Gender-related differences in the association between socioeconomic status and self-reported diabetes
Bibliographic record
Abstract
BACKGROUND: The prevalence of diabetes has been steadily increasing in Western countries. We investigated the impact of socioeconomic status (SES) on the prevalence of self-reported diabetes, and its differences between genders. METHODS: Data for this investigation were derived from the second cycle of the National Population Health Survey conducted in 1996-1997. A total of 39 021 subjects (17 730 males and 21 291 females) >/=40 years of age who answered the question about diabetes were included in the present analysis. Educational attainment and income adequacy were used as indicators of SES. Multiple logistic regression models were constructed for men and women separately to assess the effects of SES on the prevalence of diabetes after adjustment for age, area of residence, body mass index, and physical activity. RESULTS: and The prevalence of diabetes was 6.6% among men and 5.5% among women. The CONCLUSIONS: prevalence increased with decreasing income category and educational attainment in both genders. The odds ratios for income and education in relation to diabetes after adjustment remained significant in women, but attained unity in men. Canadian women >/=40 years of age of low SES have a relatively high prevalence of diabetes, independent of age, area of residence, obesity, and physical inactivity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".