A cross-national comparative perspective on racial inequities in health: the USA versus Canada
Bibliographic record
Abstract
BACKGROUND: Cross-national comparisons allow the examination of the malleability of associations between race and health. Racial inequities in chronic conditions, indicators of health status and behavioural risk factors between two similar advanced capitalist countries were compared. It was hypothesised that racial inequities will be mitigated in Canada compared with the USA. METHODS: Population-based, cross-sectional data from the 2002-3 Joint Canada-USA Survey of Health (JCUSH) with 4953 adult respondents from the USA and 3455 from Canada. Models adjusted for age, sex, foreign birth, marital status, health insurance, education, income and home ownership. RESULTS: Compared with the USA, racial inequities in health were attenuated in Canada. In the USA, racial inequities in chronic diseases and fair or poor self-rated health were largely driven by inequities found among the native born. Strikingly, in Canada, however, there were few significant racial inequities and those occurred exclusively among the foreign born. Within strata of race and foreign birth, Canadians fared better, with both white people and non-white people reporting better health than their American counterparts. Foreign-born Canadians and Americans were more similar to each other in terms of health than native-born Canadians and Americans. Only among the native born did American white people and American non-white people have higher adjusted odds of hypertension, diabetes and obesity than Canadian white people and Canadian non-white people respectively. Self-rated health was worse for non-white Americans than non-white Canadians regardless of foreign birth. CONCLUSION: The influence of race on health is context dependent. There is no necessary link between race and a variety of health indicators.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".