Strategies for sustainability and equity of prepayment health schemes\nin Uganda
Bibliographic record
Abstract
Background: Despite the long existence of community health insurance schemes (CHI) in Uganda, their numbers and coverage levels have remained small with limited accessibility by the poor.Objectives: To examine issues of equity and sustainability in CHI schemes, which are prerequisites to health sector financing.Methods: We carried out a descriptive cross-sectional study employing qualitative techniques.Eight focus group discussions (FGDs) with CHI scheme members and seven FGDs with non-members were held.Twelve Key informant interviews (KIs) were held with scheme managers, officials from Ministry of Health and one health financing organisation.We reviewed relevant documents and records of schemes.Results: Respondents' perceptions of unfairness in schemes were: non-members were treated better in hospital than members; some members pay premiums continuously without falling sick and schemes refused to cover illnesses like diabetes and hypertension.Fairness was related with the very little payment for the services received, members paying less than non-members but both getting the same treatment and no patient discrimination based on gender, age or social status.Schemes are not sustainable because they operate on small budgets, have low enrolment and lack government support.Effect of abolition of user fees on scheme enrolment was minimal.. Conclusion Government should ensure that quality of health care does not deteriorate in the context of increased utilisation after user fees removal, schemes need substantial support to build their sustainability and there is need for technical and policy considerations about whether or not CHI has a role to play in Ugandan health system.
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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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".