Disparities in chronic disease among Canada's low-income populations.
Bibliographic record
Abstract
INTRODUCTION: Many studies have found inequities in health among income groups in Canada. We report the variations in the major chronic disease risks among low-income populations, by province of residence, as a proxy measure of social environment. METHODS: We used estimates from the 2005 Canadian Community Health Survey to study residents who were aged 45 years or older and from the lowest income quintile nationally. Multivariate logistic regression was used to examine the relationship between province of residence and risk of chronic diseases. RESULTS: British Columbia is the healthiest province overall but not in terms of its low-income residents, whereas Quebec's low-income residents are at the least risk for major chronic diseases. The significant differences in risk of hypertension, diabetes, and heart disease in favor of British Columbia over Quebec for the entire population disappear when considering only the low-income subset. CONCLUSION: Quebec's antipoverty strategy, formalized as law in 2002, has led to social and health care policies that appear to give its low-income residents advantages in chronic disease prevention. Our findings demonstrate that chronic disease prevalence is associated with investment in social supports to vulnerable populations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".