A Multilevel Examination of School and Student Characteristics Associated With Physical Education Class Enrollment Among High School Students
Bibliographic record
Abstract
BACKGROUND: Schools can be an efficient venue for promoting physical activity (PA) among adolescents. Physical education (PE) requires investigation because it is a variable associated with adolescent PA levels and its existence in schools represents a significant opportunity for strategies to combat declining PA levels among this population. This article examines the between-school variability in student rates of PE enrollment among a large sample of high schools in Ontario, Canada, and identifies the school- and student-level characteristics associated with PE enrollment. METHODS: This cross-sectional study utilized self-reported school- and student-level data from administrators and students at 73 high schools. Students' enrollment in PE, demographic, behavioral, and psychosocial variables was linked to school environment data comprising of school demographics and administrator assessed quality of policies, facilities, and programs related to PA. Analysis involved multilevel modeling. RESULTS: The mean rate of PE enrollment among the 73 high schools was 62.4%, with rates by school ranging from 28.9% to 81.1%. When student demographics, behavioral, and psychosocial factors were controlled for, there was still a school effect for student PE enrollment. The school effect was explained by the provision of daily PE and school median household income. CONCLUSIONS: This is the first study to examine the extent to which PE enrollment varies between schools and to identify school factors associated with school variability in rates of PE enrollment. Although most variation in PE enrollment lies between students within schools, there is sufficient between-school variation to be of interest to practitioners and policy makers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".