The role of health insurance in explaining immigrant versus non-immigrant disparities in access to health care : Comparing the United States to Canada.
Bibliographic record
Abstract
Using a cross-national comparative approach, we examined the influence of health insurance on U.S. immigrant versus non-immigrant disparities in access to primary health care. With data from the 2002/2003 Joint Canada/United States Survey of Health, we gathered evidence using three approaches: 1) we compared health care access among insured and uninsured immigrants and non-immigrants within the U.S.; 2) we contrasted these results with health care access disparities between immigrants and non-immigrants in Canada, a country with universal health care; and 3) we conducted a novel direct comparison of health care access among insured and uninsured U.S. immigrants with Canadian immigrants (all of whom are insured). Outcomes investigated were self-reported unmet medical needs and lack of a regular doctor. Logistic regression models controlled for age, sex, nonwhite status, marital status, education, employment, and self-rated health. In the U.S., odds of unmet medical needs of insured immigrants were similar to those of insured non-immigrants but far greater for uninsured immigrants. The effect of health insurance was even more striking for lack of regular doctor. Within Canada, disparities between immigrants and non-immigrants were similar in magnitude to disparities seen among insured Americans. For both outcomes, direct comparisons of U.S. and Canada revealed significant differences between uninsured American immigrants and Canadian immigrants, but not between insured Americans and Canadians, stratified by nativity. Findings suggest health care insurance is a critical cause of differences between immigrants and non-immigrants in access to primary care, lending robust support for the expansion of health insurance coverage in the U.S. This study also highlights the usefulness of cross-national comparisons for establishing alternative counterfactuals in studies of disparities in health and health 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".