South Asian-Tamil Older Adults Accessing Diabetes-Related Health Care Services in the Greater Toronto Area, Canada: An Interpretive Descriptive Study
Bibliographic record
Abstract
Tamil immigrants in Canada face high rates of Type II Diabetes Mellitus (T2DM) and significant barriers in accessing T2DM-related services. These barriers are often amplified for older adults, whose age-related needs intersect with cultural, linguistic, and socioeconomic factors. This study explored the lived experiences of Tamil older adults accessing T2DM-related health care services in the Greater Toronto Area. A qualitative interpretive description approach was used, involving in-depth semi-structured interviews with nine Tamil older adults. Participants were recruited through purposive and snowball sampling. Thematic analysis was applied, with findings organized using Levesque et al.'s framework (). Five key themes were identified: (1) timely and informed diabetes management, (2) reliance on trusted health service providers, (3) reliance on others for transportation, (4) financial factors, and (5) navigating health care through cultural and communication factors. Identified themes can inform potential solutions to improve access including centralized resource hubs, culturally tailored education programs, affordable transportation options, and an integrated health care approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".