Embedding rapid reviews in health policy and systems decision-making: Impacts and lessons learned from four low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: Demand for rapid evidence-based syntheses to inform health policy and systems decision-making has increased worldwide, including in low- and middle-income countries (LMICs). To promote use of rapid syntheses in LMICs, the WHO's Alliance for Health Policy and Systems Research (AHPSR) created the Embedding Rapid Reviews in Health Systems Decision-Making (ERA) Initiative. Following a call for proposals, four LMICs were selected (Georgia, India, Malaysia and Zimbabwe) and supported for 1 year to embed rapid response platforms within a public institution with a health policy or systems decision-making mandate. METHODS: While the selected platforms had experience in health policy and systems research and evidence syntheses, platforms were less confident conducting rapid evidence syntheses. A technical assistance centre (TAC) was created from the outset to develop and lead a capacity-strengthening program for rapid syntheses, tailored to the platforms based on their original proposals and needs as assessed in a baseline questionnaire. The program included training in rapid synthesis methods, as well as generating synthesis demand, engaging knowledge users and ensuring knowledge uptake. Modalities included live training webinars, in-country workshops and support through phone, email and an online platform. LMICs provided regular updates on policy-makers' requests and the rapid products provided, as well as barriers, facilitators and impacts. Post-initiative, platforms were surveyed. RESULTS: Platforms provided rapid syntheses across a range of AHPSR themes, and successfully engaged national- and state-level policy-makers. Examples of substantial policy impact were observed, including for COVID-19. Although the post-initiative survey response rate was low, three quarters of those responding felt confident in their ability to conduct a rapid evidence synthesis. Lessons learned coalesced around three themes - the importance of context-specific expertise in conducting reviews, facilitating cross-platform learning, and planning for platform sustainability. CONCLUSIONS: The ERA initiative successfully established rapid response platforms in four LMICs. The short timeframe limited the number of rapid products produced, but there were examples of substantial impact and growing demand. We emphasize that LMICs can and should be involved not only in identifying and articulating needs but as co-designers in their own capacity-strengthening programs. More time is required to assess whether these platforms will be sustained for the long-term.
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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 | 0.152 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".