Type 2 diabetes in Canada: concentration of risk among most disadvantaged men but inverse social gradient across groups in women
Bibliographic record
Abstract
AIMS: To assess sex-specific associations of educational and income levels with Type 2 diabetes mellitus. METHODS: Logistic regression analyses (Canadian Community Health Survey, cross-sectional) adjusted for ethnicity, immigration, urban/rural, overweight/obesity, physical inactivity, smoking, chronic conditions and regular physician. RESULTS: Compared to women with some post-secondary education, Type 2 diabetes was more likely in both high school graduates without post-secondary education [odds ratio (OR) 1.27, 95% confidence interval (CI) 1.07-1.51] and high school non-completers (OR 1.73, 95% CI 1.47-2.04); among men, definitive conclusions in high school graduates without post-secondary education could not be drawn (OR 0.93, 95% CI 0.78-1.12), but Type 2 diabetes was more likely in high school non-completers (OR 1.26, 95% CI 1.08-1.48). Compared to women with the highest income, Type 2 diabetes was three times more likely in the lowest income group (OR 2.90, 95% CI 2.25-3.73), 2.53 times more likely in the low middle income group (OR 2.53, 95% CI 1.98-3.24) and 55% more likely in the high middle income group (OR 1.55, 95% CI 1.20-2.01). Among men, Type 2 diabetes was approximately 40% more likely in both the lowest (OR 1.41, 95% CI 1.10-1.80) and low middle income groups (OR 1.39, 95% CI 1.12-1.71); definitive conclusions in the high middle income group could not be drawn (OR 1.05, 95% CI 0.87-1.28). CONCLUSIONS: In women, Type 2 diabetes increased monotonically with lower educational and income levels; in men, Type 2 diabetes was concentrated in the least educated and least affluent. Our findings support the need for policies and practices that lower diabetes risk among the most disadvantaged women and men and moderately disadvantaged women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".