Alone together? A time use approach for examining socializing when travel is limited
Bibliographic record
Abstract
Migrant populations access different social networks due to their cultural contexts and the locations of their social relationships. The COVID‐19 pandemic generated concern about social isolation among older adults. During Ontario's extended lockdown, we investigated the social behaviours of a generally sedentary older Chinese migrant community (n = 77) in the Greater Toronto Area. Using single‐day activity diaries, we grouped respondents using a k‐means clustering approach, which resulted in four categories of socializing characteristics. We then used ANOVA tests and multinomial logistic regression to understand the geographic contexts for these socializing behaviours. Findings reveal that this older migrant community was mostly socializing online, but a small group reported socializing in person. Living in a house, having better physical health, and having children living abroad was associated with a higher likelihood of socializing in person rather than online. Ultimately, it is important to understand the context within which older migrants socialize in order to support social connection and related health outcomes as they age.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".