Driving behaviours near schools and child pedestrian-motor vehicle collisions in Toronto, Canada
Bibliographic record
Abstract
Background The burden of child pedestrian motor vehicle collisions remains high world-wide. Most of children's exposure to traffic is while walking to school with active school transportation being promoted as an important source of physical activity. Although dangerous driving behaviours have been reported extensively near schools, their relationship with child pedestrian-motor vehicle collisions (PMVC) has not been defined. The purpose of the study was to examine the correlation between dangerous driving behaviours and historical child PMVC near elementary schools in Toronto, Canada. Methods Police-reported child PMVC (ages 4–12) from 2000–2011 during school travel times were mapped within 200 m of 118 schools. Observers measured dangerous driving and numbers of children walking to school during morning drop-off on a single day in 2011. A composite score of school social disadvantage was obtained from the school board. Built environment features were mapped and included as covariates. Multivariate Poisson regression was used to model the rates of PMVCs and dangerous driving, adjusted for the built environment and social disadvantage. Results There were 45 child PMVCs with 29 (64%) sustaining minor injuries resulting in emergency department visits. Dangerous driving behaviours were observed in 104 schools (88%). Each additional dangerous driving behaviour was associated with a 45% increase in collision rates (IRR = 1.45, 95% CI 1.02, 2.07). Higher speed roads (IRR = 1.27, 95% CI 1.13, 1.44) and social disadvantage (IRR = 2.99, 95% CI 1.03, 8.68) were associated with higher collision rates. Conclusions Dangerous driving was correlated with historical non-fatal child PMVC rates near schools with the most common behaviours related to unsafe parking and drop-offs. The results have important public health implications and have had impacts on City of Toronto and school board policies related to safe walking to school Key messages Dangerous driving behaviours were correlated with historical child pedestrian collisions near schools during school travel times controlling for higher speed roads and school social disadvantage Targeted multifaceted interventions must be developed to address dangerous driving behaviours to reduce child pedestrian collisions and promote safe walking to school
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".