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Record W4206825244 · doi:10.1080/1750984x.2021.1964095

Scoping reviews and rapid reviews

2022· article· en· W4206825244 on OpenAlexafffund
Catherine M. Sabiston, Madison F. Vani, Melissa de Jonge, Amy Nesbitt

Bibliographic record

VenueInternational Review of Sport and Exercise Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaCanada Research Chairs
KeywordsSystematic reviewValue (mathematics)Sport psychologyPsychologyEngineering ethicsDependabilityManagement scienceData scienceKnowledge managementApplied psychologyComputer scienceMEDLINEEngineeringPolitical science

Abstract

fetched live from OpenAlex

Scoping reviews and rapid reviews are intentional approaches to systematically synthesize research. In sport and exercise psychology, scoping reviews can summarize information to describe what is known on a relevant topic. Rapid reviews offer accelerated knowledge synthesis through a streamlined timely and cost-effective approach that is directed and guided by stakeholders. Both scoping and rapid reviews are used to synthesize the literature, and also to describe and assess conceptual, theoretical, and methodological trends, identify gaps in research and practice, and inform future sport and exercise psychology research and practice directions. We offer evidence-based and field-specific guidelines to conduct scoping reviews and rapid reviews. These types of syntheses in sport and exercise psychology are important for advancing research and practice and highlight the value of collaborations with key stakeholders. Our guidelines will help with the uniformity and dependability of scoping and rapid reviews while also advancing the impact and value of this type of research.

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)0.292
metaresearch head score (Gemma)0.613
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.708
Threshold uncertainty score0.874

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2920.613
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0140.011
Bibliometrics0.0590.046
Science and technology studies0.0050.007
Scholarly communication0.0180.017
Open science0.0090.014
Research integrity0.0140.010
Insufficient payload (model declined to judge)0.0810.041

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 head0.047
GPT teacher head0.400
Teacher spread0.353 · 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 designTheoretical or conceptual
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

Citations76
Published2022
Admission routes2
Has abstractyes

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