A national study of the association between language use and health insurance coverage in the United States
Bibliographic record
Abstract
Background: A significant proportion of individuals seen in US hospitals speak a language other than English. A number of reports have shown that individuals who speak a language other than English have diminished access to care, but few have examined specifically language barriers and its relationship to health insurance coverage. Objectives: To estimate the impact of language use on prevalence of reported health insurance coverage across multiple racial and ethnic groups and among persons living in the U.S. for varying periods of time. Design and participants: Cross sectional study using data from the 2010 National Health Interview Survey. Main measures: The main outcome measure is health insurance status. Key results: Persons who spoke Spanish or a language other than English were less likely to have insurance. Among Hispanics who speak Spanish or a language other than English, only 50.6% report having health insurance coverage compared to 76.7% of Hispanics who speak only or mostly English. For non-Hispanic whites who speak Spanish or a language other than English, 71.7% report having health insurance coverage compared to 83.4% of non-Hispanic whites who speak only or mostly English, this same pattern was observed across all racial/ethnic groups. Among those speaking only or mostly English living in the U.S. <15 years had significantly lower adjusted odds of reporting health insurance coverage compared to those born in the United States. Conclusions: This was a large nationally representative study describing language differences in insurance access using a multi-ethnic population. This data suggest that individuals who speak a language other than English are less likely to have insurance across all racial and ethnic groups and nativity and years in the United States groups, underscoring the significant independent importance of language as a predictor for access to insurance.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".