Child pedestrian and cyclist injuries, and the built and social environment across Canadian cities: the Child Active Transportation Safety and the Environment Study (CHASE)
Bibliographic record
Abstract
INTRODUCTION: Traffic injury is a leading and preventable cause of child death and disability, with child pedestrians and cyclists particularly vulnerable. Examining built environment correlates of child pedestrian and cyclist motor vehicle collisions (PCMVC) in different settings is needed to promote an evidence-based approach to road safety. METHODS: We conducted a cross-sectional study across multiple urban/suburban environments in Canada (Calgary, Toronto, Montreal, Laval, Peel Region). All public elementary schools were included (n=1030). We examined the role of land use/social environments, road environments and traffic safety interventions on the rates of child PCMVC within 1000 m of schools. Multivariable negative binomial regression was conducted for all cities and by individual city. In a subset of schools (n=389), we examined associations when controlling for active school transportation (AST). RESULTS: Mean PCMVC rate per school ranged from 0.13 collisions/year in Peel to 0.35 in Montreal. Child PCMVC were correlated with land use, social and road environments and traffic safety interventions. In fully adjusted models, social and land use features remained the most important correlates. New immigrant population had the largest positive association with child PCMVC (incidence rate ratio (IRR): 1.26, 95% CI 1.06 to 1.50), while old housing (pre-1960) density was most protective (IRR: 0.83, 95% CI 0.77 to 0.90). AST was associated with PCMVC, but it had no effect on the relationships between PCMVC and other social/environmental correlates. CONCLUSION: The built environment and social factors influence rates of child PCMVC. Opportunities to reduce child PCMVC exist through modifications to city design and road environments and implementing traffic safety interventions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".