Dangerous student car drop-off behaviors and child pedestrian–motor vehicle collisions: An observational study
Notice bibliographique
Résumé
OBJECTIVES: The objective of this study was to examine the association between dangerous student car drop-off behaviors and historical child pedestrian-motor vehicle collisions (PMVCs) near elementary schools in Toronto, Canada. METHODS: Police-reported child PMVCs during school travel times from 2000 to 2011 were mapped within 200 m of 118 elementary schools. Observers measured dangerous student morning car drop-off behaviors and number of children walking to school during one day in 2011. A composite score of school social disadvantage was obtained from the Toronto District School Board. Built environment and traffic features were mapped and included as covariates. A multivariate Poisson regression was used to model the rates of PMVC/number of children walking and dangerous student car drop-off behaviors, adjusting for the built environment and social disadvantage. RESULTS: There were 45 child PMVCs, with 29 (64%) sustaining minor injuries resulting in emergency department visits. The mean collision rate was 2.9/10,000 children walking/year (SD = 6.7). Dangerous drop-off behaviors were observed in 104 schools (88%). In the multivariate analysis, each additional dangerous drop-off behavior was associated with a 45% increase in collision rates (incident rate ratio [IRR] = 1.45, 95% confidence interval [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 student car drop-off behaviors were associated with historical nonfatal child PMVC rates during school travel times near schools. Some caution must be taken in interpreting these results due small number of events and limitations in the data collection, because collision data were collected historically over a 12-year period, whereas driving behavior was only observed on a single day in 2011. Targeted multifaceted intervention approaches related to the built environment, enforcement, and education could address dangerous drop-off behaviors near schools to reduce child PMVCs and promote safe walking to school.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».