Contracting-out urban primary health care in Bangladesh: a qualitative exploration of implementation processes and experience
Bibliographic record
Abstract
BACKGROUND: Contracting-out (CO) to non-state providers is used widely to increase access to health care, but it entails many implementation challenges. Using Bangladesh's two decades of experience with contracting out Urban Primary Health Care (UPHC), this paper identifies contextual, contractual, and actor-related factors that require consideration when implementing CO in Low- and Middle- Income Countries. METHODS: This qualitative case-study is based on 42 in-depth interviews with past and present stakeholders working with the government and the UPHC project, as well as a desk review of key project documents. The Health Policy Triangle framework is utilized to differentiate among multiple intersecting contextual, contractual and actor-related factors that characterize and influence complex implementation processes. RESULTS: In Bangladesh, the contextual factors, both intrinsic and extrinsic to the health system, deeply impacted the CO process. These included competition with other health projects, public sector reforms, and the broader national level political and bureaucratic environment. Providing free services to the poor and a target to recover cost were two contradictory conditions set out in the contract and were difficult for providers to achieve. In relation to actors, the choice of the executing body led to complications, functionally disempowering local government institutions (cities and municipalities) from managing CO processes, and discouraging integration of CO arrangements into the broader national health system. Politics and power dynamics undermined the ethical selection of project areas. Ultimately, these and other factors weakened the project's ability to achieve one of its original objectives: to decentralize management responsibilities and develop municipal capacity in managing contracts. CONCLUSIONS: This study calls attention to factors that need to be addressed to successfully implement CO projects, both in Bangladesh and similar countries. Country ownership is crucial for adapting and integrating CO in national health systems. Concurrent processes must be ensured to develop local CO capacity. CO modalities must be adaptable and responsive to changing context, while operating within an agreed-upon and appropriate legal framework with a strong ethical foundation.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".