LEVERAGING TIME USE DATA TO EXPLORE MIGRANT HEALTH, SOCIALIZING, AND TECHNOLOGY USE
Bibliographic record
Abstract
Abstract Migrant populations tend to locate near other migrants upon arrival in their receiving country. These residential locations are the result of culturally specific social networks, and they result in differing social networks and cultural contexts relative to the native-born population and other migrant groups. Beyond their physical social networks, migrants also use information and communication technology (ICT) to maintain social connections in their sending country. During the COVID-19 pandemic, public health initiatives to discourage socializing (e.g., closing third places) generated concern about social isolation among older adults, especially due to its association with health. In Toronto, Canada, COVID-era lockdown initiatives were particularly prolonged. During this extended lockdown, we fielded a survey and time-use diary on a convenience sample of older Chinese migrants (n = 77) in the Greater Toronto Area. Using the 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 behaviors. Findings reveal that this older migrant community was largely socializing online, but a small group reported socializing in person. Regression results reveal that living conditions, physical health, and the location of social networks are associated with how migrants socialize. This study presents a novel method for exploring geographies and activities within the home in tandem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".