Does the school setting matter? Examining associations between school and classroom settings and health behaviours among students in Ontario
Bibliographic record
Abstract
BACKGROUND: Low rates of physical activity and high rates of cannabis use among adolescents are concerning given the health outcomes associated with these health behaviours. Although individual-level characteristics are strongly associated with physical activity and cannabis use, research also suggests that the school setting may influence these health behaviours. PURPOSE: To: 1) Determine the extent to which students’ cannabis use and physical activity vary across school and classroom settings, 2) Identify characteristics of the school setting that are associated with cannabis use and physical activity among students in grades 6-12 in Ontario. METHODS: This study used data from the School Mental Health Surveys, a cross-sectional school-based survey of 31,124 students, 3,373 teachers, and 206 principals from 248 schools across Ontario. Multilevel modelling was used to explore school and class effects while controlling for student compositional effects. RESULTS: About 5% of the variability in student physical activity and 14% of the variability in student cannabis use was attributable to between school and classroom differences. Students’ perception of school climate was positively associated with physical activity, and negatively associated with cannabis use. CONCLUSIONS: Findings highlight the potential influence of the school and classroom environment on students’ physical activity and cannabis use. School and classroom contexts may be important environments for targeted interventions, policies and programs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".