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 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".