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Record W2994851128 · doi:10.12820/rbafs.24e0082

Scoping review: a relevant methodological approach for knowledge synthesis in Brazil’s health literature

2019· article· en· W2994851128 on OpenAlexaff
Valter Cordeiro Barbosa Filho, Andrea C. Tricco

Bibliographic record

VenueRevista Brasileira de Atividade Física & Saúde · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealth Education and Validation
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsSystematic reviewKnowledge translationOrder (exchange)Management sciencePsychologyKnowledge managementEngineering ethicsMEDLINEComputer sciencePolitical scienceEngineeringBusiness

Abstract

fetched live from OpenAlex

A methodologically robust approach to synthesize relevant knowledge in health literature is the scoping review, which is used to answer broader questions (e.g., “What is known about this concept?”) and can be used to map evidence for research and practice decision-making. This paper discussed the importance of scoping reviews as a methodological approach for knowledge synthesis in Brazil’s health literature. Definitions and methodological steps were discussed. We examined 45 scoping reviews that were published in Brazil’s journals or available as thesis or dissertations to discuss their content and methodological characteristics. Recommendations for authors were presented in order to improve the planning, executing and reporting of further scoping reviews in Brazil. This will help Brazilian researchers and health professionals to understand when and how scoping reviews can be helpful for knowledge synthesis on health topics, including for physical activity and health research area.

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)0.432
metaresearch head score (Gemma)0.540
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.568
Threshold uncertainty score0.701

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4320.540
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0480.049
Science and technology studies0.0090.009
Scholarly communication0.0220.013
Open science0.0040.016
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0070.002

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 head0.198
GPT teacher head0.499
Teacher spread0.301 · 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
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

Citations20
Published2019
Admission routes1
Has abstractyes

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