The Impact of Changes in Physical Education Class Enrollment on <scp>Moderate‐to‐Vigorous</scp> Physical Activity Among a Large Sample of Canadian Youth
Bibliographic record
Abstract
BACKGROUND: Few youth engage in sufficient daily moderate-to-vigorous physical activity (MVPA), and the likelihood of meeting guidelines declines through secondary school. Physical education (PE) can afford youth with opportunities for MVPA. Therefore, the purpose of this study was to investigate the impact of changes in PE enrollment on MVPA and MVPA guideline adherence in Ontario and Alberta secondary students. METHODS: Linked survey data was used from 1514 students who participated in year 3 (2014/2015 baseline) and year 6 (2017/2018 follow-up) of the COMPASS Study. Regression models tested whether changes in PE enrollment predicted changes in self-reported MVPA (minutes) and MVPA guideline adherence from grade 9 (baseline) to grade 12 (follow-up), controlling for sports participation and sociodemographic variables. RESULTS: Students who remained enrolled in PE in grade 12 reported a daily average of 30 minutes more MVPA. Among students meeting MVPA guidelines and enrolled in PE in grade 9, students not taking PE in the current term in grade 12 were less likely to continue to meet guidelines than students currently enrolled in PE (adjusted odds ratio 0.53; 95% confidence interval: 0.36, 0.78; p < .0013). CONCLUSIONS: Findings from this study could inform the future implementation of a mandatory PE credit for upper year secondary students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".