PW 1753 Child pedestrian safety: individual and environmental correlates of interactions with vehicles at street crossings around schools and parks
Bibliographic record
Abstract
Much attention in the scientific literature has been paid lately to the road around schools when studying child pedestrian injury risk. However, little research and much less effort to prevent injuries has been done regarding other frequent children’s destinations such as parks. Road insecurity might be a good reason to avoid them or drive instead of walk. The goals of this study are 1) to assess the occurrence of interactions between child pedestrians and vehicles based on observations of individual and crossing characteristics and 2) to compare the prevalence and the characteristics of the interactions near schools and parks. Observations of children’s behaviors while crossing around schools (n=869) and parks (n=731) were recorded at 11 schools and 4 parks in Montréal, Canada. Environmental characteristics were recorded for 14 school and 17 park crosswalks with a minimum of 40 observations for each crosswalk. For recorded interactions, information was also collected to characterize the behaviors of involved parties. First, chi-square tests reveal the differences of interaction characteristics at school and park crossings. A mixed-effect logit regression model was then performed to identify factors associated with interactions while ensuring that the fixed effects of the crosswalks characteristics are treated as such. Much fewer interactions were recorded around schools (7.13%) than parks (18.33%) and these interactions were posing less risk to the children safety (more careful behaviors from drivers/cyclists). Our results point out the need to adapt the crossing environment to children’s capacity since most of them already respect the rules. Providing safety near parks, combined with safe routes to other destinations, can promote active transportation and livable neighborhoods for all.
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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".