Early onset of type 2 diabetes among visible minority and immigrant populations in Canada
Bibliographic record
Abstract
OBJECTIVES: Type 2 diabetes is a chronic condition that affects nearly over three million Canadians, including immigrants. The timing of the first onset of diabetes has been linked to several other severe diseases. Yet, there is a dearth of empirical studies that examine the timing of the first onset of diabetes among Canadians, in general, and among immigrants and ethnic minority populations within Canada, in particular. DESIGN: Applying event history techniques to the 2013 Canadian Community and Health Survey, we address this research void by examining factors that contribute to the first onset of diabetes among immigrant and visible minority populations in Canada (N = 8905). Given the gendered patterns in the epidemiology of diseases and the differences in risk factors for men and women, gender-specific models were estimated. RESULTS: Results showed that South Asian, Black and Filipino women developed diabetes earlier, compared to women from the UK. Similarly, South Asian, Chinese, Filipino, Black, South East Asian and Arab men developed diabetes earlier than men from the UK. A significant and important finding of this analysis was that the risks of developing diabetes vanished completely for Black and Filipino women, after accounting for lifestyle factors. For South Asian women, however, there was significant attenuation in their risks after accounting for lifestyle factors. The findings were strikingly different for immigrant men. Specifically, their risks of developing diabetes increased after accounting for lifestyle factors. CONCLUSIONS: These results suggest the development of gender-specific and lifestyle interventions, targeted at specific immigrant groups with increased risks of developing diabetes earlier in the life course.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".