Persistent Disparities in the Use of Health Care Along the US–Mexico Border: An Ecological Perspective
Bibliographic record
Abstract
OBJECTIVES: We examined disparities in health care use among US-Mexico border residents, with a focus on the unique binational environment of the region, to determine factors that may influence health care use in Mexico. METHODS: Data were from 2 waves of a population-based study of 1048 Latino residents of selected Texas border counties. Logistic regression models examined predictors of health insurance coverage. Results from these models were used to examine regional patterns of health care use. RESULTS: Of the respondents younger than 65 years, 60% reported no health insurance coverage. The uninsured were 7 and 3 times more likely in waves 3 and 4, respectively, to use medical care in Mexico than were the insured. Preference for medical care in Mexico was an important predictor. CONCLUSIONS: For those who were chronically ill, old, poor, or burdened by the lengthy processing of their documents by immigration authorities, the United States provided the only source of health care. For some, Mexico may lessen the burden at the individual level, but it does not lessen the aggregate burden of providing highly priced care to the region's neediest. Health disparities will continue unless policies are enacted to expand health care accessibility in the region.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".