Contracting non-state providers for universal health coverage: learnings from Africa, Asia, and Eastern Europe
Bibliographic record
Abstract
BACKGROUND: Formal engagement with non-state providers (NSP) is an important strategy in many low-and-middle-income countries for extending coverage of publicly financed health services. The series of country studies reviewed in this paper - from Afghanistan, Bangladesh, Bosnia & Herzegovina, Ghana, South Africa, Tanzania and Uganda - provide a unique opportunity to understand the dynamics of NSP engagement in different contexts. METHODS: A standard template was developed and used to summarize the main findings from the country studies. The summaries were then organized according to emergent themes and a narrative built around these themes. RESULTS: Governments contracted NSPs for a variety of reasons - limited public sector capacity, inability of public sector services to reach certain populations or geographic areas, and the widespread presence of NSPs in the health sector. Underlying these reasons was a recognition that purchasing services from NSPs was necessary to increase coverage of health services. Yet, institutional NSPs faced many service delivery challenges. Like the public sector, institutional NSPs faced challenges in recruiting and retaining health workers, and ensuring service quality. Properly managing relationships between all actors involved was critical to contracting success and the role of NSPs as strategic partners in achieving national health goals. Further, the relationship between the central and lower administrative levels in contract management, as well as government stewardship capacity for monitoring contractual performance were vital for NSP performance. CONCLUSION: For countries with a sizeable NSP sector, making full use of the available human and other resources by contracting NSPs and appropriately managing them, offers an important way for expanding coverage of publicly financed health services and moving towards universal health coverage.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".