Economic Factors Contributing to Social Isolation Among Immigrant Older Adults in the Greater Toronto Area: A Qualitative Interpretive Description
Bibliographic record
Abstract
Background: In Canada, 30 percent of the older adult population is foreign-born. Immigrant older adults are more likely to experience significant social isolation due to a variety of factors. However, limited research exists on the influence of specific factors. The objective of this study is to understand the economic factors that contribute to social isolation among older immigrants in the Greater Toronto Area (GTA), Canada. Methods: A qualitative interpretive description method was used. Following research ethics boards’ approval, semi-structured individual interviews were conducted with a total of 47 Arabic, Mandarin, and Punjabi-speaking older immigrants in the GTA. The interviews were conducted in their preferred language, audio-recorded, and translated (when needed) into English and transcribed. Thematic analysis of the data was informed by an ecosystemic framework. Results: Six themes were identified: (1) barriers to finding employment; (2) living a “hand-to-mouth life” due to limited income/pension; (3) housing costs that eliminate choices and options; (4) costs (and availability) of transportation as a barrier to getting around; (5) lack of “essential” healthcare coverage; and (6) costs of community programs that prevent “getting out of the house.” These economic factors at micro, meso, and macro levels of society intersected to create desperate situations that contributed to social isolation among older immigrants in the GTA. Conclusions/Implications: Addressing these economic factors is critical to immigrant older adults’ aging in place. Service providers must advocate for accessible physical and financial resources and services including affordable housing and transportation, old age security, and comprehensive healthcare coverage for older immigrants. Future research should focus on economic challenges faced by older adults across other immigrant communities in the GTA as well as in other cities, provinces, and territories.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".