Association Between Participation in Physical Education Classes and Physical Activity Among 284,820 Adolescents: A Progressive Exposure Gradient Analysis
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to analyze the associations between participation in physical education (PE) classes and days with ≥60 minutes of moderate- to vigorous-intensity physical activity (MVPA) using different reference categories for participation in PE. METHODS: We used self-reported data from 284,820 adolescents. RESULTS: When no participation in PE was the reference, participation on 1 (prevalence ratio [PR] = 1.15 [1.12-1.18]), 2 (PR = 1.24 [1.20-1.27]), 3 to 4 (PR = 1.32 [1.28-1.36]), and ≥5 days per week (PR = 1.8 [1.33-1.43]) increased frequency of days of ≥60 minutes of MVPA. Participating in PE on 2 (PR = 1.07 [1.02-1.09]), 3 to 4 (PR = 1.15 [1.12-1.18]), and ≥5 days per week (PR = 1.18 [1.15-1.22]) increased the days of ≥60 minutes of MVPA when participation in PE classes on 1 days per week was the reference. When participation in PE on 2 days per week was the reference, participation in PE classes on 3 to 4 (PR = 1.07 [1.04-1.09]) and ≥5 days per week (PR = 1.12 [1.09-1.15]) increased the days of ≥60 minutes of MVPA. Participating in PE classes on ≥5 days per week increased the days of ≥60 minutes of MVPA (PR = 1.05 [1.03-1.07]) when participation on 3 to 4 days per week was the reference. CONCLUSIONS: For those with no participation in PE classes, the addition of any PE classes could positively impact the weekly frequency of days of ≥60 minutes of MVPA. Even in countries/territories with large coverage of participation in PE classes, promoting more PE classes could be useful to increase physical activity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".