Influence of social and built environment features on children walking to school: An observational study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To estimate the proportion of children living within walking distance who walk to school in Toronto, Canada and identify built and social environmental correlates of walking. METHODS: Observational counts of school travel mode were done in 2011, at 118 elementary schools. Built environment data were obtained from municipal sources and school field audits and mapped onto school attendance boundaries. The influence of social and built environmental features on walking counts was analyzed using negative binomial regression. RESULTS: The mean proportion observed walking was 67% (standard deviation=14.0). Child population (incidence rate ratio (IRR) 1.36), pedestrian crossover (IRR 1.32), traffic light (IRR 1.19), and intersection densities (IRR 1.03), school crossing guard (IRR 1.14) and primary language other than English (IRR 1.20) were positively correlated with walking. Crossing guard presence reduced the influence of other features on walking. CONCLUSIONS: This is the first large observational study examining school travel mode and the environment. Walking proportions were higher than those previously reported in Toronto, with large variability. Associations between population density and several roadway design features and walking were confirmed. School crossing guards may override the influence of roadway features on walking. Results have important implications for policies regarding walking promotion.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it