Associations Between School Environments, Policies and Practices and Children's Physical Activity and Active Transportation
Bibliographic record
Abstract
BACKGROUND: There is evidence of school-level variability in children's active behaviors. This study investigated the associations between school environments, policies and practices, and children's physical activity (PA) and active school transportation (AST), in a school ecology context. METHODS: We recruited children (N = 1699, age = 10.2 ± 1.0 years, 55.0% girls) in 37 schools from 3 diverse regions of Canada. We then collected data using questionnaires (child, parent) and pedometers. In each school, an official completed a School Health Environment Survey. Multilevel regression models were used to examine associations with children's daily steps, and frequency and volume (frequency*distance) of AST. RESULTS: Between-school variation ranged from 4.7% to 22.2% demonstrating that school environments are associated with children's active behaviors. None of the school environment variables were significantly associated with children's PA or frequency of AST. Nevertheless, their inclusion improved the PA model. Children's volume of AST increased in schools that reported more initiatives to promote AST. CONCLUSIONS: Our findings suggest that multiple components are needed to effectively promote active behaviors in children. Schools should determine the areas in which they can improve and assess the feasibility of implementing measures to make their school environments, policies, and practices more conducive to PA and AST.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".