“Think positive and don’t die alone” - Foreign-born, South Asian older adults’ perceptions on healthy aging
Bibliographic record
Abstract
South Asians are the largest and fastest-growing racialized group in Canada, yet there are limited data on various aspects of health and well-being within this population. This includes the South Asian older adults’ ethnoculturally informed perceptions of ageing. The study aimed to understand how social and cultural forces impact the meaning assigned to healthy ageing amongst older South Asians in Canada. We recruited with purposeful and snowball sampling strategies in Southern Ontario. We conducted in-depth focus group and individual interviews (n = 19) in five South Asian languages, employing a multilingual and cross-cultural qualitative approach. In our analysis, we identified three central themes: (a) taking care of body (b) taking care of mind and heart and (c) healthy ageing through the integration of mind and body. Our study demonstrates that older immigrants are a diverse and heterogeneous population and that their conception of healthy ageing is strongly influenced by their country of origin. This study also demonstrates how racialized foreign-born older adults might provide distinctive perspectives on the ageing process and on social theories of ageing due to their simultaneous immersion in and belonging to global majority and global minority cultures. This research also adds to the limited body of literature on the theories of ageing, despite migration trends, still has a white-centric lens.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".