Decentralisation of health care and its impact on health outcomes
Bibliographic record
Abstract
This paper explores the impact of health care decentralisation on a characteristic of human development: the overall level of a population's health. While much of the literature on decentralisation in health care has stressed the advantages of sub national provision of health services, in the absence of a quantitative measure of the magnitude of the effect of decentralisation, there is little that can be said in terms of its benefits and costs for the health sector. The purpose of this study is therefore to contribute to the limited empirical literature on this issue by investigating the hypothesis that shifts towards more decentralisation would be accompanied by improvements in population health. The analysis draws on a theoretical model of local government's public finance applied to health. We use the ten provinces of Canada as a case study. Apart from being one of the most decentralised countries in the world, Canadian data required to estimate our model was found to be one of the best. The results of the empirical analysis suggest that decentralisation in Canada has had a positive and substantial influence on the effectiveness of public policy in improving population's health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".