The Experiences of South Asian Older Adults with Type II Diabetes Mellitus Accessing Diabetes-Related Healthcare Services
Bibliographic record
Abstract
Background: Type II Diabetes Mellitus (T2DM) disproportionately affects South Asian populations, with South Asian older adults facing additional barriers to accessing appropriate T2DM healthcare services. These barriers include language difficulties, cultural insensitivities, and structural challenges in the healthcare system. Within the Greater Toronto Area (GTA), Tamil older adults, a subgroup of the South Asian population, may experience these barriers more acutely due to limited access to linguistically and culturally appropriate healthcare. Aim: This dissertation aimed to explore the lived experiences of South Asian older adults and the perspectives of T2DM health service providers regarding access to T2DM healthcare. The goal was to suggest strategies to improve access to diabetes-related healthcare for South Asian-Tamil older adults in the Greater Toronto Area (GTA), Ontario. Methods: The thesis included three studies: 1) A systematic review of the experiences of South Asian adults accessing T2DM-related healthcare in host countries; 2) An interpretive descriptive study (ID) study with nine Tamil older adults with T2DM in the GTA; 3) An ID study with nine diabetes health service providers in the GTA. Results: Study 1 highlighted common barriers, including language difficulties, limited cultural competence in care, and health system navigation issues. Study 2 revealed that Tamil older adults additionally faced financial barriers, heavy reliance on family for interpretation, and a lack of culturally tailored care, which hindered their ability to access T2DM-related care effectively. Study 3 emphasized the challenges of offering culturally appropriate services due to resource limitations, lack of Tamil-speaking staff, and inadequate training in cultural safety. Both groups identified the need for improved language support, increased availability of Tamil-speaking providers, and culturally adapted T2DM education. Conclusion: The results highlight Tamil older adults' barriers to accessing T2DM-related healthcare services. Findings suggest that Tamil older adults should be empowered to navigate the health system through education and the provision of resources. Health service providers should receive training to provide culturally appropriate care and advice to Tamil older adults. Finally, coordination at the primary care level should be improved.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".