Rethinking referral systems in rural chiapas: A mixed methods study
Bibliographic record
Abstract
Background: Despite the assurance of universal health coverage, large disparities exist in access to surgery in the state of Chiapas. The purpose of this study was to determine the effectiveness of the surgical referral system at hospitals operated by the Ministry of Health in Chiapas. Methods: 13 variables were extracted from surgical referrals data from three public hospitals in Chiapas over a three-year period. Interviews were performed of health care workers involved in the referral system and surgical patients. The quantitative and qualitative data was analyzed convergently and reported using a narrative approach. Findings: In total, only 47.4% of referred patients requiring surgery received an operation. Requiring an elective, gynecological, or orthopedic surgery and each additional surgery cancellation were significantly associated with lower rates of receiving surgery. The impact of gender and surgical specialty, economic fragility of farmers, dependence upon economic resources to access care, pain leading people to seek care, and futility leading patients to abandon the public system were identified as main themes from the mixed methods analysis. Interpretation: Surgical referral patients in Chiapas struggle to navigate an inefficient and expensive system, leading to delayed care and forcing many patients to turn to the private health system. These mixed methods findings provide a detailed view of often overlooked limitations to universal health coverage in Chiapas. Moving forward, this knowledge must be applied to improve referral system coordination and provide hospitals with the necessary workforce, equipment, and protocols to ensure access to guaranteed care. Funding: Harvard University and the Abundance Fund provided funding for this project. Funding sources had no role in the writing of the manuscript or decision to submit it for publication.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".