Criteria for Priority-Setting in Health Care in Uganda: Exploration of Stakeholders' Values
Bibliographic record
Abstract
OBJECTIVE: To explore stakeholders' acceptance of criteria for setting priorities for the health care system in Uganda. METHODS: A self-administered questionnaire was used. It was distributed to health workers, planners and administrators working in all levels of the Ugandan health care system. It was also distributed to members of the public. Participants were asked how strongly they agreed or disagreed with 18 criteria that could be used to set priorities for allocating health care. A total of 408 people took part. Data were entered and analysed using SPSS statistical software. Predetermined cut-off points were used to rank the criteria into three different categories: high weight (>66% of respondents agreed), average weight (33-66% of respondents agreed) and low weight (<33% of respondents agreed). We also tested for associations between respondents' characteristics and their degree of agreement with the criteria. FINDINGS: High-weight criteria included severity of disease, benefit of the intervention, cost of the intervention, cost-effectiveness of the intervention, quality of the data on effectiveness, the patients age, place of residence, lifestyle, importance of providing equity of access to health care and the community's views. The average-weight criteria included the patient's social status, mental features, physical capabilities, political views, responsibilities for others and gender. Low-weight criteria included the patient's religion, and power and influence. There were few associations between respondents' characteristics and their preferences. CONCLUSION: There was a high degree of acceptance for commonly used disease-related and society-related criteria. There was less agreement about the patient-related criteria. We propose that average-weight criteria should be debated in Uganda and other countries facing the challenge of distributing scarce health care resources.
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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 | 0.078 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".