14-year diabetes incidence: the role of socio-economic status.
Bibliographic record
Abstract
BACKGROUND: Diabetes prevalence is associated with low socioeconomic status (SES), but less is known about the relationship between SES and diabetes incidence. DATA AND METHODS: Data from eight cycles of the National Population Health Survey (1994/1995 through 2008/2009) are used. A sample of 5,547 women and 6,786 men aged 18 or older who did not have diabetes in 1994/1995 was followed to determine if household income and educational attainment were associated with increased risk of diagnosis of or death from diabetes by 2008/2009. Three proportional hazards models were applied for income and for education--for men, for women and for both sexes combined. Independent variables were measured at baseline (1994/1995). Diabetes diagnosis was assessed by self-report of diagnosis by a health professional. Diabetes death was based on ICD-10 codes E10-E14. RESULTS: Among people aged 18 or older in 1994/1995 who were free of diabetes, 7.2% of men and 6.3% of women had developed or died from the disease by 2008/2009. Lower-income women were more likely to develop type 2 diabetes than were those in high-income households. This association was attenuated, but not eliminated, by ethno-cultural background and obesity/overweight. Associations with lower educational attainment in unadjusted models were almost completely mediated by demographic and behavioural variables. INTERPRETATION: Social gradients in diabetes incidence cannot be explained entirely by demographic and behavioural variables.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".