What are the best methodologies for rapid reviews of the research evidence for evidence-informed decision making in health policy and practice: a rapid review
Bibliographic record
Abstract
BACKGROUND: Rapid reviews have the potential to overcome a key barrier to the use of research evidence in decision making, namely that of the lack of timely and relevant research. This rapid review of systematic reviews and primary studies sought to answer the question: What are the best methodologies to enable a rapid review of research evidence for evidence-informed decision making in health policy and practice? METHODS: This rapid review utilised systematic review methods and was conducted according to a pre-defined protocol including clear inclusion criteria (PROSPERO registration: CRD42015015998). A comprehensive search strategy was used, including published and grey literature, written in English, French, Portuguese or Spanish, from 2004 onwards. Eleven databases and two websites were searched. Two review authors independently applied the eligibility criteria. Data extraction was done by one reviewer and checked by a second. The methodological quality of included studies was assessed independently by two reviewers. A narrative summary of the results is presented. RESULTS: Five systematic reviews and one randomised controlled trial (RCT) that investigated methodologies for rapid reviews met the inclusion criteria. None of the systematic reviews were of sufficient quality to allow firm conclusions to be made. Thus, the findings need to be treated with caution. There is no agreed definition of rapid reviews in the literature and no agreed methodology for conducting rapid reviews. While a wide range of 'shortcuts' are used to make rapid reviews faster than a full systematic review, the included studies found little empirical evidence of their impact on the conclusions of either rapid or systematic reviews. There is some evidence from the included RCT (that had a low risk of bias) that rapid reviews may improve clarity and accessibility of research evidence for decision makers. CONCLUSIONS: Greater care needs to be taken in improving the transparency of the methods used in rapid review products. There is no evidence available to suggest that rapid reviews should not be done or that they are misleading in any way. We offer an improved definition of rapid reviews to guide future research as well as clearer guidance for policy and practice.
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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 | 0.622 | 0.850 |
| Meta-epidemiology (narrow) | 0.007 | 0.011 |
| Meta-epidemiology (broad) | 0.029 | 0.028 |
| Bibliometrics | 0.041 | 0.034 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.035 | 0.058 |
| Open science | 0.012 | 0.016 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".