Why district assemblies disburse resources to district health systems for service delivery at district level in the context of decentralization: a comparative study of two districts in the Volta Region of Ghana
Bibliographic record
Abstract
Objectives: To explore why the District Assembly disburses financial and other resources to the District Health System. Design: Multiple case study with a single unit of analysis (holistic) using quantitative and qualitative methods of data collection involving a desk review, analysis of routine health management information system data and key informant interviews. Setting: Two districts in the Volta Region of Ghana. Participants: Twelve key officials of each district assembly and the district health system (24 total) who had worked in the district at least a year or more. Interventions: None. Results: Both District Assemblies had moderate decision space which was influenced by their capacity, power and contextual factors like politics, economics, legal and situational factors. Disbursement of financial and other resources to the District Health Systems was influenced by financial capacity, use of power by stakeholders, context and the decision space of the District Assembly. Political actors appeared to have more power in resource disbursement decision making than community members and technocrats in a context of resource constraints and inadequate funding. The funding available was used predominantly for capital investments, mainly construction of Community Based Health Planning and Services (CHPS) compounds. Conclusion: It is important to make policies that will regulate the relative power among the political appointees like the District Chief Executives (DCEs), public and civil servants in decentralized departments and agencies and Community members to make resource disbursement more sensitive to communities and decentralized departments.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".