Active school transportation and the built environment across Canadian cities: Findings from the child active transportation safety and the environment (CHASE) study
Bibliographic record
Abstract
Walking and bicycling to school (active school transportation, AST) has been in decline for decades in North America and globally with the rise of automobility. This cross-sectional study estimated associations between the built environment and AST in seven Canadian communities. We observed the travel behaviours of almost 118,000 students at 552 schools. Using beta regression, we modeled the proportion of children using AST, considering built environment and social environment factors around schools. Across all schools, the average proportion of children using AST was 54.3% (SD 18.9%), with variability among cities from a low of 39.5% (SD 22.1%) in Laval, Quebec to 69.7% (SD 18.1%) in Montreal, Quebec. Overall, several modifiable road design features were associated with AST, including the presence of school crossing guards, cycling infrastructure, Walk Score® and traffic signal density. There was variability in the directionality and statistical significance of associations with design variables across cities, suggesting that the local context and directed local interventions are important to support AST. Natural experiment studies are necessary to examine local approaches related to the built environment to increase AST and ensure appropriate new policy and program interventions are developed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.005 | 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) | 0.002 | 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".