Service Provider Perspectives on Exploring Social Determinants of Health Impacting Type 2 Diabetes Management for South Asian Adults in Peel Region, Canada
Bibliographic record
Abstract
OBJECTIVES: Individuals from South Asian communities are known to have a higher likelihood of developing type 2 diabetes (T2D), which is often attributed to individual lifestyle and behavioural factors. This focus on individual responsibility can position communities as complicit in their illness, compounding stigmatization and systemic discrimination. In this article, we explore the social determinants of health (SDOH) that influence health behaviours among South Asian adults with T2D from a service provider's perspective. METHODS: Using a qualitative descriptive design, we conducted semistructured interviews with 12 community, social and health-care service providers. We used thematic analysis and the analytical concept of intersectionality to explore how different social locations and SDOH impact T2D management for South Asian adults. RESULTS: Three themes were identified: 1) managing challenges with settlement process, labour policies and job market disparities take priority over T2D management; 2) poor working conditions and low socioeconomic status reduce access to health care and medication; and 3) there are social, economic and cultural barriers to implementing diet and exercise recommendations. CONCLUSIONS: Service providers identified social, economic and systemic factors as influencing the higher prevalence of T2D among South Asian individuals. They also identified their important roles in providing culturally appropriate supports to address SDOH and described advocacy for changes to policies and practices that reinforce systemic racism. The providers further suggested that more equitable employment policies and practices are needed to address the systemic factors that contribute to the higher risk of T2D among South Asian adults in the Peel Region.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".