Does the Narrative About the Use of Evidence in Priority Setting Vary Across Health Programs Within the Health Sector: A Case Study of 6 Programs in a Low-Income National Healthcare System
Bibliographic record
Abstract
BACKGROUND: There is a growing body of literature on evidence-informed priority setting. However, the literature on the use of evidence when setting healthcare priorities in low-income countries (LICs), tends to treat the healthcare system (HCS) as a single unit, despite the existence of multiple programs within the HCS, some of which are donor supported. OBJECTIVES: (i) To examine how Ugandan health policy-makers define and attribute value to the different types of evidence; (ii) Based on 6 health programs (HIV, maternal, newborn and child health [MNCH], vaccines, emergencies, health systems, and non- communicable diseases [NCDs]) to discuss the policy-makers' reported access to and use of evidence in priority setting across the 6 health programs in Uganda; and (iii) To identify the challenges related to the access to and use of evidence. METHODS: This was a qualitative study based on in-depth key informant interviews with 60 national level (working in 6 different health programs) and 27 sub-national (district) level policy-makers. Data were analysed used a modified thematic approach. RESULTS: While all respondents recognized and endeavored to use evidence when setting healthcare priorities across the 6 programs and in the districts; more national level respondents tended to value quantitative evidence, while more district level respondents tended to value qualitative evidence from the community. Challenges to the use of evidence included access, quality, and competing values. Respondents from highly politicized and donor supported programs such as vaccines, HIV and maternal neonatal and child health were more likely to report that they had access to, and consistently used evidence in priority setting. CONCLUSION: This study highlighted differences in the perceptions, access to, and use of evidence in priority setting in the different programs within a single HCS. The strong infrastructure in place to support for the access to and use of evidence in the politicized and donor supported programs should be leveraged to support the availability and use of evidence in the relatively under-resourced programs. Further research could explore the impact of unequal availability of evidence on priority setting between health programs within the HCS.
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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 | 0.097 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 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".