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Record W4307780873 · doi:10.1111/wvn.12609

Designing, planning, and conducting systematic reviews and other knowledge syntheses: Six key practical recommendations to improve feasibility and efficiency

2022· article· en· W4307780873 on OpenAlexaff
Guillaume Fontaine, Marc‐André Maheu‐Cadotte, Andréane Lavallée, Tanya Mailhot, Patrick Lavoie, Geneviève Rouleau, Billy Vinette, Pilar Ramirez-Garcìa, Anne Bourbonnais

Bibliographic record

VenueWorldviews on Evidence-Based Nursing · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsHôpital Maisonneuve-RosemontWomen's College HospitalUniversité de MontréalUniversity of OttawaJewish General HospitalCentre Hospitalier de l’Université de MontréalInstitut Universitaire de Gériatrie de MontréalOttawa HospitalMontreal Heart Institute
Fundersnot available
KeywordsSystematic reviewAuditKey (lock)Best practiceComputer scienceKnowledge managementQuality (philosophy)Management scienceProcess managementMEDLINEEngineeringBusinessPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Knowledge syntheses, such as systematic reviews, scoping reviews, and realist reviews, are crucial tools to guide nursing practice, policy, and research. However, conducting high-quality knowledge syntheses is a complex and time-consuming endeavor. It is imperative for nursing students, clinicians, and researchers to be aware of key practical recommendations regarding the conduct of knowledge syntheses to improve the feasibility and efficiency of such projects. AIM: The aim of this paper was to discuss key practical recommendations for designing, planning, and conducting knowledge syntheses relevant to nursing policy, practice, and research. METHODS: The recommendations discussed are based on best-practice guidance about knowledge synthesis methodology proposed by The Campbell Collaboration (Campbell systematic reviews: Policies and guidelines, 2020), Cochrane (Cochrane training, 2019), and the Joanna Briggs Institute (The Joanna Briggs Institute reviewers' manual, 2020) and on strategies used by the authors to improve the feasibility and efficiency of knowledge syntheses. RESULTS: This paper highlights six key practical recommendations that nursing students, clinicians, and researchers should take into account when deciding to embark on a knowledge synthesis project: (1) determining if (and why) knowledge synthesis should be conducted; (2) selecting the appropriate type of knowledge synthesis, as well as the associated methodological guidance and reporting standards; (3) developing a search strategy that balances sensitivity and specificity; (4) writing a protocol and obtaining feedback; (5) determining the resources required to conduct the different stages of the knowledge synthesis; and (6) keeping an audit trail. Fifteen common types of knowledge synthesis are presented with their definitions, relevant methodological guidance, and reporting standards. LINKING EVIDENCE TO ACTION: The recommendations discussed, used in conjunction with appropriate methodological guidelines, may help ensure the success of a knowledge synthesis project by providing best-practice and experience-based guidance to newcomers in the field.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.583
GPT teacher head0.571
Teacher spread0.012 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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