Decentralization and Health Resource Allocation: A Case Study at the District Level in Indonesia
Bibliographic record
Abstract
Health resource allocation has been an issue of political debate in many health systems. However, the debate has tended to concentrate on vertical allocation from the national to regional level. Allocation within regions or institutions has been largely ignored. This study was conducted to contribute analysis to this gap. The objective was to investigate health resource allocation within District Health Offices (DHOs) and to compare the trends and patterns of several budget categories before and after decentralization. The study was conducted in three districts in the Province of Nanggroe Aceh Darussalam. Six fiscal year budgets, two before decentralization and four after, were studied. Data was collected from the Local Government Planning Office and DHOs. Results indicated that in the first year of implementing a decentralization policy, the local government budget rose sharply, particularly in the wealthiest district. In contrast, in relatively poor districts the budget was only boosted slightly. Increasing total local government budgets had a positive impact on increasing the health budget. The absolute amount of health budgets increased significantly, but by percentage did not change very much. Budgets for several projects and budget items increased significantly, but others, such as health promotion, monitoring and evaluation, and public-goods-related activities, decreased. This study concluded that decentralization in Indonesia had made a positive impact on district government fiscal capacity and had affected DHO budgets positively. However, an imbalanced budget allocation between projects and budget items was obvious, and this needs serious attention from policy makers. Otherwise, decentralization will not significantly improve the health system in Indonesia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".