Rapid reviews for health policy and systems decision-making: more important than ever before
Bibliographic record
Abstract
BACKGROUND: Due to the explosion in rapid reviews in the literature during COVID-19, their utility in universal health coverage and in other routine situations, there is now a need to document and further advance the application of rapid review methods, particularly in low-resource settings where a scarcity of resources may preclude the production of a full systematic review. This is the introductory article for a series of articles to further the discussion of rapid reviews for health policy and systems decision-making. MAIN BODY: The series of papers builds on a practical guide on the conduct and reporting of rapid reviews that was published in 2019. The first paper provides an evaluation of a rapid review platform that was implemented in four centers in low-resource settings, the second paper presents approaches to tailor the methods for decision-makers through rapid reviews, the third paper focuses on selecting different types of rapid review products, and the fourth pertains to reporting the results from a rapid review. CONCLUSION: Rapid reviews have a great potential to inform universal health coverage and global health security interventions, moving forward, including preparedness and response plans to future pandemics. This series of articles will be useful for both researchers leading rapid reviews, as well as decision-makers using the results from rapid reviews.
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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 | 0.300 | 0.732 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.033 | 0.039 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.068 | 0.075 |
| Insufficient payload (model declined to judge) | 0.018 | 0.019 |
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".