Health financing changes in the context of health care decentralization: the case of three Latin American countries
Bibliographic record
Abstract
OBJECTIVE: The results of an evaluative longitudinal study, which identified the effects of health care decentralization on health financing in Mexico, Nicaragua and Peru are presented in this article. METHODS: The methodology had two main phases. In the first, secondary sources of data and documents were analyzed with the following variables: type of decentralization implemented, source of financing, funds for financing, providers, final use of resources, mechanisms for resource allocation. In the second phase, primary data were collected by a survey of key personnel in the health sector. RESULTS: Results of the comparative analysis are presented, showing the changes implemented in the three countries, as well as the strengths and weaknesses of each country in matters of financing and decentralization. CONCLUSIONS: The main financing changes implemented and quantitative trends with respect to the five financing indicators are presented as a methodological tool to implement corrections and adjustments in health financing.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".