Evaluating the Implementation of Mexico's Health Reform: The Case of <i>Seguro Popular</i>
Bibliographic record
Abstract
—In 2012, the Mexican government declared that Seguro Popular had reached the goal of providing health insurance to nearly 53 million individuals previously not enrolled with social security. This major achievement was reached in only nine years of operation of the new system. However, enormous challenges remain to guarantee that Seguro Popular will provide adequate services to the newly enrolled population. This article uses information collected by four external evaluations of Seguro Popular carried out between 2007 and 2012 to analyze how financial resources are transferred from the federal level to the states and how these resources are used to purchase services on behalf of the affiliated population. We focus on three topics: the financial transfer mechanisms, the purchasing of medicines, and the contracting of health workers. The analysis shows that the implementation of Seguro Popular has confronted major challenges due to limited institutional capacity at the federal and state levels, tension in federal–state relations, limited information systems, the influence of political interests, and the use of financial resources for unauthorized expenditures at the state level. Various legal, normative, and technical changes have been introduced during implementation of Seguro Popular to improve performance, with mixed results. Mexico's experiences with the implementation of health reform may offer important lessons for other countries seeking to expand health coverage.
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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 | 0.011 | 0.019 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".