Contextual challenges and impacts on the surgical ecosystem in Chiapas, Mexico: A qualitative study
Bibliographic record
Abstract
Chiapas is a state in southern Mexico that faces significant challenges in healthcare delivery. Strengthening the surgical system requires a comprehensive understanding of all health system domains and the contextual factors that influence care delivery. This study used qualitative methods to identify factors related to both gaps and successes in surgical care in Chiapas, Mexico. Semi-structured interviews were conducted with 23 participants at 15 public and private hospitals. Participants consisted of nurses, physicians, surgeons, and hospital administrators. Interviews were transcribed, and a codebook was developed and applied to all interviews. Recurring themes were identified and described using thematic analysis. Four themes characterizing the challenging context through which care is delivered were identified: referral system challenges, workforce shortages, insufficiencies in perioperative and nonoperative care, and waste and mismanagement of resources. Three themes related to innovations and workarounds were identified: efforts to maximize resources and reduce waste, strategies to reduce language barriers, and planning to account for clinical needs in situations of limited access and emergencies. Gaps and challenges within the surgical system of Chiapas lead to challenges in care delivery across all domains of the health system. However, several solutions have emerged among local providers. Insight into these factors can be used in planning efforts to improve access to safe and effective surgical care.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".