Accessing medical care for infertility: a study of women in Mexico
Bibliographic record
Abstract
Objective: To investigate barriers in accessing care for infertility in Mexico, because little is known about this issue for low and middle-income countries, which comprise 80% of the world's population. Design: Cross-sectional analysis. Setting: Mexcian Teachers' Cohort. Patients: A total of 115,315 female public school teachers from 12 states in Mexico. Interventions: None. Main Outcome Measures: The participants were asked detailed questions about their demographics, lifestyle characteristics, access to the health care system, and infertility history via a self-reported questionnaire. Log-binomial models, adjusted a priori for potential confounding factors, were used to estimate the prevalence ratios (PRs) and 95% confidence intervals ( CIs) of accessing medical care for infertility among women reporting a history of infertility. Results: A total of 19,580 (17%) participants reported a history of infertility. Of those who experienced infertility, 12,470 (63.7%) reported seeking medical care for infertility, among whom 8,467 (67.9%) reported undergoing fertility treatments. Among women who reported a history of infertility, women who taught in a rural school (PR, 0.95; 95% CI, 0.92-0.97), spoke an indigenous language (PR, 0.88; 95% CI, 0.84-0.92), or had less than a university degree (PR, 0.93; 95% CI, 0.90-0.97) were less likely to access medical care for fertility. Women who had ever had a mammogram (PR, 1.07; 95% CI, 1.05-1.10), had a pap smear in the past year (PR, 1.08; 95% CI, 1.06-1.10), or who had used private health care regularly or in times of illness were more likely to access medical care for fertility. Conclusions: The usage of infertility care varied by demographic, lifestyle, and access characteristics, including speaking an indigenous language, teaching in a rural school, and having a private health care provider.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".