Contracting-out primary health care services in Tanzania towards UHC: how policy processes and context influence policy design and implementation
Bibliographic record
Abstract
BACKGROUND: Governments increasingly recognize the need to engage non-state providers (NSPs) in health systems in order to move successfully towards Universal Health Coverage (UHC). One common approach to engaging NSPs is to contract-out the delivery of primary health care services. Research on contracting arrangements has typically focused on their impact on health service delivery; less is known about the actual processes underlying the development and implementation of interventions and the contextual factors that influence these. This paper reports on the design and implementation of service agreements (SAs) between local governments and NSPs for the provision of primary health care services in Tanzania. It examines the actors, policy process, context and policy content that influenced how the SAs were designed and implemented. METHODS: We used qualitative analytical methods to study the Tanzanian experience with contracting- out. Data were drawn from document reviews and in-depth interviews with 39 key informants, including six interviews at the national and regional levels and 33 interviews at the district level. All interviews were audiotaped, transcribed and translated into English. Data were managed in NVivo (version 10.0) and analyzed thematically. RESULTS: The institutional frameworks shaping the engagement of the government with NSPs are rooted in Tanzania's long history of public-private partnerships in the health sector. Demand for contractual arrangements emerged from both the government and the faith-based organizations that manage NSP facilities. Development partners provided significant technical and financial support, signaling their approval of the approach. Although districts gained the mandate and power to make contractual agreements with NSPs, financing the contracts remained largely dependent on donor funds via central government budget support. Delays in reimbursements, limited financial and technical capacity of local government authorities and lack of trust between the government and private partners affected the implementation of the contractual arrangements. CONCLUSIONS: Tanzania's central government needs to further develop the technical and financial capacity necessary to better support districts in establishing and financing contractual agreements with NSPs for primary health care services. Furthermore, forums for continuous dialogue between the government and contracted NSPs should be fostered in order to clarify the expectations of all parties and resolve any misunderstandings.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".