Risky driving behaviors at school drop-off across Canadian municipalities: Findings from the Child Active Transportation Safety and the Environment (CHASE) study
Bibliographic record
Abstract
OBJECTIVES: School drop-off times can result in high volumes of motor vehicle traffic and drivers may engage in risky behaviors that may put children at risk and inhibit active school transportation. The study objective is to examine the association between features of the roadway physical environment and the number of different risky driving behavior types around selected elementary schools in seven Canadian municipalities during morning drop-off times. METHODS: In this descriptive study research assistants in seven Canadian municipalities coded whether or not (yes/no) any of 9 types of risky driving behaviors were observed during morning school drop-off times and active transportation counts at 552 elementary schools. Proportions of driving behaviors, overall and by type, were analyzed for each municipality. Schools in the highest decile and lowest decile of risky driving behaviors types were contrasted by features of the physical environment and presence of school crossing guards. RESULTS: Risky driving behaviors were observed at 98% of the 552 schools. The most commonly observed behavior was dropping child off on the opposite side of the street with the child subsequently crossing midblock with no traffic controls (80%). Schools with the fewest types of risky behaviors observed had more favorable Active Living Environment scores, curb extensions present more frequently, more restricted parking signage, more access to school entrances, and had child crossing guards less often. CONCLUSIONS: The occurrence of risky driving behaviors is unacceptably high, with some inter-city differences observed. Understanding the extent and type of risky driving behaviors, as well as the environment in which these behaviors occur, can inform school, school board, and municipal interventions to reduce such actions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".