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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".