Paper 1: Demand-driven rapid reviews for health policy and systems decision-making: lessons from Lebanon, Ethiopia, and South Africa on researchers and policymakers’ experiences
Bibliographic record
Abstract
BACKGROUND: Rapid reviews have emerged as an approach to provide contextualized evidence in a timely and efficient manner. Three rapid review centers were established in Ethiopia, Lebanon, and South Africa through the Alliance for Health Policy and Systems Research, World Health Organization, to stimulate demand, engage policymakers, and produce rapid reviews to support health policy and systems decision-making. This study aimed to assess the experiences of researchers and policymakers engaged in producing and using rapid reviews for health systems strengthening and decisions towards universal health coverage (UHC). METHODS: Using a case study approach with qualitative research methods, experienced researchers conducted semi-structured interviews with respondents from each center (n = 16). The topics covered included the process and experience of establishing the centers, stimulating demand for rapid reviews, collaborating between researchers and policymakers, and disseminating and using rapid reviews for health policies and interventions and the potential for sustaining and institutionalizing the services. Data were analyzed using thematic analysis. RESULTS: Major themes interacted and contributed to shape the experiences of stakeholders of the rapid review centers, including the following: organizational structural arrangements of the centers, management of their processes as input factors, and the rapid reviews as the immediate policy-relevant outputs. The engagement process and the rapid review products contributed to a final theme of impact of the rapid review centers in relation to the uptake of evidence for policy and systems decision-making. CONCLUSIONS: The experiences of policymakers and researchers of the rapid review centers determined the uptake of evidence. The findings of this study can inform policymakers, health system managers, and researchers on best practices for demanding, developing and using rapid reviews to support decision- and policymaking, and implementing the universal healthcare coverage agenda.
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How this classification was reachedexpand
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".