“When in Rome…”: structural determinants impacting healthcare access, health outcomes, and well-being of South Asian older adults in Ontario using a multilingual qualitative approach
Bibliographic record
Abstract
With the increase in international migration, the need for an equitable healthcare system in Canada is increasing. The current biomedical model of healthcare is constructed largely in the Eurocentric tradition of medicine, which often disregards the diverse health perspectives of Canada's racialized immigrant older adults. As a result, current healthcare approaches (adopted in the US and Canada) fall short in addressing the health needs of a considerable segment of the population, impeding their ability to access healthcare services. This study aimed to identify and understand the structural and systemic factors that influence healthcare experiences and well-being among South Asian older adults in Ontario, addressing a significant gap in empirical and theoretical knowledge in the Canadian context. We conducted in-depth individual and dyadic interviews (n = 28) utilizing a descriptive multilingual cross-cultural qualitative approach. Through this research, participants expressed that their understanding of well-being does not align with that of their healthcare providers, resulting in unmet health needs. Our study uses an intersectional lens to demonstrate participants' perceptions of virtual access to care and systemic factors, such as mandatory assimilation and whiteness as a taken-for-granted norm impacting the health and well-being of South Asian older adults. The findings of this research can offer valuable insights to healthcare providers and policymakers in developing culturally competent practices, guidelines, and training policies that effectively address the healthcare needs of the South Asian population in Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".