Social-ecological correlates of children's outdoor playtime and their interaction with gender: a national longitudinal study
Bibliographic record
Abstract
BACKGROUND: Outdoor playtime (OP) is consistently associated with higher physical activity in children, but it has declined over the last few decades, underscoring the need to better understand its correlates. Guided by the social-ecological model, we explored the correlates of parent-reported OP in Canadian children. METHODS: In December 2020, we recruited 2291 parents of 7- to 12-year-olds across Canada and followed up every 6 months until June 2022 (4 study waves). We asked parents to report their child's OP on weekdays and weekend days during the previous week. We employed generalized estimating equations to investigate correlates of accumulating ≥1 h/day of OP, adjusting for household income and study wave. We tested whether gender moderated each correlate in the multivariable model. RESULTS: The final multivariable model included 12 significant correlates incorporating four at the individual level (child age, gender, independent mobility, and mobile phone ownership), four at the interpersonal level (parent age and gender, perceived behavioural control for physical activity, and dog ownership), two at the community level (social cohesion and school attendance mode) and two at the built/natural environment level (population density and study wave [likely acting as a proxy for season]). Gender moderated the association of five correlates: child age, independent mobility, social cohesion, school attendance mode, and study wave. CONCLUSIONS: As postulated by the social-ecological model, correlates of OP span multiple levels of influence and interactions between levels are evident. Gender also appears to be an important moderator. Findings can inform future interventions to promote OP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".