Healthcare Experiences of South Asian Older Adults in Canada: Aging well, Engagement, and Access
Bibliographic record
Abstract
Background: Racial and ethnic disparities in healthcare have negative consequences for the health and quality of life of immigrants, while the lack of clarity in healthcare systems on how to best provide social and health services for these populations further exacerbates these disparities. With an increase in immigration and in the number of older adults in the Canadian demographic landscape, further research is necessary to understand the diverse ways through which racialized foreign-born older adults experience aging and how structural determinants impact their health and healthcare experience. \n \nObjectives: My research aims to (1) describe how foreign-born South Asian older adults define and conceptualize the notion of healthy aging, (2) examine South Asian-born older adults’ experiences and approaches to patient engagement and healthcare decision-making (3) identify and understand the structural determinants and systemic factors influencing the healthcare experiences and well-being of South Asian older adults in Canada. \n \nMethods: Employing a descriptive, multilingual, and cross-cultural qualitative approach, 47 South-Asian older adults (60+) were interviewed in a semi-structured format, in Hindi, Tamil, Punjabi, Urdu, Bangla, and English over Zoom. Interviews lasted an average of 84 minutes (min: 32, max: 120). I participated in 167 hours of online community events to support relationship building prior to the interviews. \n \nResults: This thesis demonstrates that South Asian older immigrants are a diverse and heterogeneous population and that their conception of healthy aging is strongly influenced by their country of origin. The findings show how racialized foreign-born older adults might provide distinctive perspectives on the aging process and on social theories of aging due to their simultaneous immersion in and belonging to global majority and global minority cultures. The findings also highlight the nuances of language and how miscommunication can arise even when patients and providers are conversing in the same language. Patient engagement and shared decision-making, including the desire for family involvement, are heavily influenced by both culture and gender. Additionally, perceptions of patients regarding the status of physicians can have a notable influence on patient engagement, leading to an increased tendency for patients to agree with healthcare providers’ approach to care. Lastly, this thesis demonstrates participants' perceptions of access to virtual 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".