Competing challenges for immigrant seniors: Social isolation and the pandemic
Bibliographic record
Abstract
The pandemic has exposed and amplified complex and complicated health and societal challenges while offering immense opportunities to transform societies to improve health for all. Social isolation is a challenging and persistent issue experienced by many older adults, especially among immigrant and refugee seniors. Unique risk factors such as racism, discrimination, language barriers, weak social networks, and separation from friends and family predispose immigrant and refugee seniors to a higher risk of social isolation. The pandemic has magnified the unique risks and has highlighted the differential health and economic impacts. This article examines social isolation among immigrant and refugee seniors in Canada by focusing on the policy context, available programs and services to reduce social isolation, and the conceptualization and measurement considerations for advancing research to address social isolation among this growing population. Drawing on specific examples, we discuss immigration, aging, and social isolation within the context of Canada. While our article focuses on Canada as a case study, our discussion has relevancy and implications for other high-income countries with aging immigrant and refugee populations. In moving forward, we argue that a more complete and targeted understanding of social isolation is essential to informing program and policy development to support immigrant and refugee seniors in Canada and beyond. The transformation needed in our societies to create health for all requires strong equity and determinants of health perspective and a systems approach beyond health to ensure lasting change.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".