Access to Essential Surgical Care in Chiapas, Mexico: A System‐Wide Geospatial Analysis
Bibliographic record
Abstract
BACKGROUND: Long travel times to reach essential surgical care in Chiapas, Mexico's poorest state, can delay lifesaving procedures and contribute to adverse outcomes. Geographical access to surgical facilities is 1 of the 6 indicators of the Lancet Commission on Global Surgery and has been measured extensively worldwide. Our objective is to determine the population with 2-h geographical access to facilities capable of performing the Bellwether procedures (laparotomy, cesarean delivery, and open fracture repair). This is the first study in Mexico to assess access to surgical facilities, including both the fragmented public sector and the private sector. METHODS: In this cross-sectional study, conducted from June 2019 to January 2020, Bellwether capable surgical facilities from all health systems in Chiapas were geocoded and assessed through on-site data collection, Ministry of Health databases, and verified via telephone. Geospatial analyses were performed on Redivis. RESULTS: We identified 59 Bellwether capable hospitals, with 17.5% (n = 954,460) of the state residing more than 2 h from surgical care in public and private health systems. Of those, 22 facilities had confirmed 24/7 Bellwether capability, and 23% (n = 1,178,383) of the affiliated population resided more than 2 h from these hospitals. CONCLUSIONS: Our study shows that the Ministry of Health and employment-based health coverage could provide timely access to essential surgical care for the majority of the population. However, the fragmentation of the healthcare system leaves a gap that contributes to delays in care and unmet emergency surgical needs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".