Multidimensional school features associated with physical activity among youth at risk of obesity: an exploratory principal component and generalized estimating equation analysis
Bibliographic record
Abstract
BACKGROUND: Schools may be high-leverage points for the promotion of physical activity (PA), yet little is known about school built and social environments among youth at high risk of obesity. PURPOSE: To characterise school built and social environments that may be salient for PA and to examine associations between school PA environments and PA in youth at risk of obesity. METHODS: Data from youth attending 206 schools (314 youth in 2005-2008, and 129 youth in 2008-2010) within the QUALITY cohort study, a longitudinal investigation of youth at high risk of obesity were used. Features of schools, based on built, policy/programming and social environments were identified using principal components (PC) analysis. Gender-stratified generalized estimating equation (GEE) models were used to explore associations between school features and accelerometer measured mean counts per minute (MCPM), mean daily moderate-to-vigorous physical activity (MVPA) and the odds of meeting MVPA guidelines cross-sectionally and prospectively using 90% confidence intervals. RESULTS: Nine PCs were identified. Associations were observed between PA and 7 of the 9 PCs. The social environment seemed to be particularly important. Social Norms to Promote PA was associated with an increase in girls' baseline MCPM and MVPA. High Willingness to Promote PA was associated with boys' MCPM, MVPA, and odds of meeting MVPA guidelines, at both baseline and follow-up. CONCLUSION: School built and social contexts may be associated with PA cross-sectionally and over time. Further studies are necessary to confirm the direction and magnitude of effects and to establish their relevance to school-based health promotion efforts.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".