Lags in the provision of obstetric services to indigenous women and their implications for universal access to health care in Mexico
Bibliographic record
Abstract
Through quantitative and qualitative methods, in this article the authors describe the perspectives of indigenous women who received antenatal and childbirth medical care within a care model that incorporates a non-governmental organisation (NGO), Partners in Health. They discuss whether the NGO model better resolves the care-seeking process, including access to health care, compared with a standard model of care in government-subsidised health care units (setting of health services networks). Universal health coverage advocates access for the most disadvantaged and vulnerable populations as a priority. However, the issue of access includes problems related to the effect of certain structural social determinants that limit different aspects of the obstetric care process. The findings of this study show the need to modify the structure of organisational values in order to place users at the centre of medical care and ensure respect for their rights. The participation of agents outside the public system, such as NGOs, can be of great value for moving in this direction. Women's participation is also necessary for learning how they are being cared for and the extent to which they are satisfied with obstetric services. This research experience can be used for other countries with similar conditions.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".