A cross-sectional examination of school characteristics associated with overweight and obesity among grade 1 to 4 students
Bibliographic record
Abstract
BACKGROUND: Excessive weight gain among youth is an ongoing public health concern. Despite evidence linking both policies and the built environment to adolescent and adult overweight, the association between health policies or the built environment and overweight are often overlooked in research with children. The purpose of this study was to examine if school-based physical activity policies and the built environment surrounding a school are associated with weight status among children. METHODS: Objectively measured height and weight data were available for 2,331 grade 1 to 4 students (aged 6 to 9 years) attending 30 elementary schools in Ontario, Canada. Student-level data were collected using parent reports and the PLAY-On questionnaire administered to students by study nurses. School-level policy data were collected from school administrators using the Physical Activity Module of the Healthy School Planner tool, and built environment data were provided by the Enhanced Points of Interest data resource. Multi-level logistic regression models were used to examine the school- and student-level characteristics associated with the odds of a student being overweight or obese. RESULTS: There was significant between-school random variation in the odds of a student being overweight [σ²(μ0) = 0.274(0.106), p < 0.001], but not for being obese [σ²(μ0) = 0.115(0.089)]. If a student attended a school that provided student access to a variety of facilities on and off school grounds during school hours or supported active transportation to and from school, he/she was less likely to overweight than a similar student attending a school without these policies. Characteristics of the built environment were not associated with overweight or obesity among this large cross-sectional sample of children. CONCLUSIONS: This new evidence suggests that it may be wise to target obesity prevention efforts to schools that do not provide student access to recreation facilities during school hours or schools that do not support active transportation for students. Future research should evaluate if school-based overweight and obesity prevention programming might be improved if interventions selectively targeted the school characteristics that are putting students at the greatest risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".