Type 2 Diabetes in South Asians – Exploring Risk Factors and Utilization of Healthcare in Canada
Bibliographic record
Abstract
Type 2 diabetes is a serious chronic condition affecting millions of people worldwide. South Asians (individuals originating from Pakistan, India, Bangladesh, Sri Lanka, and Nepal) represent a high-risk ethnicity for developing type 2 diabetes (T2D) and experience a high prevalence of the disease, even in migrant populations. The objectives of this thesis were twofold: (1) to synthesize the latest literature on (i) risk factors that predispose South Asians to be at high-risk for T2D; (ii) utilization of diabetes-related healthcare services in Canada; and (2) investigate perceptions and experiences of South Asians living with T2D in Ontario, and their utilization of diabetes related services within the provincial healthcare system. The first objective was completed through a scoping review, while the second objective was achieved through 20 in-depth interviews. Findings from these interviews are discussed and broader policy options are recommended.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".