AVENUES OF SOCIAL ISOLATION AND HEALTH AMONG OLDER CHINESE MIGRANTS: A STRUCTURAL EQUATION MODELING APPROACH
Bibliographic record
Abstract
Abstract Social isolation and loneliness have detrimental impacts on health. During the COVID-19 pandemic, access to third places (e.g., coffee shops, libraries) decreased due to the closure of non-essential destinations for fear of serious illness. With reported rises in anti-Asian hate crimes, which primarily impacted older Asian adults, community mobility might have been additionally affected. Older adults reported adopting ICT during pandemic lockdowns, which may have sufficiently replaced previous activities that would require trips out of the home. Understanding different avenues of social connection and their distinct relationships to health for a vulnerable migrant population is critical to supporting equitable, healthy aging in a post-COVID world. Using a survey of older Chinese migrants in the Greater Toronto Area (GTA) during the extended COVID-19 lockdown, we investigate both community mobility and ICT use to understand how either avenue of socializing is related to the built environment and what the impact of community mobility and ICT use has on loneliness (De Jong Gierveld 6-item scale), mental and physical health (SF-12). Specifically, we use a structural equation model to test a theoretical framework of older adult social isolation. Ultimately, our model demonstrates the importance of community mobility in reducing feelings of loneliness, while ICT use is significantly related to better physical health. Both community mobility and ICT use have significant, although opposite, relationships to transit density. Results indicate that ICT use might have a limited ability to reduce loneliness and support mental health when mobility is limited.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".