Mobility, ICT, and health: a built environment investigation of older Chinese migrants’ social isolation and loneliness
Bibliographic record
Abstract
BACKGROUND: Social isolation and loneliness have detrimental impacts on health, especially for older adults. During the COVID-19 pandemic, physical access to third places (e.g., coffee shops, libraries) decreased due to the closure of non-essential destinations and personal risk assessments. Older adults reported adopting information and communication technology (ICT) during pandemic lockdowns. ICT-mediated socializing may have different impacts on loneliness than in-person equivalents. Understanding access to social connection and their distinct relationships to the built environment and health for older Chinese migrants is critical to supporting equitable, healthy aging in a post-COVID world. METHODS: Using a survey of older Chinese migrants in the Greater Toronto Area (GTA) during the extended COVID-19 lockdown, we investigate how community mobility and ICT use, two mechanisms of socializing, relate to the built environment and influence loneliness (De Jong Gierveld 6-item scale), as well as mental and physical health (SF-12). Specifically, we use a structural equation model to test a theoretical framework of older adult social isolation. RESULTS: Our model demonstrates the importance of community mobility for 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. CONCLUSIONS: Results indicate that ICT use might have limited ability to reduce loneliness and support mental health when mobility is limited. Addressing older migrants' barriers to community mobility is critical to reducing feelings of loneliness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".