ETHNICITY AND WELL-BEING AMONG OLDER IMMIGRANTS IN DIVERSE SOCIOCULTURAL CONTEXTS
Bibliographic record
Abstract
Abstract The critical interaction between culture, ethnicity, and social determinants of health is a vital area of study, particularly regarding the unique experiences of ethnocultural minority older adults. The complex intersections of these factors across diverse social landscapes are ripe for investigation to uncover inequalities and inform targeted strategies to reduce the inequalities encountered by these groups. This symposium presents four key studies that examine the well-being, healthcare access, and civic participation of aging Asian immigrants, with a focus on South Asian populations in Hong Kong and Canada, as well as Chinese communities in Canada and the United States. These studies highlight the need for culturally congruent interventions. They emphasise the challenges faced in health and mental health service accessibility and the rich tapestry of cultural diversity within civic involvement. Critical factors such as the quality of intergenerational relationships, the breadth and depth of social capital, language skills, transportation availability, financial dependencies, and the impact of pre-migration histories are shown to significantly affect the lives and civic engagement of aging Asian immigrants. The research presented here strongly advocates for culturally attuned policies and practices. It underscores an imperative for health promotion, healthcare accessibility, and civic inclusion that are responsive to the cultural identities and preferences of diverse older populations. The findings of these studies are critical to the development of targeted interventions, public policies and supportive frameworks that address the unique needs and overcome the barriers faced by ageing migrants around the world.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".