Promoting Social Resilience for Racialized Older Immigrants in Canada: Stakeholder Engagement Project Findings
Bibliographic record
Abstract
Abstract The Canadian population is increasingly aging and diversifying. The proportion of racialized older immigrants among older Canadians is projected to be about 25 percent after 2030. However, Racialized older immigrants experience greater social isolation and loneliness when compared to their Canada-born counterparts. Social resilience, understood as the maintenance of positive social relationships and interactions, is essential to reduce social isolation and loneliness. Thus, this study explores the strategies to promote social resilience for racialized old immigrants in Canada. A stakeholder engagement project was conducted in September 2024, including five focus groups with Chinese, Southeast Asia, South Asia, Black and Muslim communities in a metropolitan city in western Canada. Stakeholders in each focus group included community-based racialized older immigrants, service providers to older immigrants, and scholars in immigration studies. Four main groups of strategies were identified from the focus group discussion, including 1) Empowering racialized older immigrants through various learning programs and volunteering opportunities, 2) Developing awareness of the situation of racialized older immigrants within the family, 3) Increasing community-based programs, such as intergenerational projects and neighborhood activities, to enable social interaction and participation for older immigrants, and 4) Advocate for society-level changes (e.g., inclusiveness, public fundings) to support racialized older immigrants. Findings of the project reveal the urgent need to promote social resilience for racialized older immigrants. Multiple levels of strategies were proposed and discussed, with an emphasis on resilience from the society level. The project supports the idea of “social resilience is the resilience of society.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.009 |
| 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".