The association between overweight and school policies on physical activity: a multilevel analysis among elementary school youth in the PLAY-On study
Bibliographic record
Abstract
The objective is to examine school-level program and policy characteristics and student-level behavioural characteristics associated with being overweight. Multilevel logistic regression analysis were used to examine the school- and student-level characteristics associated with the odds of a student being overweight among 1264 Grade 5-8 students attending 30 elementary schools in Ontario, Canada. Data were derived from the Physical Activity of Youth in Ontario Schools host study. Significant between-school random variation in overweight was identified [σ²(μ0)=0:187 (0:084), P < 0.001]; school-level differences accounted for 5.4% of the variability in the odds of a student being overweight versus a normal weight. A student attending a school that was in the action phase for the school-level construct 'Availability and use of interschool programs' was significantly less likely to be overweight than a similar student attending a school that was in the initiation phase for this construct. Important student-level characteristics included physical activity (PA) and gender. Developing a better understanding of the school- and student-level characteristics associated with overweight among youth is critical for informing school-based prevention policies. Future research should evaluate if implementing and promoting interschool PA programming, and which types of interschool activities, are effective in preventing or reducing overweight.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".