Exploring diabetes management amongst immigrant Sikhs in the Greater Toronto Area: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: This study describes the ethnocultural influences associated with managing diabetes (Type 2) in a small sample of older Sikh immigrants in Toronto, Canada. The South Asian community, which includes Sikhs, is the fastest growing immigrant population, the second largest visible minority in Canada, and is five times more likely to have diabetes than their Canadian counterparts. The relationship between culture, immigration, and management of diabetes has been recognized, but research of how these areas intersect in the Sikh community is sparse. DESIGN: Data were collected using qualitative semi-structured interviews, and participants were recruited via purposive and snowball sampling techniques. Data were analysed using constant comparative methods. RESULTS: The complexities of diabetes management are organized in this study as the (1) external (2) internal and (3) actualized experiences participants faced navigating cultural dynamics, understanding their diagnosis, and interacting with health resources. CONCLUSION: An individual's diabetes diagnosis and treatment plan interacts with layers beyond the health system which must be understood in order to provide health care that is truly an empowering resource.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".