The relationship between school physical activity policy and objectively measured physical activity of elementary school students: a multilevel model analysis
Bibliographic record
Abstract
BACKGROUND: There is evidence of school level variability in the physical activity of children and youth. Less is known about factors that may contribute to this variation. The purpose of this study was to examine if the school health environment (Healthy Physical Environment, Instruction and Programs, Supportive Social Environment, and Community Partnerships) is associated with objectively measured time spent in light to vigorous physical activity among a sample of Toronto children. METHODS: The sample comprised 856 grade 5 and 6 students from 18 elementary schools in Toronto, Ontario. Multilevel linear regression analyses were used to examine the impact of school physical activity policy on students' time spent in light-to-vigorous physical activity. RESULTS: Significant between-school random variation in objectively measured time spent in light-to-vigorous physical activity was identified [σ(2)μ0 = 0.067; p < 0.001]; school-level differences accounted for 6.7% of the variability in the time individual students spent in light-to-vigorous physical activity. Of the 22 school-level variables, students attending schools with support for active transportation to/from school and written policies/practices for physical activity, accumulated significantly more minutes of physical activity per school week than students who attended schools that did not. CONCLUSIONS: School physical activity policy and support for active school travel is associated with objectively measured time spent in light to vigorous physical activity. School physical activity policy might be a critical mechanism through which schools can impact the physical activity levels of their students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".