Comparing the health of low income and less well educated groups in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: A limited number of health status and health-related quality of life (HRQL) measures have been used for inter-country comparisons of population health. We compared the health of Canadians and Americans using a preference-based measure. METHODS: The Joint Canada/United States Survey of Health (JCUSH) 2002-03 conducted a comprehensive cross-sectional telephone survey on the health of community-dwelling residents in Canada and the US (n = 8688). A preference-based measure, the Health Utilities Index Mark 3 (HUI3), was included in the JCUSH. Health status was analyzed for the entire population and white population only in both countries. Mean HUI3 overall scores were compared for both countries. A linear regression determinants of health model was estimated to account for differences in health between Canada and the US. Estimation with bootstraps was used to derive variance estimates that account for the survey's complex sampling design of clustering and stratification. RESULTS: Income is associated with health in both countries. In the lowest income quintile, Canadians are healthier than Americans. At lower levels of education, again Canadians are healthier than Americans. Differences in health among subjects in the JCUSH are explained by age, gender, education, income, marital status, and country of residence. CONCLUSION: On average, population health in Canada and the US is similar. However, health disparities between Canadians and Americans exist at lower levels of education and income with Americans worse off. The results highlight the usefulness of continuous preference-based measures of population health such as the HUI3.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".