Associations Between School Recreational Environments and Physical Activity
Bibliographic record
Abstract
BACKGROUND: School environments may promote or hinder physical activity in young people. The purpose of this research was to examine relationships between school recreational environments and adolescent physical activity. METHODS: Using multilevel logistic regression, data from 7638 grade 6 to 10 students from 154 schools who participated in the 2005/06 Canadian Health Behaviour in School-Aged Children Survey were analyzed. Individual and cumulative effects of school policies, varsity and intramural athletics, presence and condition of fields, and condition of gymnasiums on students' self-reported physical activity (>or=2 h/wk vs <2 h/wk) were examined. RESULTS: Moderate gradients in physical activity were observed according to number of recreational features and opportunities. Overall, students at schools with more recreational features and opportunities reported higher rates of class-time and free-time physical activity; this was strongest among high school students. Boys' rates of class-time physical activity were 1.53 (95% confidence interval (CI) = 1.12-1.80) times as high at high schools with the most recreational features as at schools with the fewest. Similarly, girls' rates of free-time physical activity at school were 1.62 (95% CI: 0.96-2.21) times as high at high schools with the most opportunities and facilities as compared to schools with the fewest. Modest associations were observed between individual school characteristics and class-time and free-time physical activity. CONCLUSIONS: Taken together, the cumulative effect of school recreational features may be more important than any one characteristic individually.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".