Scoping reviews: reinforcing and advancing the methodology and application
Bibliographic record
Abstract
Scoping reviews are an increasingly common approach to evidence synthesis with a growing suite of methodological guidance and resources to assist review authors with their planning, conduct and reporting. The latest guidance for scoping reviews includes the JBI methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Extension for Scoping Reviews. This paper provides readers with a brief update regarding ongoing work to enhance and improve the conduct and reporting of scoping reviews as well as information regarding the future steps in scoping review methods development. The purpose of this paper is to provide readers with a concise source of information regarding the difference between scoping reviews and other review types, the reasons for undertaking scoping reviews, and an update on methodological guidance for the conduct and reporting of scoping reviews.Despite available guidance, some publications use the term 'scoping review' without clear consideration of available reporting and methodological tools. Selection of the most appropriate review type for the stated research objectives or questions, standardised use of methodological approaches and terminology in scoping reviews, clarity and consistency of reporting and ensuring that the reporting and presentation of the results clearly addresses the review's objective(s) and question(s) are critical components for improving the rigour of scoping reviews.Rigourous, high-quality scoping reviews should clearly follow up to date methodological guidance and reporting criteria. Stakeholder engagement is one area where further work could occur to enhance integration of consultation with the results of evidence syntheses and to support effective knowledge translation. Scoping review methodology is evolving as a policy and decision-making tool. Ensuring the integrity of scoping reviews by adherence to up-to-date reporting standards is integral to supporting well-informed decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.589 | 0.688 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.044 | 0.044 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.050 | 0.047 |
| Open science | 0.010 | 0.042 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".