Association Between School Policies and Built Environment, and Youth's Participation in Various Types of Physical Activities
Bibliographic record
Abstract
BACKGROUND: School environmental characteristics may be associated with youth's participation in different types of physical activities (PAs). This study aimed to identify which school policies and built environmental characteristics were associated with participation in organized, nonorganized, individual, and group-based activities. METHODS: This cross-sectional analysis included 776 students in grade 5 or 6 from 16 schools. The school environment was assessed through school-based questionnaires completed by school representatives. Types of PA and attainment of PA recommendations were obtained using self-administered student questionnaires. Associations between environment and student PA were examined using multilevel logistic regressions. RESULTS: Schools with favorable active commuting environments were positively associated with girls' participation in organized (odds ratio [OR] = 1.34, confidence interval [CI] = 1.04-1.74) and group-based PA (OR = 1.54, CI = 1.19-1.99) and with boys' odds of participating in individual activities (OR = 1.45, CI = 1.04-2.04). There was also a positive relationship between having a school environment favorable to active commuting and boys' odds of meeting PA recommendations (OR = 2.19, CI = 1.43-3.37). School policies supporting PA were positively associated with girls' odds of participating in nonorganized activities (OR = 1.18, CI = 1.00-1.40). CONCLUSIONS: School environments that favor active commuting may encourage participation in different types of PA. School policies promoting PA also may encourage girls to participate in organized activities.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".