A scoping approach to systematically review published reviews: Adaptations and recommendations
Bibliographic record
Abstract
Knowledge translation is a central focus of the health research community, which includes strategies to synthesize published research to support uptake within health care practice and policy arenas. Within the literature concerning review methodologies, a new discussion has emerged concerning methods that review and synthesize published review articles. In this paper, our multidisciplinary team from family medicine, nursing, dental hygiene, kinesiology, occupational therapy, physiology, population health, clinical psychology, and library sciences contributes to this discussion by sharing our experiences in conducting 3 scoping reviews of published review studies. A brief discussion of Cochrane Collaboration overview reviews and Joanna Briggs Institute umbrella reviews foreshadows a discussion of insights from our experiences of conducting the 3 scoping reviews of published reviews. We address 6 adaptations along with our recommendations for each, which may guide other researchers with designing scoping review approaches to synthesize published reviews. The ability of researchers to publish research findings is growing, and our ability to effectively transfer findings into useful evidence for health care practice and policy is imperative to our work.
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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 | 0.720 | 0.837 |
| Meta-epidemiology (narrow) | 0.008 | 0.010 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.071 | 0.075 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.016 | 0.024 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".