Feasibility of a rapid response mechanism to meet policymakers' urgent needs for research evidence about health systems in a low income country: a case study
Bibliographic record
Abstract
OBJECTIVES: Despite the recognition of the importance of evidence-informed health policy and practice, there are still barriers to translating research findings into policy and practice. The present study aimed to establish the feasibility of a rapid response mechanism, a knowledge translation strategy designed to meet policymakers' urgent needs for evidence about health systems in a low income country, Uganda. Rapid response mechanisms aim to address the barriers of timeliness and relevance of evidence at the time it is needed. METHODS: A rapid response mechanism (service) designed a priori was offered to policymakers in the health sector in Uganda. In the form of a case study, data were collected about the profile of users of the service, the kinds of requests for evidence, changes in answers, and courses of action influenced by the mechanism and their satisfaction with responses and the mechanism in general. RESULTS: We found that in the first 28 months, the service received 65 requests for evidence from 30 policymakers and stakeholders, the majority of whom were from the Ministry of Health. The most common requests for evidence were about governance and organization of health systems. It was noted that regular contact between the policymakers and the researchers at the response service was an important factor in response to, and uptake of the service. The service seemed to increase confidence for policymakers involved in the policymaking process. CONCLUSION: Rapid response mechanisms designed to meet policymakers' urgent needs for research evidence about health systems are feasible and acceptable to policymakers in low income countries.
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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 | 0.155 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".