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
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
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.
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 | 0.161 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it