Evidence on equity, governance and financing after health care reform in Mexico: lessons for Latin American countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article includes evidence on equity, governance and health financing outcomes of the Mexican health system. An evaluative research with a cross-sectional design was oriented towards the qualitative and quantitative analysis of financing, governance and equity indicators. Taking into account feasibility, as well as political and technical criteria, seven Mexican states were selected as study populations and an evaluative research was conducted during 2002-2010. The data collection techniques were based on in-depth interviews with key personnel (providers, users and community leaders), consensus technique and document analysis. The qualitative analysis was done with ATLAS TI and POLICY MAKER softwares. The Mexican health system reform has modified dependence at the central level; there is a new equity equation for resources allocation, community leaders and users of services reported the need to improve an effective accountability system at both municipal and state levels. Strategies for equity, governance and financing do not have adequate mechanisms to promote participation from all social actors. Improving this situation is a very important goal in the Mexican health democratization process, in the context of health care reform. Inequality on resources allocation in some regions and catastrophic expenditure for users is unequal in all states, producing more negative effects on states with high social marginalization. Special emphasis is placed on the analysis of the main strengths and weaknesses, as relevant evidences for other Latin American countries which are designing, implementing and evaluating reform strategies in order to achieve equity, good governance and a greater financial protection in health.
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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 | 0.001 | 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 | 0.000 | 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 it