Spatial Mobility Change Among Older Chinese Immigrants During the COVID-19 Pandemic: The Role of Physical, Social, and Virtual Environmental Factors
Bibliographic record
Abstract
BACKGROUND: Vast spatial mobility changes happened globally during the COVID-19 pandemic, profoundly affecting older adults' well-being and active aging experience. This study aims to examine how the virtual environment and cyberspace, in conjunction with the physical and social neighbourhood environments, influence outdoor activities and spatial mobility for older immigrants. METHODS: Four online focus groups were conducted with 25 older Chinese immigrants aged 65 and over in the Greater Toronto Area, Canada. The focus groups explored coping strategies during the pandemic and spatial mobility patterns related to different activity types such as grocery shopping, leisure activities and physical exercises, social and familial activities, and healthcare. Qualitative thematic analysis was conducted guided by the neighbourhood and health theoretical framework. RESULTS: The overall engagement of older Chinese immigrants in various types of outdoor activities declined drastically and the spatial mobility pattern was complex. This change was shaped largely by the intersecting physical/built (e.g., residential conditions, access to public spaces), social (e.g., social support, interpersonal cohesion) and virtual (e.g., online communities and internet-based resources) environmental factors, as well as individual risk perceptions towards COVID-19 and public health interventions during the pandemic. CONCLUSIONS: Virtual environment emerged as an important domain that compensates for the heavily reduced spatial mobility of the group during the pandemic. It functioned as a vital channel for older Chinese immigrants to sustain the necessary leisure, social, and healthcare-related activities and maintain well-being during the pandemic. The study provides implications for addressing neighbourhood-level factors in policymaking and implementing initiatives to enhance active ageing experience of older Chinese immigrants.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".