The effects of a ban on extracurricular activities by teachers on students' levels of physical activity in the Montreal area /
Bibliographic record
Abstract
Since physical activity is part of a healthy lifestyle, there is a lot of interest concerning the determinants of this behaviour in youth since it is tracked into adulthood. Environmental determinants belong to one subset of factors that influence physical activity. Baseline and one-year follow-up data on level of physical activity were collected in classroom questionnaires from 1264 7th grade students (56.2% of eligible students). Physical activity was assessed via an adaptation of the Weekly Activity Checklist. The effect of a ban on extracurricular activities on adolescent physical activity levels was determined. Multivariate logistic regression analyses were used to determine the effect of the ban when controlling for baseline physical activity, the number of extracurricular activities regularly offered at the school, and season at baseline. Students attending high implementation schools were significantly more likely to increase their levels of physical activity after the ban was lifted, than students in low implementation schools (odds ratio (OR) = 1.49 (95% CI = 1.16, 1.91) and 2.19 (95% CI = 1.80, 2.67 for boys and girls respectively). These results suggest that a teachers' ban on sports-related ECAs was associated with a decrease in the PA levels of students attending secondary schools. The impact was higher among girls than boys.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".