School environments and social risk factors for child pedestrian-motor vehicle collisions: A case-control study
Bibliographic record
Abstract
BACKGROUND: Child pedestrian-motor vehicle collisions (PMVCs) have decreased in Canada in the past 20 years. Many believe this trend is explained by the rise in automobile use for all travel. Initiatives to increase walking to school need to consider PMVC risk. Potential risk factors related to walking to school, the built environment and social factors were examined for schools with historically high child PMVC rates. METHODS: Child PMVCs (age 4-12 years) from 2000 to 2013 and built environment features were mapped within school attendance boundaries in the City of Toronto, Canada. Case and control schools were in the highest and lowest PMVC quartiles respectively. Observational counts of travel mode to school were conducted. Logistic regression evaluated walking to school, built environment and social risk factors for higher PMVC rates, stratified by geographic location (downtown vs. inner suburbs). RESULTS: The mean PMVC rates were 18.8/10,000/year (cases) and 2.5/10,000/year (controls). One-way street density (OR=4.00), school crossing guard presence (OR=3.65) and higher social disadvantage (OR=1.37) were associated with higher PMVCs. Higher residential land use density had a protective effect (OR=0.56). More walking was not a risk factor. While several built environment risk factors were identified for the inner suburbs; only social disadvantage was a risk factor within older urban neighbourhoods. CONCLUSIONS: Several modifiable environmental risk factors were identified for child PMVCs. More walking to school was not associated with increased PMVCs after controlling for the environment. School social disadvantage was associated with higher PMVCs with differences by geographic location. These results have important implications for the design of roadways 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".