Equity, governance and financing after health care reform: lessons from Mexico
Bibliographic record
Abstract
To determine, from the perspective of providers, community leaders and users of health services, equity, governance and health financing outcomes of the Mexican health system reform.Cross-sectional study oriented towards the qualitative analysis of financing, governance and equity indicators for the uninsured population. Taking into account feasibility, as well as political and technical criteria, six Mexican states were selected as study populations and a qualitative research was conducted during 2004-2006. Two hundred and forty in-depth interviews were applied, in all selected states, to 60 decision-makers, including medical and administrative personnel; 60 service providers at health centres; 60 representatives of civil organizations, including municipal representatives and, finally, 60 members of health committees and users of services at second and first levels of care units. The analysis of interviews was performed using ATLAS-Ti software. An outcome mapping of health reform was developed. For political actors, Mexican health system reform has not modified dependence on the central level; ignorance about reform strategies and lack of participation in the search for financial resources to finance health systems were evidenced. Also, in all states under study, community leaders and users of services reported the need to improve an effective accountability system at both municipal and state levels. Health 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. There are relevant positive and negative effects of the reform on equity, governance and financing in health. Special emphasis is placed on the analysis of lessons learned in Mexico and the usefulness of the main strengths and weaknesses, as relevant evidences for other middle-income countries which are designing, implementing and evaluating reform strategies in order to achieve equity in resource allocation, good levels of 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".