Contrasting Inequalities: Comparing Correlates of Health in Canada and the United States
Bibliographic record
Abstract
Comparative health studies consistently find that Canadians on average are healthier than Americans. Comparing health status within and between Canada and the United States provides key insights into the distribution of inequalities in these two countries. Canada’s universal health care insurance system contrasts with the mixed system of the United States: universal care for seniors, private health care insurance for many, and no or intermittent coverage for others. These countries are also notably different in the extent of income and racial/ethnic inequalities. It is within this context that this study compares the relative strength of the relationships between social, economic, and demographic factors (sex, age, marital status, income, education, country of birth, and race/ethnicity) and health status in Canada and the United States. Evidence drawn from the 2002-2003 Joint Canada/United States Survey of Health reveals that the correlations between these factors, above all country of birth and race/ethnicity, and health are relatively stronger in the United States, reflecting differences in health care access and racial/ethnic-based inequalities between the countries. The study findings are suggestive of the effects of universal access to health care and more equitable distribution of other social resources in protecting the health of the general population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.020 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".