance between two words, etc. Our overview shows the most important search commands for our top databases.

Additional searches in sources other than literature databases

In addition to literature databases, other sources should also be searched. Fink (2014, p. 27) lists the following reasons for this:

the topic is new and not yet included in indexes of subject headings;
search terms are not used congruently in articles because uniform definitions do not exist;
some studies are still in the process of being published, or have been completed, but not published.

Therefore, further search strategies are manual search, bibliographic analysis, personal contacts and academic networks (Briner & Denyer, p. 349). Manual search means that you go through the source information of relevant articles and supplement your hit list accordingly. In addition, you should conduct a targeted search for so-called gray literature, that is, literature not distributed via the book trade, such as working papers from specialist areas and conference reports. By including different types of publications, the so-called publication bias (DBWM video “Understanding publication bias” ) – that is, distortions due to exclusive use of articles from peer-reviewed journals – should be kept to a minimum.

The PRESS-Checklist can support you to check the correctness of your search terms.

4. Merging hits from different databases

In principle, large amounts of data can be easily collected, structured and sorted with data processing programs such as Excel. Another option is to use reference management programs such as EndNote, Citavi or Zotero. The Saxon State and University Library Dresden (SLUB Dresden) provides an overview of current reference management programs . Software for qualitative data analysis such as NVivo is equally suited for data processing. A comprehensive overview of the features of different tools that support the SLR process can be found in Bandara et al. (2015).

Our online-self study course "Managing literature with Citavi" shows you how to use the reference management software Citavi.

When conducting an SLR, you should specify for each hit the database from which it originates and the date on which the query was made. In addition, you should always indicate how many hits you have identified in the various databases or, for example, by manual search.

Exporting data from literature databases

Exporting from literature databases is very easy. In Business Source Complete , you must first click on the “Share” button in the hit list, then “Email a link to download exported results” at the very bottom and then select the appropriate format for the respective literature program.

Exporting data from the literature database EconBiz is somewhat more complex. Here you must first create a marked list and then select each hit individually and add it to the marked list. Afterwards, articles on the list can be exported.

After merging all hits from the various databases, duplicate entries (duplicates) are deleted.

5. Applying inclusion and exclusion criteria

All publications are evaluated in the literature management program applying the previously defined criteria for inclusion and exclusion. Only those sources that survive this selection process will subsequently be analyzed. The review process and inclusion criteria should be tested with a small sample and adjustments made if necessary before applying it to all articles. In the ideal case, even this selection would be carried out by more than one person, with each working independently of one another. It needs to be made clear how discrepancies between reviewers are dealt with.

The review of the criteria for inclusion and exclusion is primarily based on the title, abstract and subject headings in the databases, as well as on the keywords provided by the authors of a publication in the first step. In a second step the whole article / source will be read.

You can create tag words for the inclusion and exclusion in your literature management tool to keep an overview.

In addition to the common literature management tools, you can also use software tools that have been developed to support SLRs. The central library of the university in Zurich has published an overview and evaluation of different tools based on a survey among researchers. --> View SLR tools

The selection process needs to be made transparent. The PRISMA flow diagram supports the visualization of the number of included / excluded studies.

Forward and backward search

Should it become apparent that the number of sources found is relatively small, or if you wish to proceed with particular thoroughness, a forward-and-backward search based on the sources found is recommendable (Webster & Watson 2002, p. xvi). A backward search means going through the bibliographies of the sources found. A forward search, by contrast, identifies articles that have cited the relevant publications. The Web of Science and Scopus databases can be used to perform citation analyses.

6. Perform the review

As the next step, the remaining titles are analyzed as to their content by reading them several times in full. Information is extracted according to defined criteria and the quality of the publications is evaluated. If the data extraction is carried out by more than one person, a training ensures that there will be no differences between the reviewers.

Depending on the research questions there exist diffent methods for data abstraction (content analysis, concept matrix etc.). A so-called concept matrix can be used to structure the content of information (Webster & Watson 2002, p. xvii). The image to the right gives an example of a concept matrix according to Becker (2014).

Particularly in the field of economic sciences, the evaluation of a study’s quality cannot be performed according to a generally valid scheme, such as those existing in the field of medicine, for instance. Quality assessment therefore depends largely on the research questions.

Based on the findings of individual studies, a meta-level is then applied to try to understand what similarities and differences exist between the publications, what research gaps exist, etc. This may also result in the development of a theoretical model or reference framework.

Example concept matrix (Becker 2013) on the topic Business Process Management

Article	Pattern	Configuration	Similarities
Thom (2008)	x
Yang (2009)	x		x
Rosa (2009)		x	x

7. Synthesizing results

Once the review has been conducted, the results must be compiled and, on the basis of these, conclusions derived with regard to the research question (Fink 2014, p. 199ff.). This includes, for example, the following aspects:

historical development of topics (histogram, time series: when, and how frequently, did publications on the research topic appear?);
overview of journals, authors or specialist disciplines dealing with the topic;
comparison of applied statistical methods;
topics covered by research;
identifying research gaps;
developing a reference framework;
developing constructs;
performing a meta-analysis: comparison of the correlations of the results of different empirical studies (see for example Fink 2014, p. 203 on conducting meta-analyses)

Publications about the method

Bandara, W., Furtmueller, E., Miskon, S., Gorbacheva, E., & Beekhuyzen, J. (2015). Achieving Rigor in Literature Reviews: Insights from Qualitative Data Analysis and Tool-Support. Communications of the Association for Information Systems . 34(8), 154-204.

Booth, A., Papaioannou, D., and Sutton, A. (2012) Systematic approaches to a successful literature review. London: Sage.

Briner, R. B., & Denyer, D. (2012). Systematic Review and Evidence Synthesis as a Practice and Scholarship Tool. In Rousseau, D. M. (Hrsg.), The Oxford Handbook of Evidenence Based Management . (S. 112-129). Oxford: Oxford University Press.

Durach, C. F., Wieland, A., & Machuca, Jose A. D. (2015). Antecedents and dimensions of supply chain robustness: a systematic literature review . International Journal of Physical Distribution & Logistic Management , 46 (1/2), 118-137. doi: https://doi.org/10.1108/IJPDLM-05-2013-0133

Feak, C. B., & Swales, J. M. (2009). Telling a Research Story: Writing a Literature Review. English in Today's Research World 2. Ann Arbor: University of Michigan Press. doi: 10.3998/mpub.309338

Fink, A. (2014). Conducting Research Literature Reviews: From the Internet to Paper (4. Aufl.). Los Angeles, London, New Delhi, Singapore, Washington DC: Sage Publication.

Fisch, C., & Block, J. (2018). Six tips for your (systematic) literature review in business and management research. Management Review Quarterly, 68, 103–106 (2018). doi.org/10.1007/s11301-018-0142-x

Guba, B. (2008). Systematische Literaturrecherche. Wiener Medizinische Wochenschrift , 158 (1-2), S. 62-69. doi: doi.org/10.1007/s10354-007-0500-0 Hart, C. Doing a literature review: releasing the social science research imagination. London: Sage.

Jesson, J. K., Metheson, L. & Lacey, F. (2011). Doing your Literature Review - traditional and Systematic Techniques . Los Angeles, London, New Delhi, Singapore, Washington DC: Sage Publication.

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71.

Petticrew, M. and Roberts, H. (2006). Systematic Reviews in the Social Sciences: A Practical Guide . Oxford:Blackwell. Ridley, D. (2012). The literature review: A step-by-step guide . 2nd edn. London: Sage.

Chang, W. and Taylor, S.A. (2016), The Effectiveness of Customer Participation in New Product Development: A Meta-Analysis, Journal of Marketing , American Marketing Association, Los Angeles, CA, Vol. 80 No. 1, pp. 47–64.

Tranfield, D., Denyer, D. & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management , 14 (3), S. 207-222. doi: https://doi.org/10.1111/1467-8551.00375

Webster, J., & Watson, R. T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. Management Information Systems Quarterly , 26(2), xiii-xxiii. http://www.jstor.org/stable/4132319

Durach, C. F., Wieland, A. & Machuca, Jose. A. D. (2015). Antecedents and dimensions of supply chain robustness: a systematic literature review. International Journal of Physical Distribution & Logistics Management, 45(1/2), 118 – 137.

What is particularly good about this example is that search terms were defined by a number of experts and the review was conducted by three researchers working independently of one another. Furthermore, the search terms used have been very well extracted and the procedure of the literature selection very well described.

On the downside, the restriction to English-language literature brings the language bias into play, even though the authors consider it to be insignificant for the subject area.

Bos-Nehles, A., Renkema, M. & Janssen, M. (2017). HRM and innovative work behaviour: a systematic literature review. Personnel Review, 46(7), pp. 1228-1253

Only very specific keywords used
No precise information on how the review process was carried out (who reviewed articles?)
Only journals with impact factor (publication bias)

Jia, F., Orzes, G., Sartor, M. & Nassimbeni, G. (2017). Global sourcing strategy and structure: towards a conceptual framework. International Journal of Operations & Production Management, 37(7), 840-864

Research questions are explicitly presented
Search string very detailed
Exact description of the review process
2 persons conducted the review independently of each other

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The ABC of systematic literature review: the basic methodological guidance for beginners

Published: 23 October 2020
Volume 55 , pages 1319–1346, ( 2021 )

Cite this article

Hayrol Azril Mohamed Shaffril 1 ,
Samsul Farid Samsuddin 2 &
Asnarulkhadi Abu Samah 1 , 3

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There is a need for more methodological-based articles on systematic literature review (SLR) for non-health researchers to address issues related to the lack of methodological references in SLR and less suitability of existing methodological guidance. With that, this study presented a beginner's guide to basic methodological guides and key points to perform SLR, especially for those from non-health related background. For that, a total of 75 articles that passed the minimum quality were retrieved using systematic searching strategies. Seven main points of SLR were discussed, namely (1) the development and validation of the review protocol/publication standard/reporting standard/guidelines, (2) the formulation of research questions, (3) systematic searching strategies, (4) quality appraisal, (5) data extraction, (6) data synthesis, and (7) data demonstration.

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Reading and interpreting reviews for health professionals: a practical review

Definition of a systematic review used in overviews of systematic reviews, meta-epidemiological studies and textbooks

What do meta-analysts need in primary studies guidelines and the semi checklist for facilitating cumulative knowledge, explore related subjects.

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Mohamed Shaffril, H.A., Samsuddin, S.F. & Abu Samah, A. The ABC of systematic literature review: the basic methodological guidance for beginners. Qual Quant 55 , 1319–1346 (2021). https://doi.org/10.1007/s11135-020-01059-6

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Systematic literature review
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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


	Aims to demonstrate writer has extensively researched literature and critically evaluated its quality. Goes beyond mere description to include degree of analysis and conceptual innovation. Typically results in hypothesis or mode	Seeks to identify most significant items in the field	No formal quality assessment. Attempts to evaluate according to contribution	Typically narrative, perhaps conceptual or chronological	Significant component: seeks to identify conceptual contribution to embody existing or derive new theory
	Generic term: published materials that provide examination of recent or current literature. Can cover wide range of subjects at various levels of completeness and comprehensiveness. May include research findings	May or may not include comprehensive searching	May or may not include quality assessment	Typically narrative	Analysis may be chronological, conceptual, thematic, etc.
Mapping review/ systematic map	Map out and categorize existing literature from which to commission further reviews and/or primary research by identifying gaps in research literature	Completeness of searching determined by time/scope constraints	No formal quality assessment	May be graphical and tabular	Characterizes quantity and quality of literature, perhaps by study design and other key features. May identify need for primary or secondary research
	Technique that statistically combines the results of quantitative studies to provide a more precise effect of the results	Aims for exhaustive, comprehensive searching. May use funnel plot to assess completeness	Quality assessment may determine inclusion/ exclusion and/or sensitivity analyses	Graphical and tabular with narrative commentary	Numerical analysis of measures of effect assuming absence of heterogeneity
	Refers to any combination of methods where one significant component is a literature review (usually systematic). Within a review context it refers to a combination of review approaches for example combining quantitative with qualitative research or outcome with process studies	Requires either very sensitive search to retrieve all studies or separately conceived quantitative and qualitative strategies	Requires either a generic appraisal instrument or separate appraisal processes with corresponding checklists	Typically both components will be presented as narrative and in tables. May also employ graphical means of integrating quantitative and qualitative studies	Analysis may characterise both literatures and look for correlations between characteristics or use gap analysis to identify aspects absent in one literature but missing in the other
	Generic term: summary of the [medical] literature that attempts to survey the literature and describe its characteristics	May or may not include comprehensive searching (depends whether systematic overview or not)	May or may not include quality assessment (depends whether systematic overview or not)	Synthesis depends on whether systematic or not. Typically narrative but may include tabular features	Analysis may be chronological, conceptual, thematic, etc.
	Method for integrating or comparing the findings from qualitative studies. It looks for ‘themes’ or ‘constructs’ that lie in or across individual qualitative studies	May employ selective or purposive sampling	Quality assessment typically used to mediate messages not for inclusion/exclusion	Qualitative, narrative synthesis	Thematic analysis, may include conceptual models
	Assessment of what is already known about a policy or practice issue, by using systematic review methods to search and critically appraise existing research	Completeness of searching determined by time constraints	Time-limited formal quality assessment	Typically narrative and tabular	Quantities of literature and overall quality/direction of effect of literature
	Preliminary assessment of potential size and scope of available research literature. Aims to identify nature and extent of research evidence (usually including ongoing research)	Completeness of searching determined by time/scope constraints. May include research in progress	No formal quality assessment	Typically tabular with some narrative commentary	Characterizes quantity and quality of literature, perhaps by study design and other key features. Attempts to specify a viable review
	Tend to address more current matters in contrast to other combined retrospective and current approaches. May offer new perspectives	Aims for comprehensive searching of current literature	No formal quality assessment	Typically narrative, may have tabular accompaniment	Current state of knowledge and priorities for future investigation and research
	Seeks to systematically search for, appraise and synthesis research evidence, often adhering to guidelines on the conduct of a review	Aims for exhaustive, comprehensive searching	Quality assessment may determine inclusion/exclusion	Typically narrative with tabular accompaniment	What is known; recommendations for practice. What remains unknown; uncertainty around findings, recommendations for future research
	Combines strengths of critical review with a comprehensive search process. Typically addresses broad questions to produce ‘best evidence synthesis’	Aims for exhaustive, comprehensive searching	May or may not include quality assessment	Minimal narrative, tabular summary of studies	What is known; recommendations for practice. Limitations
	Attempt to include elements of systematic review process while stopping short of systematic review. Typically conducted as postgraduate student assignment	May or may not include comprehensive searching	May or may not include quality assessment	Typically narrative with tabular accompaniment	What is known; uncertainty around findings; limitations of methodology
	Specifically refers to review compiling evidence from multiple reviews into one accessible and usable document. Focuses on broad condition or problem for which there are competing interventions and highlights reviews that address these interventions and their results	Identification of component reviews, but no search for primary studies	Quality assessment of studies within component reviews and/or of reviews themselves	Graphical and tabular with narrative commentary	What is known; recommendations for practice. What remains unknown; recommendations for future research

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http://orcid.org/0000-0001-6644-9845 Irma Klerings 1 ,
Shannon Robalino 2 ,
http://orcid.org/0000-0003-4808-3880 Andrew Booth 3 ,
http://orcid.org/0000-0002-2903-6870 Camila Micaela Escobar-Liquitay 4 ,
Isolde Sommer 1 ,
http://orcid.org/0000-0001-5531-3678 Gerald Gartlehner 1 , 5 ,
Declan Devane 6 , 7 ,
Siw Waffenschmidt 8
On behalf of the Cochrane Rapid Reviews Methods Group
1 Department for Evidence-Based Medicine and Evaluation , University of Krems (Danube University Krems) , Krems , Niederösterreich , Austria
2 Center for Evidence-based Policy , Oregon Health & Science University , Portland , Oregon , USA
3 School of Health and Related Research (ScHARR) , The University of Sheffield , Sheffield , UK
4 Research Department, Associate Cochrane Centre , Instituto Universitario Escuela de Medicina del Hospital Italiano de Buenos Aires , Buenos Aires , Argentina
5 RTI-UNC Evidence-based Practice Center , RTI International , Research Triangle Park , North Carolina , USA
6 School of Nursing & Midwifery, HRB TMRN , National University of Ireland Galway , Galway , Ireland
7 Evidence Synthesis Ireland & Cochrane Ireland , University of Galway , Galway , Ireland
8 Information Management Department , Institute for Quality and Efficiency in Healthcare , Cologne , Germany
Correspondence to Irma Klerings, Department for Evidence-based Medicine and Evaluation, Danube University Krems, Krems, Niederösterreich, Austria; irma.klerings{at}donau-uni.ac.at

This paper is part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group. Rapid reviews (RR) use modified systematic review methods to accelerate the review process while maintaining systematic, transparent and reproducible methods. In this paper, we address considerations for RR searches. We cover the main areas relevant to the search process: preparation and planning, information sources and search methods, search strategy development, quality assurance, reporting, and record management. Two options exist for abbreviating the search process: (1) reducing time spent on conducting searches and (2) reducing the size of the search result. Because screening search results is usually more resource-intensive than conducting the search, we suggest investing time upfront in planning and optimising the search to save time by reducing the literature screening workload. To achieve this goal, RR teams should work with an information specialist. They should select a small number of relevant information sources (eg, databases) and use search methods that are highly likely to identify relevant literature for their topic. Database search strategies should aim to optimise both precision and sensitivity, and quality assurance measures (peer review and validation of search strategies) should be applied to minimise errors.

Evidence-Based Practice
Systematic Reviews as Topic
Information Science

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https://doi.org/10.1136/bmjebm-2022-112079

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Compared with systematic reviews, rapid reviews (RR) often abbreviate or limit the literature search in some way to accelerate review production. However, RR guidance rarely specifies how to select topic-appropriate search approaches.

WHAT THIS STUDY ADDS

This paper presents an overview of considerations and recommendations for RR searching, covering the complete search process from the planning stage to record management. We also provide extensive appendices with practical examples, useful sources and a glossary of terms.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

There is no one-size-fits-all solution for RR literature searching: review teams should consider what search approaches best fit their RR project.

Introduction

This paper is part of a series from the Cochrane Rapid Reviews Methods Group (RRMG) providing methodological guidance for rapid reviews (RRs). 1–3 While the RRMG’s guidance 4 5 on Cochrane RR production includes brief advice on literature searching, we aim to provide in-depth recommendations for the entire search process.

Literature searching is the foundation for all reviews; therefore, it is important to understand the goals of a specific RR. The scope of RRs varies considerably (from focused questions to overviews of broad topics). 6 As with conventional systematic reviews (SRs), there is not a one-size-fits-all approach for RR literature searches. We aim to support RR teams in choosing methods that best fit their project while understanding the limitations of modified search methods. Our recommendations derive from current systematic search guidance, evidence on modified search methods and practical experience conducting RRs.

This paper presents considerations and recommendations, described briefly in table 1 . The table also includes a comparison to the SR search process based on common recommendations. 7–10 We provide supplemental materials, including a list of additional resources, further details of our recommendations, practical examples, and a glossary (explaining the terms written in italics) in online supplemental appendices A–C .

Supplemental material

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Recommendations for rapid review literature searching

Preparation and planning

Given that the results of systematic literature searches underpin a review, planning the searches is integral to the overall RR preparation. The RR search process follows the same steps as an SR search; therefore, RR teams must be familiar with the general standards of systematic searching . Templates (see online supplemental appendix B ) and reporting guidance 11 for SR searches can also be adapted to structure the RR search process.

Developing a plan for the literature search forms part of protocol development and should involve an information specialist (eg, librarian). Information specialists can assist in refining the research question, selecting appropriate search methods and resources, designing and executing search strategies, and reporting the search methods. At minimum, specialist input should include assessing information sources and methods and providing feedback on the primary database search strategy.

Two options exist for abbreviating the search process: (1) reducing time spent on conducting searches (eg, using automation tools, reusing existing search strategies, omitting planning or quality assurance steps) and (2) reducing the size of the search result (eg, limiting the number of information sources, increasing the precision of search strategies, using study design filters). Study selection (ie, screening search results) is usually more resource-intensive than searching, 12 particularly for topics with complex or broad concepts or diffuse terminology; thus, the second option may be more efficient for the entire RR. Investing time upfront in optimising search sensitivity (ie, completeness) and precision (ie, positive predictive value) can save time in the long run by reducing the screening and selection workload.

Preliminary or scoping searches are critical to this process. They inform the choice of search methods and identify potentially relevant literature. Texts identified through preliminary searching serve as known relevant records that can be used throughout the search development process (see sections on database selection, development and validation of search strategies).

In addition to planning the search itself, the review team should factor in time for quality assurance steps (eg, search strategy peer review) and the management of search results (eg, deduplication, full-text retrieval).

Information sources and methods

To optimise the balance of search sensitivity and precision, RR teams should prioritise the most relevant information sources for the topic and the type of evidence required. These can include bibliographic databases (eg, MEDLINE/PubMed), grey literature sources and targeted supplementary search methods. Note that this approach differs from the Methodological Expectations of Cochrane Intervention Reviews Standards 9 where the same core set of information sources is required for every review and further supplemented by additional topic-specific and evidence-specific sources.

Choosing bibliographic databases

For many review topics, most evidence is found in peer-reviewed journal articles, making bibliographic databases the main resource of systematic searching. Limiting the number of databases searched can be a viable option in RRs, but it is important to prioritise topic-appropriate databases.

MEDLINE has been found to have high coverage for studies included in SRs 13 14 and is an appealing database choice because access is free via PubMed. However, coverage varies depending on topics and relevant study designs. 15 16 Additionally, even if all eligible studies for a topic were available in MEDLINE, search strategies will usually miss some eligible studies because search sensitivity is lower than database coverage. 13 17 This means searching MEDLINE alone is not a viable option, and additional information sources or search methods are required. Known relevant records can be used to help assess the coverage of selected databases (see also online supplemental appendix C ).

Further information sources and search techniques

Supplementary systematic search methods have three main goals, to identify (1) grey literature, (2) published literature not covered by the selected bibliographic databases and (3) database-covered literature that was not retrieved by the database searches.

When RRs search only a small number of databases, supplementary searches can be particularly important to pick up eligible studies not identified via database searching. While supplementary methods might increase the time spent on searching, they sometimes better optimise search sensitivity and precision, saving time in the long run. 18 Depending on the topic and relevant evidence, such methods can offer an alternative to adding additional specialised database searches. To decide if and what supplementary searches are helpful, it is important to evaluate what literature might be missed by the database searches and how this might affect the specific RR.

Study registries and other grey literature

Some studies indicate that the omission of grey literature searches rarely affects review conclusions. 17 19 However, the relevance of study registries and other grey literature sources is topic-dependent. 16 19–21 For example, randomised controlled trials (RCTs) on newly approved drugs are typically identified in ClinicalTrials.gov. 20 For rapidly evolving topics such as COVID-19, preprints are an important source. 21 For public health interventions, various types of grey literature may be important (eg, evaluations conducted by local public health agencies). 22

Further supplementary search methods

Other supplementary techniques (eg, checking reference lists, reviewing specific websites or electronic table of contents, contacting experts) may identify additional studies not retrieved by database searches. 23 One of the most common approaches involves checking reference lists of included studies and relevant reviews. This method may identify studies missed by limited database searches. 12 Another promising citation-based approach is using the ‘similar articles’ option in PubMed, although research has focused on updating existing SRs. 24 25

Considerations for RRs of RCTs

Databases and search methods to identify RCTs have been particularly well researched. 17 20 24 26 27 For this reason, it is possible to give more precise recommendations for RRs based on RCTs than for other types of review. Table 2 provides an overview of the most important considerations; additional information can be found in online supplemental appendix C .

Information sources for identification of randomised controlled trials (RCTs)

Search strategies

We define ‘search strategy’ as a Boolean search query in a specific database (eg, MEDLINE) using a specific interface (eg, Ovid). When several databases are searched, this query is usually developed in a primary database and interface (eg, Ovid MEDLINE) and translated to other databases.

Developing search strategies

Optimising search strategy precision while aiming for high sensitivity is critical in reducing the number of records retrieved. Preliminary searches provide crucial information to aid efficient search strategy development. Reviewing the abstracts and subject headings used in known relevant records will assist in identifying appropriate search terms. Text analysis tools can also be used to support this process, 28 29 for example, to develop ‘objectively derived’ search strategies. 30

Reusing or adapting complete search strategies (eg, from SRs identified by the preliminary searches) or selecting elements of search strategies for reuse can accelerate search strategy development. Additionally, validated search filters (eg, for study design) can be used to reduce the size of the search result without compromising the sensitivity of a search strategy. 31 However, quality assurance measures are necessary whether the search strategy is purpose-built, reused or adapted (see the ‘Quality assurance’ section.)

Database-specific and interface-specific functionalities can also be used to improve searches’ precision and reduce the search result’s size. Some options are: restricting to records where subject terms have been assigned as the major focus of an article (eg, major descriptors in MeSH), using proximity operators (ie, terms adjacent or within a set number of words), frequency operators (ie, terms have to appear a minimum number of times in an abstract) or restricting search terms to the article title. 32–34

Automated syntax translation can save time and reduce errors when translating a primary search strategy to different databases. 35 36 However, manual adjustments will usually be necessary.

The time taken to learn how to use supporting technologies (eg, text analysis, syntax translation) proficiently should not be underestimated. The time investment is most likely to pay off for frequent searchers. A later paper in this series will address supporting software for the entire review process.

Limits and restrictions

Limits and restrictions (eg, publication dates, language) are another way to reduce the number of records retrieved but should be tailored to the topic and applied with caution. For example, if most studies about an intervention were published 10 years ago, then an arbitrary cut-off of ‘the last 5 years’ will miss many relevant studies. 37 Similarly, limiting to ‘English only’ is acceptable for most cases, but early in the COVID-19 pandemic, a quarter of available research articles were written in Chinese. 38 Depending on the RR topic, certain document types (eg, conference abstracts, dissertations) might be excluded if not considered relevant to the research question.

Note also that preset limiting functions in search interfaces (eg, limit to humans) often rely on subject headings (eg, MeSH) alone. They will miss eligible studies that lack or have incomplete subject indexing. Using (validated) search filters 31 is preferable.

Updating existing reviews

One approach to RR production involves updating an existing SR. In this case, preliminary searches should be used to check if new evidence is available. If the review team decide to update the review, they should assess the original search methods and adapt these as necessary.

One option is to identify the minimum set of databases required to retrieve all the original included studies. 39 Any reused search strategies should be validated and peer-reviewed (see below) and optimised for precision and/or sensitivity.

Additionally, it is important to assess whether the topic terminology or the relevant databases have changed since the original SR search.

In some cases, designing a new search process may be more efficient than reproducing the original search.

Quality assurance and search strategy peer review

Errors in search strategies are common and can impact the sensitivity and comprehensiveness of the search result. 40 If an RR search uses a small number of information sources, such errors could affect the identification of relevant studies.

Validation of search strategies

The primary database search strategy should be validated using known relevant records (if available). This means testing if the primary search strategy retrieves eligible studies found through preliminary searching. If some known studies are not identified, the searcher assesses the reasons and decides if revisions are necessary. Even a precision-focused systematic search should identify the majority—we suggest at least 80%–90%—of known studies. This is based on benchmarks for sensitivity-precision-maximising search filters 41 and assumes that the set of known studies is representative of the whole of relevant studies.

Peer review of search strategies

Ideally, an information specialist should review the planned information sources and search methods and use the PRESS (Peer Review of Electronic Search Strategies) checklist 42 to assess the primary search strategy. Turnaround time has to be factored into the process from the outset (eg, waiting for feedback, revising the search strategy). PRESS recommends a maximum turnaround time of five working days for feedback, but in-house peer review often takes only a few hours.

If the overall RR time plan does not allow for a full peer review of the search strategy, a review team member with search experience should check the search strategy for spelling errors and correct use of Boolean operators (AND, OR, NOT) at a minimum.

Reporting and record management

Record management requirements of RRs are largely identical to SRs and have to be factored into the time plan. Teams should develop a data management plan and review the relevant reporting standards at the project’s outset. PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension) 11 is a reporting standard for SR searches that can be adapted for RRs.

Reference management software (eg, EndNote, 43 Zotero 44 ) should be used to track search results, including deduplication. Note that record management for database searches is less time-consuming than for many supplementary or grey literature searches, which often require manual entry into reference management software. 12

Additionally, software platforms for SR production (eg, Covidence, 45 EPPI-Reviewer, 46 Systematic Review Data Repository Plus 47 ) can provide a unified way to keep track of records throughout the whole review process, which can improve management and save time. These platforms and other dedicated tools (eg, SRA Deduplicator) 48 also offer automated deduplication. However, the time and cost investment necessary to appropriately use these tools have to be considered.

Decisions about search methods for an RR need to consider where time can be most usefully invested and processes accelerated. The literature search should be considered in the context of the entire review process, for example, protocol development and literature screening: Findings of preliminary searches often affect the development and refinement of the research question and the review’s eligibility criteria . In turn, they affect the number of records retrieved by the searches and therefore the time needed for literature selection.

For this reason, focusing only on reducing time spent on designing and conducting searches can be a false economy when seeking to speed up review production. While some approaches (eg, text analysis, automated syntax translation) may save time without negatively affecting search validity, others (eg, skipping quality assurance steps, using convenient information sources without considering their topic appropriateness) may harm the entire review. Information specialists can provide crucial aid concerning the appropriate design of search strategies, choice of methods and information sources.

For this reason, we consider that investing time at the outset of the review to carefully choose a small number of highly appropriate search methods and optimise search sensitivity and precision likely leads to better and more manageable results.

Ethics statements

Patient consent for publication.

Not applicable.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Data supplement 1

Twitter @micaelaescb

Collaborators On behalf of the Cochrane Rapid Reviews Methods Group: Declan Devane, Gerald Gartlehner, Isolde Sommer.

Contributors IK, SR, AB, CME-L and SW contributed to the conceptualisation of this paper. IK, AB and CME-L wrote the first draft of the manuscript. All authors critically reviewed and revised the manuscript. IK is responsible for the overall content.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests AB is co-convenor of the Cochrane Qualitative and Implementation Methods Group. In the last 36 months, he received royalties from Systematic Approaches To a Successful Literature Review (Sage 3rd edn), payment or honoraria form the Agency for Healthcare Research and Quality, and travel support from the WHO. DD works part time for Cochrane Ireland and Evidence Synthesis Ireland, which are funded within the University of Ireland Galway (Ireland) by the Health Research Board (HRB) and the Health and Social Care, Research and Development (HSC R&D) Division of the Public Health Agency in Northern Ireland.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Linked Articles

Research methods and reporting Rapid reviews methods series: Guidance on team considerations, study selection, data extraction and risk of bias assessment Barbara Nussbaumer-Streit Isolde Sommer Candyce Hamel Declan Devane Anna Noel-Storr Livia Puljak Marialena Trivella Gerald Gartlehner BMJ Evidence-Based Medicine 2023; 28 418-423 Published Online First: 19 Apr 2023. doi: 10.1136/bmjebm-2022-112185
Research methods and reporting Rapid reviews methods series: Guidance on assessing the certainty of evidence Gerald Gartlehner Barbara Nussbaumer-Streit Declan Devane Leila Kahwati Meera Viswanathan Valerie J King Amir Qaseem Elie Akl Holger J Schuenemann BMJ Evidence-Based Medicine 2023; 29 50-54 Published Online First: 19 Apr 2023. doi: 10.1136/bmjebm-2022-112111
Research methods and reporting Rapid Reviews Methods Series: Involving patient and public partners, healthcare providers and policymakers as knowledge users Chantelle Garritty Andrea C Tricco Maureen Smith Danielle Pollock Chris Kamel Valerie J King BMJ Evidence-Based Medicine 2023; 29 55-61 Published Online First: 19 Apr 2023. doi: 10.1136/bmjebm-2022-112070

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Systematic Literature Review of the Research Design Based on the Tafsir Tahliliy Method

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A systematic literature review of modalities, trends, and limitations in emotion recognition, affective computing, and sentiment analysis.

1. Introduction

2. methodology, 2.1. research questions, 2.2. search process, 2.2.1. search terms, 2.2.2. inclusion and exclusion criteria, 2.2.3. quality assessment, 2.2.4. data extraction, 3.1. overview, 3.2. unimodal data approaches, 3.2.1. unimodal physical approaches, 3.2.2. unimodal speech data approaches.

Several articles mention the use of transfer learning for speech emotion recognition. This technique involves training models on one dataset and applying them to another. This can improve the efficiency of emotion recognition across different datasets.
Some articles discuss multitask learning models, which are designed to simultaneously learn multiple related tasks. In the context of speech emotion recognition, this approach may help capture commonalities and differences across different datasets or emotions.
Data augmentation techniques are mentioned in multiple articles, which involve generating additional training data from existing data, which can improve model performance and generalization.
Attention mechanisms are a common trend for improving emotion recognition. Attention models allow the model to focus on specific features or segments of the input data that are most relevant for recognizing emotions, such as in multi-level attention-based approaches.
Many articles discuss the use of deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and some variants like “Two-Stage Fuzzy Fusion Based-Convolution Neural Network, “Deep Convolutional LSTM”, and “Attention-Oriented Parallel CNN Encoders”.
While deep learning is prevalent, some articles explore novel feature engineering methods, such as modulation spectral features and wavelet packet information gain entropy, to enhance emotion recognition.
From the list of articles on unimodal emotion recognition through speech, 7.14% address the challenge of recognizing emotions across different datasets or corpora. This is an important trend for making emotion recognition models more versatile.
A few articles focus on making emotion recognition models more interpretable and explainable, which is crucial for real-world applications and understanding how the model makes its predictions.
Ensemble methods, which combine multiple models to make predictions, are mentioned in several articles as a way to improve the performance of emotion recognition systems.
Some articles discuss emotion recognition in specific contexts, such as call/contact centers, school violence detection, depression detection, analysis of podcast recordings, noisy environment analysis, in-the-wild sentiment analysis, and speech emotion segmentation of vowel-like and non-vowel-like regions. This indicates a trend toward applying emotion recognition in diverse applications.

3.2.3. Unimodal Text Data Approaches

3.2.4. unimodal physiological data approaches.

Attention and self-attention mechanisms: These suggest that researchers are paying attention to the relevance of different parts of EEG signals for emotion recognition.
Generative adversarial networks (GANs): Used for generating synthetic EEG data in order to improve the robustness and generalization of the models.
Semi-supervised learning and domain transfer: Allow emotion recognition with limited datasets or datasets that are applicable to different domains, suggesting a concern for scalability and generalization of models.
Interpretability and explainability: There is a growing interest in models that are interpretable and explainable, suggesting a concern for understanding how models make decisions and facilitating user trust in them.
Utilization of transformers and capsule networks: Newer neural network architectures such as transformers and capsule networks are being explored for emotion recognition, indicating an interest in enhancing the modeling and representation capabilities of EEG signals.
Although studies with a unimodal physical approach using signals different from EEG, like ECG, EDA, HR, and PPG, are still scarce, these can provide information about the cardiovascular system and the body’s autonomic response to emotions. Their limitations are that they may not be as specific or sensitive in detecting subtle or changing emotions. Noise and artifacts, such as motion, can affect the quality of these signals in practical situations and can be influenced by non-emotional factors, such as physical exercise and fatigue. Various studies explore the utilization of ECG and PPG signals for emotion recognition and stress classification. Techniques such as CNNs, LSTMs, attention mechanisms, self-supervised learning, and data augmentation are employed to analyze these signals and extract meaningful features for emotion recognition tasks. Bayesian deep learning frameworks are utilized for probabilistic modeling and uncertainty estimation in emotion prediction from HB data. These approaches aim to enhance human–computer interaction, improve mental health monitoring, and develop personalized systems for emotion recognition based on individual user characteristics.

3.3. Multi-Physical Data Approaches

Most studies employ CNNs and RNNs, while others utilize variations of general neural networks, such as spiking neural networks (SNN) and tree-based neural networks. SNNs represent and transmit information through discrete bursts of neuronal activity, known as “spikes” or “pulses”, unlike conventional neural networks, which process information in continuous values. Additionally, several studies leverage advanced analysis models such as the stacked ensemble model and multimodal fusion models, which focus on integrating diverse sources of information to enhance decision-making. Transfer learning models and hybrid attention networks aim to capitalize on knowledge from related tasks or domains to improve performance in a target task. Attention-based neural networks prioritize capturing relevant information and patterns within the data. Semi-supervised and contrastive learning models offer alternative learning paradigms by incorporating both labeled and unlabeled data.
The studies address diverse applications, including sarcasm, sentiment, and emotion recognition in conversations, financial distress prediction, performance evaluation in job interviews, emotion-based location recommendation systems, user experience (UX) analysis, emotion detection in video games, and in educational settings. This suggests that emotion recognition thorough multi-physical data analysis has a wide spectrum of applications in everyday life.
Various audio and video signal processing techniques are employed, including pitch analysis, facial feature detection, cross-attention, and representational learning.

3.4. Multi-Physiological Data Approaches

The fusion of physiological signals, such as EEG, ECG, PPG, GSR, EMG, BVP, EOG, respiration, temperature, and movement signals, is a predominant trend in these studies. The combination of multiple physiological signals allows for a richer representation of emotions.
Most studies apply deep learning models, such as CNNs, RNNs, and autoencoder neural networks (AE), for the processing and analysis of these signals. Supervised and unsupervised learning approaches are also used.
These studies focus on a variety of applications, such as emotion recognition in healthcare environments, brain–computer interfaces for music, emotion detection in interactive virtual environments, stress assessment in mobility environments for visually impaired people, among others. This indicates that emotion recognition based on physiological signals has applications in healthcare, technology, and beyond.
Some studies focus on personalized emotion recognition, suggesting tailoring of models for each individual. This may be relevant for personalized health and wellness applications. Others focus on interactive applications and virtual environments useful for entertainment and virtual therapy.
It is important to mention that the studies within this classification are quite limited in comparison to the previously described modalities. Although it appears that they are using similar physiological signals, the databases differ in terms of their approaches and generation methods. Therefore, there is an opportunity to establish a protocol for generating these databases, allowing for meaningful comparisons among studies.

3.5. Multi-Physical–Physiological Data Approaches

Studies tend to combine multiple types of signals, such as EEG, facial expressions, voice signals, GSR, and other physiological data. Combining signals aims to take advantage of the complementarity of different modalities to improve accuracy in emotion detection.
Machine learning models, in particular CNNs, are widely used in signal fusion for emotion recognition. CNN models can effectively process data from multiple modalities.
Applications are also being explored in the health and wellness domain, such as emotion detection for emotional health analysis of people in smart environments.
The use of standardized and widely accepted databases is important for comparing results between different studies; however, these are still limited.
The trend towards non-intrusive sensors and wireless technology enables data collection in more natural and less intrusive environments, which facilitates the practical application of these systems in everyday environments.

4. Discussion

Facial expression analysis approaches are currently being applied across various domains, including naturalistic settings (“in the wild”), on-road driver monitoring, virtual reality environments, smart homes, IoT and edge devices, and assistive robots. There is also a focus on mental health assessment, including autism, depression, and schizophrenia, and distinguishing between genuine and unfelt facial expressions of emotion. Efforts are being made to improve performance in processing faces acquired at a distance despite the challenges posed by low-quality images. Furthermore, there is an emerging interest in utilizing facial expression analysis in human–computer interaction (HCI), learning environments, and multicultural contexts.
The recognition of emotions through speech and text has experienced tremendous growth, largely due to the abundance of information facilitated by advancements in technology and social media. This has enabled individuals to express their opinions and sentiments through various media, including podcast recordings, live videos, and readily available data sources such as social media platforms like Twitter, Facebook, Instagram, and blogs. Additionally, researchers have utilized unconventional sources like stock market data and tourism-related reviews. The variety and richness of these data sources indicate a wide range of segments where such emotion recognition analyses can be applied effectively.
EEG signals continue to be a prominent modality for emotion recognition due to their highly accurate insights into emotional states. Between 2022 and 2023, studies in this field experienced exponential growth. The identified trends include utilizing EEG for enhancing human–computer interaction, recognizing emotions in various contexts such as patients with consciousness disorders, movie viewing, virtual environments, and driving scenarios. EEG is being used for detecting and monitoring mental health issues. There is also a growing focus on personalization, leading towards more individualized and user-specific emotion recognition systems, Other physiological signals, such as ECG, EDA, and HR, are also gaining attention, albeit at a slower pace.
In the realm of multi-physical, multi-physiological, and multi-physical–physiological approaches, it is the former that appears to be laying the groundwork, as evidenced by the abundance of studies in this area. The latter two approaches, incorporating fusions with physiological signals, are still relatively scarce but seem to be paving the way for future researchers to contribute to their growth. Multimodal approaches, which integrate both physical and physiological signals, are finding diverse applications in emotion recognition. These range from healthcare systems, individual and group mood research, personality recognition, pain intensity recognition, anxiety detection, work stress detection, stress classification and security monitoring in public spaces, to vehicle security monitoring, movie audience emotion recognition, applications for autism spectrum disorder detection, music interfacing, and virtual environments.
Bidirectional encoder representations from transformers: Used in sentiment analysis and emotion recognition from text, BERT models can understand the context of words in sentences by pre-training on a large text and then fine-tuning for specific tasks like sentiment analysis.
CNNs: These are commonly applied in facial emotion recognition, emotion recognition from physiological signals, and even in speech emotion recognition by analyzing spectrograms.
RNNS and variants (LSTM, GRU): These models are suited for sequential data like speech and text. LSTMs and GRUs are particularly effective in speech emotion recognition and sentiment analysis of time-series data.
Graph convolutional networks (GCNs): Applied in emotion recognition from EEG signals and conversation-based emotion recognition, these can model relational data and capture the complex dependencies in graph-structured data, like brain connectivity patterns or conversational contexts.
Attention mechanisms and transformers: Enhancing the ability of models to focus on relevant parts of the data, attention mechanisms are integral to models like transformers for tasks that require understanding the context, such as sentiment analysis in long documents or emotion recognition in conversations.
Ensemble models: Combining predictions from multiple models to improve accuracy, ensemble methods are used in multimodal emotion recognition, where inputs from different modalities (e.g., audio, text, and video) are integrated to make more accurate predictions.
Autoencoders and generative adversarial networks (GANs): For tasks like data augmentation in emotion recognition from EEG or for generating synthetic data to improve model robustness, these unsupervised learning models can learn compact representations of data or generate new data samples, respectively.
Multimodal fusion models: In applications requiring the integration of multiple data types (e.g., speech, text, and video for emotion recognition), fusion models combine features from different modalities to capture more comprehensive information for prediction tasks.
Transfer learning: Utilizing pre-trained models on large datasets and fine-tuning them for specific affective computing tasks, transfer learning is particularly useful in scenarios with limited labeled data, such as sentiment analysis in niche domains.
Spatio-temporal models: For tasks that involve data with both spatial and temporal dimensions (like video-based emotion recognition or physiological signal analysis), models that capture spatio-temporal dynamics are employed, combining approaches like CNNs for spatial features and RNNs/LSTMs for temporal features.

5. Conclusions

Author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Database	Resulted Studies with Key Terms	After Years Filter	After Article Type	Relevant Order
IEEE	2112	1152	536	200
Springer	4121	1808	1694	200
Science Direct	1041	582	480	200
MDPI	686	643	635	200

Database	Quantity
IEEE	148
Springer	112
Science Direct	166
MDPI	183

Modality	2018	2019	2020	2021	2022	2023	Total
Multi-physical	8	6		8	22	27	71
Multi-physical–physiological	2			3	6	7	18
Multi-physiological	2		6	3	6	4	21
Unimodal	37	26	29	37	176	194	499
Total	49	32	35	51	210	232	609

Article Title	Databases Used	Ref.
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild.	AffectNet	[ ]
Video-Based Depression Level Analysis by Encoding Deep Spatiotemporal Features.	AVEC2013, AVEC2014	[ ]
Exploiting Multi-CNN Features in CNN-RNN Based Dimensional Emotion Recognition on the OMG in-the-Wild Dataset.	Aff-Wild, Aff-Wild2, OMG	[ ]
A Deeper Look at Facial Expression Dataset Bias.	CK+, JAFFE, MMI, Oulu-CASIA, AffectNet, FER2013, RAF-DB 2.0, SFEW 2.0	[ ]
Automatic Recognition of Facial Displays of Unfelt Emotions.	CK+, OULU-CASIA, BP4D	[ ]
Spatio-Temporal Encoder-Decoder Fully Convolutional Network for Video-Based Dimensional Emotion Recognition.	OMG, RECOLA, SEWA	[ ]
Efficient Net-XGBoost: An Implementation for Facial Emotion Recognition Using Transfer Learning.	CK+, FER2013, JAFFE, KDEF	[ ]
Masked Face Emotion Recognition Based on Facial Landmarks and Deep Learning Approaches for Visually Impaired People.	AffectNet	[ ]
Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition.	JAFFE	[ ]
Manta Ray Foraging Optimization with Transfer Learning Driven Facial Emotion Recognition.	CK+, FER-2013	[ ]
Emotion recognition at a distance: The robustness of machine learning based on hand-crafted facial features vs deep learning models.	CK+	[ ]
Deep learning-based dimensional emotion recognition combining the attention mechanism and global second-order feature representations.	AffectNet	[ ]
On-road driver facial expression emotion recognition with parallel multi-verse optimizer (PMVO) and optical flow reconstruction for partial occlusion in internet of things (IoT).	CK+, KMU-FED	[ ]
Emotion recognition by web-shaped model.	CK+, KDEF	[ ]
Edge-enhanced bi-dimensional empirical mode decomposition-based emotion recognition using fusion of feature set	eNTERFACE, CK, JAFFE	[ ]
A novel driver emotion recognition system based on deep ensemble classification	AffectNet, CK+, DFER, FER-2013, JAFFE, and custom- dataset)	[ ]

1.Facial emotion recognition for mental health assessment (depression, schizophrenia)	14. Emotion recognition performance assessment from faces acquired at a distance.
2. Emotion analysis in human-computer interaction	15. Facial emotion recognition for IoT and edge devices
3. Emotion recognition in the context of autism	16. Idiosyncratic bias in emotion recognition
4. Driver emotion recognition for intelligent vehicles	17. Emotion recognition in socially assistive robots
5. Assessment of emotional engagement in learning environments	18. In the wild facial emotion recognition
6. Facial emotion recognition for apparent personality trait analysis	19. Video-based emotion recognition
7. Facial emotion recognition for gender, age, and ethnicity estimation	20. Spatio-temporal emotion recognition in videos
8. Emotion recognition in virtual reality and smart homes	21. Spontaneous emotion recognition
9. Emotion recognition in healthcare and clinical settings	22. Emotion recognition using facial components
10. Emotion recognition in real-world and COVID-19 masked scenarios	23. Comparing emotion recognition from genuine and unfelt
11. Personalized and group-based emotion recognition	facial expressions.
12. Music-enhanced emotion recognition
13. Cross-dataset emotion recognition

Database Name	Description	Advantages	Limitation
MELD (Multimodal Emotion Lines Dataset) [ ]	Focuses on emotion recognition in movie dialogues. It contains transcriptions of dialogues and their corresponding audio and video tracks. Emotions are labeled at the sentence and speaker levels.	Large amount of data, multimodal (text, audio, video).	Emotions induced by movies. Manually labeled.
IEMOCAP (Interactive Emotional Dyadic Motion Capture), 2005 [ ]	Focuses on emotional interactions between two individuals during acting sessions. It contains video and audio recordings of actors performing emotional scenes.	Realistic data, emotional interactions, a wide range of emotions.	Not real induced emotions (acting).
CMU-MOSI (Multimodal Corpus of Sentiment Intensity. 2014, 2017 [ ]	Focuses on sentiment intensity in speeches and interviews. It includes transcriptions of audio and video, along with sentiment annotations. Updated in the 2017 CMU-MOSEI.	Emotions are derived from real speeches and interviews.	Relatively small size.
AVEC (Affective Behavior in the Context of E-Learning with Social Signals 2007–2016 [ ]	AVEC is a series of competitions focused on the detection of emotions and behaviors in the context of online learning. It includes video and audio data of students participating in e-learning activities.	Emotions are naturally induced during online learning activities.	Context-specific data, enables emotion assessment in e-learning settings.
RAVDESS (The Ryerson Audio-Visual Database of Emotional Speech and Song) 2016 [ ]	Audio and video database that focuses on emotion recognition in speech and song. It includes performances by actors expressing various emotions.	Diverse data in terms of emotions, modalities, and contexts.	Does not contain natural dialogues.
SAVEE (Surrey Audio–Visual Expressed Emotion) 2010 [ ]	Focuses on emotion recognition in speech. It contains recordings of speakers expressing emotions through phrases and words.	Clean audio data.
SAMM (Spontaneous Micro-expression Dataset) [ ]	Focuses on spontaneous micro-expressions that last only a fraction of a second. It contains videos of people expressing emotions in real emotional situations.	Real spontaneous micro-expressions.
CASME (Chinese Academy of Sciences Micro-Expression) [ ]	Focus on the detection of micro-expressions in response to emotional stimuli. They contain videos of micro-expressions.	Induced by emotional stimuli.	Not multicultural.

Database Name	Description	Advantages	Limitation
WESAD (Wearable Stress and Affect Detection) [ ]	It focuses on stress and affect recognition from physiological signals like ECG, EMG, and EDA, as well as motion signals from accelerometers. Data were collected while participants performed tasks and experienced emotions in a controlled laboratory setting, wearing wearable sensors.	Facilitates the development of wearable emotion recognition systems.	The dataset is relatively small, and participant diversity may be limited.
AMIGOS [ ]	It is a multimodal dataset for personality traits and mood. Emotions are induced by emotional videos in two social contexts: one with individual viewers and one with groups of viewers. Participants’ EEG, ECG, and GSR signals were recorded using wearable sensors. Frontal HD videos and full-body videos in RGB and depth were also recorded.	Participants’ emotions were scored by self-assessment of valence, arousal, control, familiarity, liking, and basic emotions felt during the videos, as well as external assessments of valence and arousal.	Reduced number of participants.
DREAMER [ ]	Records physiological ECG, EMG, and EDA signals and self-reported emotional responses. Collected during the presentation of emotional video clips.	Enables the study of emotional responses in a controlled environment and their comparison with self-reported emotions.	Emotions may be biased towards those induced by video clips, and the dataset size is limited.
ASCERTAIN [ ]	Focus on linking personality traits and emotional states through physiological responses like EEG, ECG, GSR, and facial activity data while participants watched emotionally charged movie clips.	Suitable for studying emotions in stressful situations and their impact on human activity.	The variety of emotions induced is limited.
DEAP (Database for Emotion Analysis using Physiological Signals), [ , ]	Includes physiological signals like EEG, ECG, EMG, and EDA, as well as audiovisual data. Data were collected by exposing participants to audiovisual stimuli designed to elicit various emotions.	Provides a diverse range of emotions and physiological data for emotion analysis.	The size of the database is small.
MAHNOB-HCI (Multimodal Human Computer Interaction Database for Affect Analysis and Recognition) [ , ].	Includes multimodal data, such as audio, video, physiological, ECG, EDA, and kinematic data. Data were collected while participants engaged in various human–computer interaction scenarios.	Offers a rich dataset for studying emotional responses during interactions with technology.

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García-Hernández, R.A.; Luna-García, H.; Celaya-Padilla, J.M.; García-Hernández, A.; Reveles-Gómez, L.C.; Flores-Chaires, L.A.; Delgado-Contreras, J.R.; Rondon, D.; Villalba-Condori, K.O. A Systematic Literature Review of Modalities, Trends, and Limitations in Emotion Recognition, Affective Computing, and Sentiment Analysis. Appl. Sci. 2024 , 14 , 7165. https://doi.org/10.3390/app14167165

García-Hernández RA, Luna-García H, Celaya-Padilla JM, García-Hernández A, Reveles-Gómez LC, Flores-Chaires LA, Delgado-Contreras JR, Rondon D, Villalba-Condori KO. A Systematic Literature Review of Modalities, Trends, and Limitations in Emotion Recognition, Affective Computing, and Sentiment Analysis. Applied Sciences . 2024; 14(16):7165. https://doi.org/10.3390/app14167165

García-Hernández, Rosa A., Huizilopoztli Luna-García, José M. Celaya-Padilla, Alejandra García-Hernández, Luis C. Reveles-Gómez, Luis Alberto Flores-Chaires, J. Ruben Delgado-Contreras, David Rondon, and Klinge O. Villalba-Condori. 2024. "A Systematic Literature Review of Modalities, Trends, and Limitations in Emotion Recognition, Affective Computing, and Sentiment Analysis" Applied Sciences 14, no. 16: 7165. https://doi.org/10.3390/app14167165

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Protocol for Systematic Review and Meta-Analysis of Prehospital Large Vessel Occlusion Screening Scales

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Background Large Vessel Occlusion (LVO) is a serious condition that causes approximately 24-46% of acute ischemic strokes (AIS). LVO strokes tend to have higher mortality rates and result in more severe longterm disabilities compared to non LVO ischemic strokes. Early intervention with endovascular therapy (EVT) is recommended; however, EVT is limited to tertiary care hospitals with specialized facilities. Therefore, identifying patients with a high probability of LVO in prehospital settings and ensuring their rapid transfer to appropriate hospitals is crucial. While LVO diagnosis typically requires advanced imaging like MRI or CT scans, various scoring systems based on neurological symptoms have been developed for prehospital use. Although previous systematic reviews have addressed some of these scales, recent studies have introduced new scales and additional data on their accuracy. This systematic review and meta-analysis aim to summarize the current evidence on the diagnostic accuracy of these prehospital LVO screening scales.

Methods This systematic review and meta-analysis will be conducted in accordance with the PRISMA-DTA Statement and the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. We will include observational studies and randomized controlled trials that assess the utility of LVO scales in suspected stroke patients in prehospital settings. Eligible studies must provide sufficient data to calculate sensitivity and specificity, and those lacking such data or being case reports will be excluded. The literature search will cover CENTRAL, MEDLINE, and Ichushi databases, including studies in English and Japanese. Bias will be assessed using QUADAS-2, and meta-analysis will be conducted using a random effects model, with subgroup and sensitivity analyses to explore heterogeneity.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

We will search the following databases CENTRAL, MEDLINE, and Ichushi.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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All data produced in the present study are available upon reasonable request to the authors.

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Behind the Intent of Extract Method Refactoring: A Systematic Literature Review

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Bibliometrics & citations, view options, recommendations, uml model refactoring: a systematic literature review.

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Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.

Cover of Handbook of eHealth Evaluation: An Evidence-based Approach

Handbook of eHealth Evaluation: An Evidence-based Approach [Internet].

Chapter 9 methods for literature reviews.

Guy Paré and Spyros Kitsiou .

9.1. Introduction

Literature reviews play a critical role in scholarship because science remains, first and foremost, a cumulative endeavour ( vom Brocke et al., 2009 ). As in any academic discipline, rigorous knowledge syntheses are becoming indispensable in keeping up with an exponentially growing eHealth literature, assisting practitioners, academics, and graduate students in finding, evaluating, and synthesizing the contents of many empirical and conceptual papers. Among other methods, literature reviews are essential for: (a) identifying what has been written on a subject or topic; (b) determining the extent to which a specific research area reveals any interpretable trends or patterns; (c) aggregating empirical findings related to a narrow research question to support evidence-based practice; (d) generating new frameworks and theories; and (e) identifying topics or questions requiring more investigation ( Paré, Trudel, Jaana, & Kitsiou, 2015 ).

Literature reviews can take two major forms. The most prevalent one is the “literature review” or “background” section within a journal paper or a chapter in a graduate thesis. This section synthesizes the extant literature and usually identifies the gaps in knowledge that the empirical study addresses ( Sylvester, Tate, & Johnstone, 2013 ). It may also provide a theoretical foundation for the proposed study, substantiate the presence of the research problem, justify the research as one that contributes something new to the cumulated knowledge, or validate the methods and approaches for the proposed study ( Hart, 1998 ; Levy & Ellis, 2006 ).

The second form of literature review, which is the focus of this chapter, constitutes an original and valuable work of research in and of itself ( Paré et al., 2015 ). Rather than providing a base for a researcher’s own work, it creates a solid starting point for all members of the community interested in a particular area or topic ( Mulrow, 1987 ). The so-called “review article” is a journal-length paper which has an overarching purpose to synthesize the literature in a field, without collecting or analyzing any primary data ( Green, Johnson, & Adams, 2006 ).

When appropriately conducted, review articles represent powerful information sources for practitioners looking for state-of-the art evidence to guide their decision-making and work practices ( Paré et al., 2015 ). Further, high-quality reviews become frequently cited pieces of work which researchers seek out as a first clear outline of the literature when undertaking empirical studies ( Cooper, 1988 ; Rowe, 2014 ). Scholars who track and gauge the impact of articles have found that review papers are cited and downloaded more often than any other type of published article ( Cronin, Ryan, & Coughlan, 2008 ; Montori, Wilczynski, Morgan, Haynes, & Hedges, 2003 ; Patsopoulos, Analatos, & Ioannidis, 2005 ). The reason for their popularity may be the fact that reading the review enables one to have an overview, if not a detailed knowledge of the area in question, as well as references to the most useful primary sources ( Cronin et al., 2008 ). Although they are not easy to conduct, the commitment to complete a review article provides a tremendous service to one’s academic community ( Paré et al., 2015 ; Petticrew & Roberts, 2006 ). Most, if not all, peer-reviewed journals in the fields of medical informatics publish review articles of some type.

The main objectives of this chapter are fourfold: (a) to provide an overview of the major steps and activities involved in conducting a stand-alone literature review; (b) to describe and contrast the different types of review articles that can contribute to the eHealth knowledge base; (c) to illustrate each review type with one or two examples from the eHealth literature; and (d) to provide a series of recommendations for prospective authors of review articles in this domain.

9.2. Overview of the Literature Review Process and Steps

As explained in Templier and Paré (2015) , there are six generic steps involved in conducting a review article:

formulating the research question(s) and objective(s),
searching the extant literature,
screening for inclusion,
assessing the quality of primary studies,
extracting data, and
analyzing data.

Although these steps are presented here in sequential order, one must keep in mind that the review process can be iterative and that many activities can be initiated during the planning stage and later refined during subsequent phases ( Finfgeld-Connett & Johnson, 2013 ; Kitchenham & Charters, 2007 ).

Formulating the research question(s) and objective(s): As a first step, members of the review team must appropriately justify the need for the review itself ( Petticrew & Roberts, 2006 ), identify the review’s main objective(s) ( Okoli & Schabram, 2010 ), and define the concepts or variables at the heart of their synthesis ( Cooper & Hedges, 2009 ; Webster & Watson, 2002 ). Importantly, they also need to articulate the research question(s) they propose to investigate ( Kitchenham & Charters, 2007 ). In this regard, we concur with Jesson, Matheson, and Lacey (2011) that clearly articulated research questions are key ingredients that guide the entire review methodology; they underscore the type of information that is needed, inform the search for and selection of relevant literature, and guide or orient the subsequent analysis. Searching the extant literature: The next step consists of searching the literature and making decisions about the suitability of material to be considered in the review ( Cooper, 1988 ). There exist three main coverage strategies. First, exhaustive coverage means an effort is made to be as comprehensive as possible in order to ensure that all relevant studies, published and unpublished, are included in the review and, thus, conclusions are based on this all-inclusive knowledge base. The second type of coverage consists of presenting materials that are representative of most other works in a given field or area. Often authors who adopt this strategy will search for relevant articles in a small number of top-tier journals in a field ( Paré et al., 2015 ). In the third strategy, the review team concentrates on prior works that have been central or pivotal to a particular topic. This may include empirical studies or conceptual papers that initiated a line of investigation, changed how problems or questions were framed, introduced new methods or concepts, or engendered important debate ( Cooper, 1988 ). Screening for inclusion: The following step consists of evaluating the applicability of the material identified in the preceding step ( Levy & Ellis, 2006 ; vom Brocke et al., 2009 ). Once a group of potential studies has been identified, members of the review team must screen them to determine their relevance ( Petticrew & Roberts, 2006 ). A set of predetermined rules provides a basis for including or excluding certain studies. This exercise requires a significant investment on the part of researchers, who must ensure enhanced objectivity and avoid biases or mistakes. As discussed later in this chapter, for certain types of reviews there must be at least two independent reviewers involved in the screening process and a procedure to resolve disagreements must also be in place ( Liberati et al., 2009 ; Shea et al., 2009 ). Assessing the quality of primary studies: In addition to screening material for inclusion, members of the review team may need to assess the scientific quality of the selected studies, that is, appraise the rigour of the research design and methods. Such formal assessment, which is usually conducted independently by at least two coders, helps members of the review team refine which studies to include in the final sample, determine whether or not the differences in quality may affect their conclusions, or guide how they analyze the data and interpret the findings ( Petticrew & Roberts, 2006 ). Ascribing quality scores to each primary study or considering through domain-based evaluations which study components have or have not been designed and executed appropriately makes it possible to reflect on the extent to which the selected study addresses possible biases and maximizes validity ( Shea et al., 2009 ). Extracting data: The following step involves gathering or extracting applicable information from each primary study included in the sample and deciding what is relevant to the problem of interest ( Cooper & Hedges, 2009 ). Indeed, the type of data that should be recorded mainly depends on the initial research questions ( Okoli & Schabram, 2010 ). However, important information may also be gathered about how, when, where and by whom the primary study was conducted, the research design and methods, or qualitative/quantitative results ( Cooper & Hedges, 2009 ). Analyzing and synthesizing data : As a final step, members of the review team must collate, summarize, aggregate, organize, and compare the evidence extracted from the included studies. The extracted data must be presented in a meaningful way that suggests a new contribution to the extant literature ( Jesson et al., 2011 ). Webster and Watson (2002) warn researchers that literature reviews should be much more than lists of papers and should provide a coherent lens to make sense of extant knowledge on a given topic. There exist several methods and techniques for synthesizing quantitative (e.g., frequency analysis, meta-analysis) and qualitative (e.g., grounded theory, narrative analysis, meta-ethnography) evidence ( Dixon-Woods, Agarwal, Jones, Young, & Sutton, 2005 ; Thomas & Harden, 2008 ).

9.3. Types of Review Articles and Brief Illustrations

EHealth researchers have at their disposal a number of approaches and methods for making sense out of existing literature, all with the purpose of casting current research findings into historical contexts or explaining contradictions that might exist among a set of primary research studies conducted on a particular topic. Our classification scheme is largely inspired from Paré and colleagues’ (2015) typology. Below we present and illustrate those review types that we feel are central to the growth and development of the eHealth domain.

9.3.1. Narrative Reviews

The narrative review is the “traditional” way of reviewing the extant literature and is skewed towards a qualitative interpretation of prior knowledge ( Sylvester et al., 2013 ). Put simply, a narrative review attempts to summarize or synthesize what has been written on a particular topic but does not seek generalization or cumulative knowledge from what is reviewed ( Davies, 2000 ; Green et al., 2006 ). Instead, the review team often undertakes the task of accumulating and synthesizing the literature to demonstrate the value of a particular point of view ( Baumeister & Leary, 1997 ). As such, reviewers may selectively ignore or limit the attention paid to certain studies in order to make a point. In this rather unsystematic approach, the selection of information from primary articles is subjective, lacks explicit criteria for inclusion and can lead to biased interpretations or inferences ( Green et al., 2006 ). There are several narrative reviews in the particular eHealth domain, as in all fields, which follow such an unstructured approach ( Silva et al., 2015 ; Paul et al., 2015 ).

Despite these criticisms, this type of review can be very useful in gathering together a volume of literature in a specific subject area and synthesizing it. As mentioned above, its primary purpose is to provide the reader with a comprehensive background for understanding current knowledge and highlighting the significance of new research ( Cronin et al., 2008 ). Faculty like to use narrative reviews in the classroom because they are often more up to date than textbooks, provide a single source for students to reference, and expose students to peer-reviewed literature ( Green et al., 2006 ). For researchers, narrative reviews can inspire research ideas by identifying gaps or inconsistencies in a body of knowledge, thus helping researchers to determine research questions or formulate hypotheses. Importantly, narrative reviews can also be used as educational articles to bring practitioners up to date with certain topics of issues ( Green et al., 2006 ).

Recently, there have been several efforts to introduce more rigour in narrative reviews that will elucidate common pitfalls and bring changes into their publication standards. Information systems researchers, among others, have contributed to advancing knowledge on how to structure a “traditional” review. For instance, Levy and Ellis (2006) proposed a generic framework for conducting such reviews. Their model follows the systematic data processing approach comprised of three steps, namely: (a) literature search and screening; (b) data extraction and analysis; and (c) writing the literature review. They provide detailed and very helpful instructions on how to conduct each step of the review process. As another methodological contribution, vom Brocke et al. (2009) offered a series of guidelines for conducting literature reviews, with a particular focus on how to search and extract the relevant body of knowledge. Last, Bandara, Miskon, and Fielt (2011) proposed a structured, predefined and tool-supported method to identify primary studies within a feasible scope, extract relevant content from identified articles, synthesize and analyze the findings, and effectively write and present the results of the literature review. We highly recommend that prospective authors of narrative reviews consult these useful sources before embarking on their work.

Darlow and Wen (2015) provide a good example of a highly structured narrative review in the eHealth field. These authors synthesized published articles that describe the development process of mobile health ( m-health ) interventions for patients’ cancer care self-management. As in most narrative reviews, the scope of the research questions being investigated is broad: (a) how development of these systems are carried out; (b) which methods are used to investigate these systems; and (c) what conclusions can be drawn as a result of the development of these systems. To provide clear answers to these questions, a literature search was conducted on six electronic databases and Google Scholar . The search was performed using several terms and free text words, combining them in an appropriate manner. Four inclusion and three exclusion criteria were utilized during the screening process. Both authors independently reviewed each of the identified articles to determine eligibility and extract study information. A flow diagram shows the number of studies identified, screened, and included or excluded at each stage of study selection. In terms of contributions, this review provides a series of practical recommendations for m-health intervention development.

9.3.2. Descriptive or Mapping Reviews

The primary goal of a descriptive review is to determine the extent to which a body of knowledge in a particular research topic reveals any interpretable pattern or trend with respect to pre-existing propositions, theories, methodologies or findings ( King & He, 2005 ; Paré et al., 2015 ). In contrast with narrative reviews, descriptive reviews follow a systematic and transparent procedure, including searching, screening and classifying studies ( Petersen, Vakkalanka, & Kuzniarz, 2015 ). Indeed, structured search methods are used to form a representative sample of a larger group of published works ( Paré et al., 2015 ). Further, authors of descriptive reviews extract from each study certain characteristics of interest, such as publication year, research methods, data collection techniques, and direction or strength of research outcomes (e.g., positive, negative, or non-significant) in the form of frequency analysis to produce quantitative results ( Sylvester et al., 2013 ). In essence, each study included in a descriptive review is treated as the unit of analysis and the published literature as a whole provides a database from which the authors attempt to identify any interpretable trends or draw overall conclusions about the merits of existing conceptualizations, propositions, methods or findings ( Paré et al., 2015 ). In doing so, a descriptive review may claim that its findings represent the state of the art in a particular domain ( King & He, 2005 ).

In the fields of health sciences and medical informatics, reviews that focus on examining the range, nature and evolution of a topic area are described by Anderson, Allen, Peckham, and Goodwin (2008) as mapping reviews . Like descriptive reviews, the research questions are generic and usually relate to publication patterns and trends. There is no preconceived plan to systematically review all of the literature although this can be done. Instead, researchers often present studies that are representative of most works published in a particular area and they consider a specific time frame to be mapped.

An example of this approach in the eHealth domain is offered by DeShazo, Lavallie, and Wolf (2009). The purpose of this descriptive or mapping review was to characterize publication trends in the medical informatics literature over a 20-year period (1987 to 2006). To achieve this ambitious objective, the authors performed a bibliometric analysis of medical informatics citations indexed in medline using publication trends, journal frequencies, impact factors, Medical Subject Headings (MeSH) term frequencies, and characteristics of citations. Findings revealed that there were over 77,000 medical informatics articles published during the covered period in numerous journals and that the average annual growth rate was 12%. The MeSH term analysis also suggested a strong interdisciplinary trend. Finally, average impact scores increased over time with two notable growth periods. Overall, patterns in research outputs that seem to characterize the historic trends and current components of the field of medical informatics suggest it may be a maturing discipline (DeShazo et al., 2009).

9.3.3. Scoping Reviews

Scoping reviews attempt to provide an initial indication of the potential size and nature of the extant literature on an emergent topic (Arksey & O’Malley, 2005; Daudt, van Mossel, & Scott, 2013 ; Levac, Colquhoun, & O’Brien, 2010). A scoping review may be conducted to examine the extent, range and nature of research activities in a particular area, determine the value of undertaking a full systematic review (discussed next), or identify research gaps in the extant literature ( Paré et al., 2015 ). In line with their main objective, scoping reviews usually conclude with the presentation of a detailed research agenda for future works along with potential implications for both practice and research.

Unlike narrative and descriptive reviews, the whole point of scoping the field is to be as comprehensive as possible, including grey literature (Arksey & O’Malley, 2005). Inclusion and exclusion criteria must be established to help researchers eliminate studies that are not aligned with the research questions. It is also recommended that at least two independent coders review abstracts yielded from the search strategy and then the full articles for study selection ( Daudt et al., 2013 ). The synthesized evidence from content or thematic analysis is relatively easy to present in tabular form (Arksey & O’Malley, 2005; Thomas & Harden, 2008 ).

One of the most highly cited scoping reviews in the eHealth domain was published by Archer, Fevrier-Thomas, Lokker, McKibbon, and Straus (2011) . These authors reviewed the existing literature on personal health record ( phr ) systems including design, functionality, implementation, applications, outcomes, and benefits. Seven databases were searched from 1985 to March 2010. Several search terms relating to phr s were used during this process. Two authors independently screened titles and abstracts to determine inclusion status. A second screen of full-text articles, again by two independent members of the research team, ensured that the studies described phr s. All in all, 130 articles met the criteria and their data were extracted manually into a database. The authors concluded that although there is a large amount of survey, observational, cohort/panel, and anecdotal evidence of phr benefits and satisfaction for patients, more research is needed to evaluate the results of phr implementations. Their in-depth analysis of the literature signalled that there is little solid evidence from randomized controlled trials or other studies through the use of phr s. Hence, they suggested that more research is needed that addresses the current lack of understanding of optimal functionality and usability of these systems, and how they can play a beneficial role in supporting patient self-management ( Archer et al., 2011 ).

9.3.4. Forms of Aggregative Reviews

Healthcare providers, practitioners, and policy-makers are nowadays overwhelmed with large volumes of information, including research-based evidence from numerous clinical trials and evaluation studies, assessing the effectiveness of health information technologies and interventions ( Ammenwerth & de Keizer, 2004 ; Deshazo et al., 2009 ). It is unrealistic to expect that all these disparate actors will have the time, skills, and necessary resources to identify the available evidence in the area of their expertise and consider it when making decisions. Systematic reviews that involve the rigorous application of scientific strategies aimed at limiting subjectivity and bias (i.e., systematic and random errors) can respond to this challenge.

Systematic reviews attempt to aggregate, appraise, and synthesize in a single source all empirical evidence that meet a set of previously specified eligibility criteria in order to answer a clearly formulated and often narrow research question on a particular topic of interest to support evidence-based practice ( Liberati et al., 2009 ). They adhere closely to explicit scientific principles ( Liberati et al., 2009 ) and rigorous methodological guidelines (Higgins & Green, 2008) aimed at reducing random and systematic errors that can lead to deviations from the truth in results or inferences. The use of explicit methods allows systematic reviews to aggregate a large body of research evidence, assess whether effects or relationships are in the same direction and of the same general magnitude, explain possible inconsistencies between study results, and determine the strength of the overall evidence for every outcome of interest based on the quality of included studies and the general consistency among them ( Cook, Mulrow, & Haynes, 1997 ). The main procedures of a systematic review involve:

Formulating a review question and developing a search strategy based on explicit inclusion criteria for the identification of eligible studies (usually described in the context of a detailed review protocol).
Searching for eligible studies using multiple databases and information sources, including grey literature sources, without any language restrictions.
Selecting studies, extracting data, and assessing risk of bias in a duplicate manner using two independent reviewers to avoid random or systematic errors in the process.
Analyzing data using quantitative or qualitative methods.
Presenting results in summary of findings tables.
Interpreting results and drawing conclusions.

Many systematic reviews, but not all, use statistical methods to combine the results of independent studies into a single quantitative estimate or summary effect size. Known as meta-analyses , these reviews use specific data extraction and statistical techniques (e.g., network, frequentist, or Bayesian meta-analyses) to calculate from each study by outcome of interest an effect size along with a confidence interval that reflects the degree of uncertainty behind the point estimate of effect ( Borenstein, Hedges, Higgins, & Rothstein, 2009 ; Deeks, Higgins, & Altman, 2008 ). Subsequently, they use fixed or random-effects analysis models to combine the results of the included studies, assess statistical heterogeneity, and calculate a weighted average of the effect estimates from the different studies, taking into account their sample sizes. The summary effect size is a value that reflects the average magnitude of the intervention effect for a particular outcome of interest or, more generally, the strength of a relationship between two variables across all studies included in the systematic review. By statistically combining data from multiple studies, meta-analyses can create more precise and reliable estimates of intervention effects than those derived from individual studies alone, when these are examined independently as discrete sources of information.

The review by Gurol-Urganci, de Jongh, Vodopivec-Jamsek, Atun, and Car (2013) on the effects of mobile phone messaging reminders for attendance at healthcare appointments is an illustrative example of a high-quality systematic review with meta-analysis. Missed appointments are a major cause of inefficiency in healthcare delivery with substantial monetary costs to health systems. These authors sought to assess whether mobile phone-based appointment reminders delivered through Short Message Service ( sms ) or Multimedia Messaging Service ( mms ) are effective in improving rates of patient attendance and reducing overall costs. To this end, they conducted a comprehensive search on multiple databases using highly sensitive search strategies without language or publication-type restrictions to identify all rct s that are eligible for inclusion. In order to minimize the risk of omitting eligible studies not captured by the original search, they supplemented all electronic searches with manual screening of trial registers and references contained in the included studies. Study selection, data extraction, and risk of bias assessments were performed independently by two coders using standardized methods to ensure consistency and to eliminate potential errors. Findings from eight rct s involving 6,615 participants were pooled into meta-analyses to calculate the magnitude of effects that mobile text message reminders have on the rate of attendance at healthcare appointments compared to no reminders and phone call reminders.

Meta-analyses are regarded as powerful tools for deriving meaningful conclusions. However, there are situations in which it is neither reasonable nor appropriate to pool studies together using meta-analytic methods simply because there is extensive clinical heterogeneity between the included studies or variation in measurement tools, comparisons, or outcomes of interest. In these cases, systematic reviews can use qualitative synthesis methods such as vote counting, content analysis, classification schemes and tabulations, as an alternative approach to narratively synthesize the results of the independent studies included in the review. This form of review is known as qualitative systematic review.

A rigorous example of one such review in the eHealth domain is presented by Mickan, Atherton, Roberts, Heneghan, and Tilson (2014) on the use of handheld computers by healthcare professionals and their impact on access to information and clinical decision-making. In line with the methodological guidelines for systematic reviews, these authors: (a) developed and registered with prospero ( www.crd.york.ac.uk/ prospero / ) an a priori review protocol; (b) conducted comprehensive searches for eligible studies using multiple databases and other supplementary strategies (e.g., forward searches); and (c) subsequently carried out study selection, data extraction, and risk of bias assessments in a duplicate manner to eliminate potential errors in the review process. Heterogeneity between the included studies in terms of reported outcomes and measures precluded the use of meta-analytic methods. To this end, the authors resorted to using narrative analysis and synthesis to describe the effectiveness of handheld computers on accessing information for clinical knowledge, adherence to safety and clinical quality guidelines, and diagnostic decision-making.

In recent years, the number of systematic reviews in the field of health informatics has increased considerably. Systematic reviews with discordant findings can cause great confusion and make it difficult for decision-makers to interpret the review-level evidence ( Moher, 2013 ). Therefore, there is a growing need for appraisal and synthesis of prior systematic reviews to ensure that decision-making is constantly informed by the best available accumulated evidence. Umbrella reviews , also known as overviews of systematic reviews, are tertiary types of evidence synthesis that aim to accomplish this; that is, they aim to compare and contrast findings from multiple systematic reviews and meta-analyses ( Becker & Oxman, 2008 ). Umbrella reviews generally adhere to the same principles and rigorous methodological guidelines used in systematic reviews. However, the unit of analysis in umbrella reviews is the systematic review rather than the primary study ( Becker & Oxman, 2008 ). Unlike systematic reviews that have a narrow focus of inquiry, umbrella reviews focus on broader research topics for which there are several potential interventions ( Smith, Devane, Begley, & Clarke, 2011 ). A recent umbrella review on the effects of home telemonitoring interventions for patients with heart failure critically appraised, compared, and synthesized evidence from 15 systematic reviews to investigate which types of home telemonitoring technologies and forms of interventions are more effective in reducing mortality and hospital admissions ( Kitsiou, Paré, & Jaana, 2015 ).

9.3.5. Realist Reviews

Realist reviews are theory-driven interpretative reviews developed to inform, enhance, or supplement conventional systematic reviews by making sense of heterogeneous evidence about complex interventions applied in diverse contexts in a way that informs policy decision-making ( Greenhalgh, Wong, Westhorp, & Pawson, 2011 ). They originated from criticisms of positivist systematic reviews which centre on their “simplistic” underlying assumptions ( Oates, 2011 ). As explained above, systematic reviews seek to identify causation. Such logic is appropriate for fields like medicine and education where findings of randomized controlled trials can be aggregated to see whether a new treatment or intervention does improve outcomes. However, many argue that it is not possible to establish such direct causal links between interventions and outcomes in fields such as social policy, management, and information systems where for any intervention there is unlikely to be a regular or consistent outcome ( Oates, 2011 ; Pawson, 2006 ; Rousseau, Manning, & Denyer, 2008 ).

To circumvent these limitations, Pawson, Greenhalgh, Harvey, and Walshe (2005) have proposed a new approach for synthesizing knowledge that seeks to unpack the mechanism of how “complex interventions” work in particular contexts. The basic research question — what works? — which is usually associated with systematic reviews changes to: what is it about this intervention that works, for whom, in what circumstances, in what respects and why? Realist reviews have no particular preference for either quantitative or qualitative evidence. As a theory-building approach, a realist review usually starts by articulating likely underlying mechanisms and then scrutinizes available evidence to find out whether and where these mechanisms are applicable ( Shepperd et al., 2009 ). Primary studies found in the extant literature are viewed as case studies which can test and modify the initial theories ( Rousseau et al., 2008 ).

The main objective pursued in the realist review conducted by Otte-Trojel, de Bont, Rundall, and van de Klundert (2014) was to examine how patient portals contribute to health service delivery and patient outcomes. The specific goals were to investigate how outcomes are produced and, most importantly, how variations in outcomes can be explained. The research team started with an exploratory review of background documents and research studies to identify ways in which patient portals may contribute to health service delivery and patient outcomes. The authors identified six main ways which represent “educated guesses” to be tested against the data in the evaluation studies. These studies were identified through a formal and systematic search in four databases between 2003 and 2013. Two members of the research team selected the articles using a pre-established list of inclusion and exclusion criteria and following a two-step procedure. The authors then extracted data from the selected articles and created several tables, one for each outcome category. They organized information to bring forward those mechanisms where patient portals contribute to outcomes and the variation in outcomes across different contexts.

9.3.6. Critical Reviews

Lastly, critical reviews aim to provide a critical evaluation and interpretive analysis of existing literature on a particular topic of interest to reveal strengths, weaknesses, contradictions, controversies, inconsistencies, and/or other important issues with respect to theories, hypotheses, research methods or results ( Baumeister & Leary, 1997 ; Kirkevold, 1997 ). Unlike other review types, critical reviews attempt to take a reflective account of the research that has been done in a particular area of interest, and assess its credibility by using appraisal instruments or critical interpretive methods. In this way, critical reviews attempt to constructively inform other scholars about the weaknesses of prior research and strengthen knowledge development by giving focus and direction to studies for further improvement ( Kirkevold, 1997 ).

Kitsiou, Paré, and Jaana (2013) provide an example of a critical review that assessed the methodological quality of prior systematic reviews of home telemonitoring studies for chronic patients. The authors conducted a comprehensive search on multiple databases to identify eligible reviews and subsequently used a validated instrument to conduct an in-depth quality appraisal. Results indicate that the majority of systematic reviews in this particular area suffer from important methodological flaws and biases that impair their internal validity and limit their usefulness for clinical and decision-making purposes. To this end, they provide a number of recommendations to strengthen knowledge development towards improving the design and execution of future reviews on home telemonitoring.

9.4. Summary

Table 9.1 outlines the main types of literature reviews that were described in the previous sub-sections and summarizes the main characteristics that distinguish one review type from another. It also includes key references to methodological guidelines and useful sources that can be used by eHealth scholars and researchers for planning and developing reviews.

Typology of Literature Reviews (adapted from Paré et al., 2015).

As shown in Table 9.1 , each review type addresses different kinds of research questions or objectives, which subsequently define and dictate the methods and approaches that need to be used to achieve the overarching goal(s) of the review. For example, in the case of narrative reviews, there is greater flexibility in searching and synthesizing articles ( Green et al., 2006 ). Researchers are often relatively free to use a diversity of approaches to search, identify, and select relevant scientific articles, describe their operational characteristics, present how the individual studies fit together, and formulate conclusions. On the other hand, systematic reviews are characterized by their high level of systematicity, rigour, and use of explicit methods, based on an “a priori” review plan that aims to minimize bias in the analysis and synthesis process (Higgins & Green, 2008). Some reviews are exploratory in nature (e.g., scoping/mapping reviews), whereas others may be conducted to discover patterns (e.g., descriptive reviews) or involve a synthesis approach that may include the critical analysis of prior research ( Paré et al., 2015 ). Hence, in order to select the most appropriate type of review, it is critical to know before embarking on a review project, why the research synthesis is conducted and what type of methods are best aligned with the pursued goals.

9.5. Concluding Remarks

In light of the increased use of evidence-based practice and research generating stronger evidence ( Grady et al., 2011 ; Lyden et al., 2013 ), review articles have become essential tools for summarizing, synthesizing, integrating or critically appraising prior knowledge in the eHealth field. As mentioned earlier, when rigorously conducted review articles represent powerful information sources for eHealth scholars and practitioners looking for state-of-the-art evidence. The typology of literature reviews we used herein will allow eHealth researchers, graduate students and practitioners to gain a better understanding of the similarities and differences between review types.

We must stress that this classification scheme does not privilege any specific type of review as being of higher quality than another ( Paré et al., 2015 ). As explained above, each type of review has its own strengths and limitations. Having said that, we realize that the methodological rigour of any review — be it qualitative, quantitative or mixed — is a critical aspect that should be considered seriously by prospective authors. In the present context, the notion of rigour refers to the reliability and validity of the review process described in section 9.2. For one thing, reliability is related to the reproducibility of the review process and steps, which is facilitated by a comprehensive documentation of the literature search process, extraction, coding and analysis performed in the review. Whether the search is comprehensive or not, whether it involves a methodical approach for data extraction and synthesis or not, it is important that the review documents in an explicit and transparent manner the steps and approach that were used in the process of its development. Next, validity characterizes the degree to which the review process was conducted appropriately. It goes beyond documentation and reflects decisions related to the selection of the sources, the search terms used, the period of time covered, the articles selected in the search, and the application of backward and forward searches ( vom Brocke et al., 2009 ). In short, the rigour of any review article is reflected by the explicitness of its methods (i.e., transparency) and the soundness of the approach used. We refer those interested in the concepts of rigour and quality to the work of Templier and Paré (2015) which offers a detailed set of methodological guidelines for conducting and evaluating various types of review articles.

To conclude, our main objective in this chapter was to demystify the various types of literature reviews that are central to the continuous development of the eHealth field. It is our hope that our descriptive account will serve as a valuable source for those conducting, evaluating or using reviews in this important and growing domain.

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Cite this Page Paré G, Kitsiou S. Chapter 9 Methods for Literature Reviews. In: Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27.
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Data Quality–Driven Improvement in Health Care: Systematic Literature Review

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Systematic Literature Review Methodology
Step-by-step description of the systematic review process. Adapted from
Systematic Literature Review Methodology
Steps for systematic literature review (PRISMA method) Source: Scheme
systematic literature review using prisma a step by step guide

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An overview of methodological approaches in systematic reviews
1. INTRODUCTION. Evidence synthesis is a prerequisite for knowledge translation. 1 A well conducted systematic review (SR), often in conjunction with meta‐analyses (MA) when appropriate, is considered the "gold standard" of methods for synthesizing evidence related to a topic of interest. 2 The central strength of an SR is the transparency of the methods used to systematically search ...
Method Article How-to conduct a systematic literature review: A quick
Method details Overview. A Systematic Literature Review (SLR) is a research methodology to collect, identify, and critically analyze the available research studies (e.g., articles, conference proceedings, books, dissertations) through a systematic procedure [12].An SLR updates the reader with current literature about a subject [6].The goal is to review critical points of current knowledge on a ...
Method for conducting systematic literature review and meta-analysis
Method details: the six basic steps Protocol - SLR methodology step 1. The need for a research protocol for SLR is for the consideration of transparency, transferability, and replicability of the work, which are the characteristics that make a literature review systematic [12].This helps to minimize the bias by conducting exhaustive literature searches.
Guidance on Conducting a Systematic Literature Review
Literature reviews establish the foundation of academic inquires. However, in the planning field, we lack rigorous systematic reviews. In this article, through a systematic search on the methodology of literature review, we categorize a typology of literature reviews, discuss steps in conducting a systematic literature review, and provide suggestions on how to enhance rigor in literature ...
Systematic Review
Systematic review vs. literature review. A literature review is a type of review that uses a less systematic and formal approach than a systematic review. Typically, an expert in a topic will qualitatively summarize and evaluate previous work, without using a formal, explicit method.
Ten Steps to Conduct a Systematic Review
Registration can be done on platforms like PROSPERO 5 for health and social care reviews or Cochrane 3 for interventions. Step 3: search. In the process of conducting a systematic review, a well-organized literature search is a pivotal step.
Guidelines for writing a systematic review
Systematic review: The most robust review method, usually with the involvement of more than one author, intends to systematically search for and appraise literature with pre-existing inclusion criteria. (Salem et al., 2023) Rapid review: Utilises Systematic Review methods but may be time limited. (Randles and Finnegan, 2022) Meta-analysis
How to Do a Systematic Review: A Best Practice Guide for Conducting and
The best reviews synthesize studies to draw broad theoretical conclusions about what a literature means, linking theory to evidence and evidence to theory. This guide describes how to plan, conduct, organize, and present a systematic review of quantitative (meta-analysis) or qualitative (narrative review, meta-synthesis) information.
Systematic reviews: Brief overview of methods, limitations, and
CONCLUSION. Siddaway 16 noted that, "The best reviews synthesize studies to draw broad theoretical conclusions about what the literature means, linking theory to evidence and evidence to theory" (p. 747). To that end, high quality systematic reviews are explicit, rigorous, and reproducible. It is these three criteria that should guide authors seeking to write a systematic review or editors ...
How to Do a Systematic Review: A Best Practice Guide for Conducting and
Systematic reviews are characterized by a methodical and replicable methodology and presentation. They involve a comprehensive search to locate all relevant published and unpublished work on a subject; a systematic integration of search results; and a critique of the extent, nature, and quality of evidence in relation to a particular research question. The best reviews synthesize studies to ...
How to write the methods section of a systematic review
Keep it brief. The methods section should be succinct but include all the noteworthy information. This can be a difficult balance to achieve. A useful strategy is to aim for a brief description that signposts the reader to a separate section or sections of supporting information. This could include datasets, a flowchart to show what happened to ...
(PDF) A guide to systematic literature reviews
The research method applied in this study is the Systematic Literature Review (SLR). This systematic approach allows us to investigate and synthesize the latest research findings in primary school ...
Description of the Systematic Literature Review Method
A systematic literature review (SLR) is an independent academic method that aims to identify and evaluate all relevant literature on a topic in order to derive conclusions about the question under consideration. "Systematic reviews are undertaken to clarify the state of existing research and the implications that should be drawn from this."
The ABC of systematic literature review: the basic methodological
There is a need for more methodological-based articles on systematic literature review (SLR) for non-health researchers to address issues related to the lack of methodological references in SLR and less suitability of existing methodological guidance. With that, this study presented a beginner's guide to basic methodological guides and key points to perform SLR, especially for those from non ...
How-to conduct a systematic literature review: A quick guide for
Abstract. Performing a literature review is a critical first step in research to understanding the state-of-the-art and identifying gaps and challenges in the field. A systematic literature review is a method which sets out a series of steps to methodically organize the review. In this paper, we present a guide designed for researchers and in ...
(PDF) Systematic Literature Reviews: An Introduction
Systematic literature reviews (SRs) are a way of synt hesising scientific evidence to answer a particular. research question in a way that is transparent and reproducible, while seeking to include ...
Research Guides: Systematic Reviews: Types of Literature Reviews
Mixed studies review/mixed methods review: Refers to any combination of methods where one significant component is a literature review (usually systematic). Within a review context it refers to a combination of review approaches for example combining quantitative with qualitative research or outcome with process studies:
PDF Conducting a Systematic Review: Methodology and Steps
meta-analysis is necessarily in a systematic review.4The main purpose of this document is to provide guidelines, recommendations and propose a methodology for conducting mixed-method systematic reviews for evidence synthesis for "gender in agricult. re and food systems" for the CGIAR GENDER Platform. In this document we highlight some of ...
Sample Selection in Systematic Literature Reviews of Management
The present methodological literature review (cf. Aguinis et al., 2020) addresses this void and aims to identify the dominant approaches to sample selection and provide insights into essential choices in this step of systematic reviews, with a particular focus on management research.To follow these objectives, I have critically reviewed systematic reviews published in the two most prominent ...
Guidance to best tools and practices for systematic reviews
Methods and guidance to produce a reliable evidence synthesis. Several international consortiums of EBM experts and national health care organizations currently provide detailed guidance (Table (Table1). 1).They draw criteria from the reporting and methodological standards of currently recommended appraisal tools, and regularly review and update their methods to reflect new information and ...
Rapid reviews methods series: Guidance on literature search
This paper is part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group. Rapid reviews (RR) use modified systematic review methods to accelerate the review process while maintaining systematic, transparent and reproducible methods. In this paper, we address considerations for RR searches. We cover the main areas relevant to the search process: preparation and ...
Literature review as a research methodology: An ...
2.1.1. Systematic literature review. What is it and when should we use it? Systematic reviews have foremost been developed within medical science as a way to synthesize research findings in a systematic, transparent, and reproducible way and have been referred to as the gold standard among reviews (Davis et al., 2014).Despite all the advantages of this method, its use has not been overly ...
Systematic Literature Review of the Research Design Based on the Tafsir
A systematic literature review ( SLR ) would provide a more specific picture of related publications. Hence, what elements of the Tahliliy interpretation method ( TIM ) can be applied in a study ...
Applied Sciences
This systematic literature review delves into the extensive landscape of emotion recognition, sentiment analysis, and affective computing, analyzing 609 articles. Exploring the intricate relationships among these research domains, and leveraging data from four well-established sources—IEEE, Science Direct, Springer, and MDPI—this systematic review classifies studies in four modalities ...
Protocol for Systematic Review and Meta-Analysis of Prehospital Large
The literature search will cover CENTRAL, MEDLINE, and Ichushi databases, including studies in English and Japanese. Bias will be assessed using QUADAS-2, and meta-analysis will be conducted using a random effects model, with subgroup and sensitivity analyses to explore heterogeneity. ... Methods: This systematic review and meta-analysis ...
Behind the Intent of Extract Method Refactoring: A Systematic
Hence, researchers and practitioners begin to be aware of the state-of-the-art and identify new research opportunities in this context. <bold>Method:</bold> We review the body of knowledge related to <italic>Extract Method</italic> refactoring in the form of a systematic literature review (SLR).
Chapter 9 Methods for Literature Reviews
9.3. Types of Review Articles and Brief Illustrations. EHealth researchers have at their disposal a number of approaches and methods for making sense out of existing literature, all with the purpose of casting current research findings into historical contexts or explaining contradictions that might exist among a set of primary research studies conducted on a particular topic.
Data Quality-Driven Improvement in Health Care: Systematic Literature
Methods: A systematic literature search of studies in the English language was implemented in the Embase and PubMed databases to select studies that specifically aimed to measure and improve the quality of structured real-world data within any clinical setting. The time frame for the analysis was from January 1945 to June 2023.
Differentiated Instruction in Chinese Primary and Secondary Schools: A
This study comprises a systematic review of the Chinese literature. Forty-five articles, published between 2000 and 2022, were reviewed and the results are presented as a thematic overview. ... Curriculum, Teaching Material, Teaching Method, 37(12), 37-42+36. Google Scholar.