Assessment of surgical capacity in Chiapas, Mexico: a cross-sectional study of the public and private sector
Bibliographic record
Abstract
INTRODUCTION: Surgical, anaesthesia and obstetric (SAO) care are essential, life-saving components of universal healthcare. In Chiapas, Mexico's southernmost state, the capacity of SAO care is unknown. This study aims to assess the surgical capacity in Chiapas, Mexico, as it relates to access, infrastructure, service delivery, surgical volume, quality, workforce and financial risk protection. METHODS: A cross-sectional study of Ministry of Health public hospitals and private hospitals in Chiapas was performed. The translated Surgical Assessment Tool (SAT) was implemented in sampled hospitals. Surgical volume was collected retrospectively from hospital logbooks. Fisher's exact test and Mann-Whitney U test were used to compare public and private hospitals. Catastrophic expenditure from surgical care was calculated. RESULTS: Data were collected from 17 public hospitals and 20 private hospitals in Chiapas. Private hospitals were smaller than public hospitals and public hospitals performed more surgeries per operating room. Not all hospitals reported consistent electricity, running water or oxygen, but private hospitals were more likely to have these basic infrastructure components compared with public hospitals (84% vs 95%; 60% vs 100%; 94.1% vs 100%, respectively). Bellwether surgical procedures performed in private hospitals cost significantly more, and posed a higher risk of catastrophic expenditure, than those performed in public hospitals. CONCLUSION: Capacity limitations are greater in public hospitals compared with private hospitals. However, the cost of care in the private sector is significantly higher than the public sector and may result in catastrophic expenditures. Targeted interventions to improve the infrastructure, workforce availability and data collection are needed.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".