The relationship between walking to school and child pedestrian injury in Toronto, Canada
Bibliographic record
Abstract
Background Pedestrian collisions are a major cause of injury in Canadian children. Parents indicate that traffic injury risk is a leading reason why children are not encouraged to walk to school. Promoting walking to school must take injury risk into consideration. Previous studies investigating walking to school have generally relied on parent report, and have not included pedestrian injury data. Objective To determine the relationship between observed numbers of children walking to school and collision rates at elementary schools. Methods Observational counts were conducted of elementary school transportation modes to school in Toronto, Canada. Pedestrian-motor vehicle collision data from 2000–2009 were obtained. Negative binomial regression was used to model rates of child pedestrian injury within school attendance boundaries with observed proportion of children walking to school. Results There were 441 collisions in 118 study school boundaries. A total of 16 137 children were observed walking. The mean collision rate was 78/1000/child years. The mean proportion of walking to school was 67%. Walking to school was a significant predictor of collisions (4.05, 95% CI 1.22 to 13.43). Significance The proportion of children arriving to school using AST was higher and demonstrated greater variability than expected. Proportion of exposure to traffic with children walking to school was positively significantly associated with rate of child pedestrian collision. Increasing the numbers of children walking to school could have the undesired effect of increasing pedestrian injury rates, if efforts are not made to enhance pedestrian safety around schools.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".