Developing social network typologies for South Asian caregivers with prediabetes, gestational diabetes, and type 2 diabetes in Peel Region, Ontario, Canada.
Bibliographic record
Abstract
BACKGROUND: The prevalence of type 2 diabetes (T2D) is increasing globally, especially among South Asian (SA) adults, and requires innovative solutions. Traditional T2D prevention and management programs focus on lifestyle change but this is often challenging for caregivers with competing priorities and may not fit a family's needs. Utilizing social support has been promising for supporting diabetes prevention and management. We aim to explore how the social networks of SA caregivers with diabetes can influence their health and inform interpersonal and community-level health interventions for this community. METHOD: Participants lived in Peel Region, Ontario were diagnosed with T2D, prediabetes, or gestational diabetes and were SA caregivers of children under 24 years old. Caregivers completed a semistructured interview to discuss (a) their experiences, perceptions, and beliefs about T2D, (b) their caregiving roles in their diabetes management, (c) how the social determinants of health impact diabetes management, and (d) the individuals and resources influencing their diabetes management. We used network analysis to explore network size and composition in relation to social supports. RESULTS: Twenty-one caregivers completed interviews. We identified three network typologies for caregivers, including (a) healthcare and community program focused, (b) isolated, and (c) family and diverse social support and explored how caregivers describe the network actors as supporting or not supporting their diabetes management. CONCLUSION: This study advances our understanding of the social networks and supports that SA caregivers use to support diabetes management and further emphasizes the importance of leveraging existing community supports. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".