Study protocol for a scoping review on rehabilitation scoping reviews
Bibliographic record
Abstract
INTRODUCTION: Scoping reviews are increasingly popular in rehabilitation. However, significant variability in scoping review conduct and reporting currently exists, limiting potential for the methodology to advance rehabilitation research, practice and policy. Our aim is to conduct a scoping review of rehabilitation scoping reviews in order to examine the current volume, yearly distribution, proportion, scope and methodological practices involved in the conduct of scoping reviews in rehabilitation. Key areas of methodological improvement will be described. Methods and analysis: We will undertake the review using the Arksey and O'Malley scoping review methodology. Our search will involve two phases. The first will combine a previously conducted scoping review of scoping reviews (not distinct to rehabilitation, with data current to July 2014) together with a rehabilitation keyword search in PubMed. Articles found in the first phase search will undergo a full text review. The second phase will include an update of the previously conducted scoping review of scoping reviews (July 2014 to current). This update will include the search of nine electronic databases, followed by title and abstract screening as well as a full text review. All screening and extraction will be performed independently by two authors. Articles will be included if they are scoping reviews within the field of rehabilitation. A consultation exercise with key targets will inform plans to improve rehabilitation scoping reviews. Ethics and dissemination: Ethics will be required for the consultation phase of our scoping review. Dissemination will include peer-reviewed publication and conferences in rehabilitation-specific contexts.
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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 | 0.135 | 0.205 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.229 | 0.066 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".