“I Am the Last Priority”: Factors Influencing Diabetes Management Among South Asian Caregivers in Peel Region, Ontario
Bibliographic record
Abstract
OBJECTIVE: In this study, we aimed to identify sociocultural and systemic factors influencing diabetes management among South Asian (SA) caregivers in Peel Region, Ontario. METHODS: Twenty-one semistructured interviews were conducted with SA caregivers using a qualitative descriptive design. Data were analyzed using thematic analysis and intersectionality analysis. RESULTS: Themes identified included 1) prioritizing family caregiving over diabetes self-management; 2) labour market impacts on diabetes self-management; and 3) challenges navigating Canadian health and social service systems. SA caregivers described social, economic, and systemic challenges impacting type 2 diabetes management. Systemic factors influencing diabetes management included discrimination and inequities in labour policies and lack of social and health resources funding. Recommendations by caregivers included whole-family, community-based, culturally tailored approaches to diabetes prevention and management strategies. CONCLUSIONS: Providing support with system navigation, encouraging family-based approaches, and addressing the social determinants of health could be beneficial for supporting SA families with diabetes management and prevention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".