Access to Care, Health Status, and Health Disparities in the United States and Canada: Results of a Cross-National Population-Based Survey
Bibliographic record
Abstract
OBJECTIVES: We compared health status, access to care, and utilization of medical services in the United States and Canada and compared disparities according to race, income, and immigrant status. METHODS: We analyzed population-based data on 3505 Canadian and 5183 US adults from the Joint Canada/US Survey of Health. Controlling for gender, age, income, race, and immigrant status, we used logistic regression to analyze country as a predictor of access to care, quality of care, and satisfaction with care and as a predictor of disparities in these measures. RESULTS: In multivariate analyses, US respondents (compared with Canadians) were less likely to have a regular doctor, more likely to have unmet health needs, and more likely to forgo needed medicines. Disparities on the basis of race, income, and immigrant status were present in both countries but were more extreme in the United States. CONCLUSIONS: United States residents are less able to access care than are Canadians. Universal coverage appears to reduce most disparities in access to 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".