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Record W3128074178 · doi:10.1111/jan.14743

Undertaking a scoping review: A practical guide for nursing and midwifery students, clinicians, researchers, and academics

2021· review· en· W3128074178 on OpenAlexaff
Danielle Pollock, Ellen Davies, Micah D.J. Peters, Andrea C. Tricco, Lyndsay Alexander, Patricia McInerney, Christina Godfrey, Hanan Khalil, Zachary Munn

Bibliographic record

VenueJournal of Advanced Nursing · 2021
Typereview
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsQueen's UniversityKingston Health Sciences CentrePublic Health OntarioUniversity of TorontoCentre for Excellence in Mining InnovationSt. Michael's Hospital
Fundersnot available
KeywordsRigourSystematic reviewPopularityTransparency (behavior)Evidence-based practiceNursing researchVariety (cybernetics)Protocol (science)NursingMEDLINEBest practiceMedicineMedical educationEngineering ethicsPsychologyAlternative medicineComputer sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

AIM: The aim of this study is to discuss the available methodological resources and best-practice guidelines for the development and completion of scoping reviews relevant to nursing and midwifery policy, practice, and research. DESIGN: Discussion Paper. DATA SOURCES: Scoping reviews that exemplify best practice are explored with reference to the recently updated JBI scoping review guide (2020) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Scoping Review extension (PRISMA-ScR). IMPLICATIONS FOR NURSING AND MIDWIFERY: Scoping reviews are an increasingly common form of evidence synthesis. They are used to address broad research questions and to map evidence from a variety of sources. Scoping reviews are a useful form of evidence synthesis for those in nursing and midwifery and present opportunities for researchers to review a broad array of evidence and resources. However, scoping reviews still need to be conducted with rigour and transparency. CONCLUSION: This study provides guidance and advice for researchers and clinicians who are preparing to undertake an evidence synthesis and are considering a scoping review methodology in the field of nursing and midwifery. IMPACT: With the increasing popularity of scoping reviews, criticism of the rigour, transparency, and appropriateness of the methodology have been raised across multiple academic and clinical disciplines, including nursing and midwifery. This discussion paper provides a unique contribution by discussing each component of a scoping review, including: developing research questions and objectives; protocol development; developing eligibility criteria and the planned search approach; searching and selecting the evidence; extracting and analysing evidence; presenting results; and summarizing the evidence specifically for the fields of nursing and midwifery. Considerations for when to select this methodology and how to prepare a review for publication are also discussed. This approach is applied to the disciplines of nursing and midwifery to assist nursing and/or midwifery students, clinicians, researchers, and academics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.309
metaresearch head score (Gemma)0.440
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.691
Threshold uncertainty score0.853

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3090.440
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0260.026
Science and technology studies0.0070.010
Scholarly communication0.0180.022
Open science0.0080.018
Research integrity0.0140.024
Insufficient payload (model declined to judge)0.0280.038

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 head0.633
GPT teacher head0.740
Teacher spread0.107 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations719
Published2021
Admission routes1
Has abstractyes

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