Evidence for informing health policy development in Low-income Countries (LICs): perspectives of policy actors in Uganda
Bibliographic record
Abstract
BACKGROUND: Although there is a general agreement on the benefits of evidence informed health policy development given resource constraints especially in Low-Income Countries (LICs), the definition of what evidence is, and what evidence is suitable to guide decision-making is still unclear. Our study is contributing to filling this knowledge gap. We aimed to explore health policy actors' views regarding what evidence they deemed appropriate to guide health policy development. METHODS: Using exploratory qualitative methods, we conducted interviews with 51 key informants using an in-depth interview guide. We interviewed a diverse group of stakeholders in health policy development and knowledge translation in the Uganda health sector. Data were analyzed using inductive content analysis techniques. RESULTS: Different stakeholders lay emphasis on different kinds of evidence. While donors preferred international evidence and Ministry of Health (MoH) officials looked to local evidence, district health managers preferred local evidence, evidence from routine monitoring and evaluation, and reports from service providers. Service providers on the other hand preferred local evidence and routine monitoring and evaluation reports whilst researchers preferred systematic reviews and clinical trials. Stakeholders preferred evidence covering several aspects impacting on decision-making highlighting the fact that although policy actors look for factual information, they also require evidence on context and implementation feasibility of a policy decision. CONCLUSION: What LICs like Uganda categorize as evidence suitable for informing policy encompasses several types with no consensus on what is deemed as most appropriate. Evidence must be of high quality, applicable, acceptable to the users, and informing different aspects of decision-making.
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.
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 | 0.139 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".