School Sports Opportunities Influence Physical Activity in Secondary School and Beyond
Bibliographic record
Abstract
BACKGROUND: The purpose of the present study was to examine whether the availability of intramural or extramural sports in secondary schools is associated with physical activity levels in youth throughout secondary school and at age 20. METHODS: Eight hundred and eight adolescents from 10 secondary schools in Montreal, Canada, provided physical activity data every 3 months during the school year from ages 13 to 17, and again at age 20. School administrators completed questionnaires on the availability of intramural and extramural sports. Three-level general linear models were used to examine associations among the number of intramural and extramural sports, moderate and vigorous physical activity controlling for age, sex, body mass index, mother's education, and school-level socioeconomic status. RESULTS: Regardless of whether or not they reported participating in intramural sports, adolescents in schools with more intramural sports engaged in 3.6 (p = .03) more total, and 1.3 (p = 0.03) more vigorous activities per week than those attending schools with fewer intramural sports. Number of extramural sports was not statistically significantly associated with physical activity, regardless of whether or not individual students participated. CONCLUSION: Providing more opportunities for intramural sports in secondary schools may be an effective strategy to help adolescents attain physical activity recommendations.
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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 | 0.000 | 0.001 |
| 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) | 0.005 | 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".