Understanding Social Network and Support for Older Immigrants in Ontario, Canada: Protocol for a Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Older adults are the fastest growing age group worldwide and in Canada. Immigrants represent a significant proportion of older Canadians. Social isolation is common among older adults and has many negative consequences, including limited community and civic participation, increased income insecurity, and increased risk of elder abuse. Additional factors such as the social, cultural, and economic changes that accompany migration, language differences, racism, and ageism heighten older immigrants' vulnerability to social isolation. OBJECTIVE: This mixed-methods sequential (qualitative-quantitative) study seeks to clarify older immigrants' social needs, networks, and support and how these shape their capacity, resilience, and independence in aging well in Ontario. METHODS: Theoretically, our research is informed by an intersectionality perspective and an ecological model, allowing us to critically examine the complexity surrounding multiple dimensions of social identity (eg, gender and immigration) and how these interrelate at the micro (individual and family), meso (community), and macro (societal) levels in diverse geographical settings. Methodologically, the project is guided by a collaborative, community-based, mixed-methods approach to engaging a range of stakeholders in Toronto, Ottawa, Waterloo, and London in generating knowledge. The 4 settings were strategically chosen for their diversity in the level of urbanization, size of community, and the number of immigrants and immigrant-serving organizations. Interviews will be conducted in Arabic, Mandarin, and Spanish with older women, older men, family members, community leaders, and service providers. The study protocol has received ethics approval from the 4 participating universities. RESULTS: Quantitative and qualitative data collection is ongoing. The project is funded by the Social Sciences and Humanities Council of Canada. CONCLUSIONS: Comparative analyses of qualitative and quantitative data within and across sites will provide insights about common and unique factors that contribute to the well-being of older immigrants in different regions of Ontario. Given the comprehensive approach to incorporating local knowledge and expert contributions from multilevel stakeholders, the empirical and theoretical findings will be highly relevant to our community partners, help facilitate practice change, and improve the well-being of older men and women in immigrant communities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12616.
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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 | 0.035 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.005 |
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".