Correlates of Outdoor Time in Schoolchildren From Families Speaking Nonofficial Languages at Home: A Multisite Canadian Study
Bibliographic record
Abstract
BACKGROUND: Previous research shows that children from ethnic minority groups spend less time outdoors. Using data collected in 3 regions of Canada, we investigated the correlates of outdoor time among schoolchildren who spoke a nonofficial language at home. METHODS: A total of 1699 children were recruited from 37 schools stratified by area-level socioeconomic status and type of urbanization. Among these, 478 spoke a nonofficial language at home. Children's outdoor time and data on potential correlates were collected via questionnaires. Gender-stratified linear multiple regression models examined the correlates of outdoor time while controlling for age and sampling variables. RESULTS: In boys, higher independent mobility, higher outdoor air temperature, mobile phone ownership, having older parents, and parents who biked to work were associated with more outdoor time. Boys living in suburban (vs urban) areas spent less time outdoors. The association between independent mobility and outdoor time became weaker with increasing age for boys. In girls, lower parental education and greater parental concerns about neighborhood safety and social cohesion were associated with less outdoor time. CONCLUSIONS: Correlates of outdoor time differ by gender and span the social ecological model underscoring the need for gender-sensitized interventions targeted at individual, family, social, and physical environmental correlates to increase outdoor time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".