Are Environmental Influences on Physical Activity Distinct for Urban, Suburban, and Rural Schools? A Multilevel Study Among Secondary School Students in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: This study examined differences in students' time spent in physical activity (PA) across secondary schools in rural, suburban, and urban environments and identified the environment-level factors associated with these between school differences in students' PA. METHODS: Multilevel linear regression analyses were used to examine the environment- and student-level characteristics associated with time spent in PA among grades 9 to 12 students attending 76 secondary schools in Ontario, Canada, as part of the SHAPES-Ontario study. This approach was first conducted with the full data set testing for interactions between environment-level factors and school location. Then, school-location specific regression models were run separately. RESULTS: Statistically significant between-school variation was identified among students attending urban (σ(2) μ0 = 8959.63 [372.46]), suburban (σ(2) μ0 = 8918.75 [186.20]), and rural (σ(2) μ0 = 9403.17 [203.69]) schools, where school-level differences accounted for 4.0%, 2.0%, and 2.1% of the variability in students' time spent in PA, respectively. Students attending an urban or suburban school that provided another room for PA or was located within close proximity to a shopping mall or fast food outlet spent more time in PA. CONCLUSION: Students' time spent in PA varies by school location and some features of the school environment have a different impact on students' time spent in PA by school location. Developing a better understanding of the environment-level characteristics associated with students' time spent in PA by school location may help public health and planning experts to tailor school programs and policies to the needs of students in different locations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".