A scoping approach to systematically review published reviews: Adaptations and recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.719 | 0.872 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it