Evidence on equity, governance and financing after health care reform in Mexico: lessons for Latin American countries
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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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".