Rapid reviews for health policy and systems decision-making: more important than ever before
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
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.
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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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