Health Care Quality Improvement in Mexico: Challenges, Opportunities, and Progress
Bibliographic record
Abstract
The health care quality improvement effort is international: all nations seek to apply new knowledge and new technology for the health of their populations. However, the environment within which this effort takes place differs remarkably. In Mexico, for example, total expenditure on health care is only 5.6% of the gross national product—compared with about 15% in the USA, 11% or 12% in Canada, and an average of 6.1% in the Latin American countries. Further, 52% of Mexican health care expenditures are out of pocket in a country where poverty is prevalent and many people postpone care. Structurally, the health care system in Mexico is public and private. Mexico has 4000 hospitals. The 1000 public hospitals have 75% of the beds; 90% of the 3000 private hospitals have ≤20 beds, often as few as ≤5 beds. In fact, some “private hospitals” can hardly be considered hospitals at all, since they have no laboratories, radiography equipment, or even nurses. The system also includes 20,000 primary care facilities. As soon as President Vicente Fox began his administration in December 2000, government leaders began working on a national strategy for improving health care. In this article, I discuss the health care challenges, the objectives of this particular strategy, and the progress made to date.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".