Identifying motorist characteristics associated with youth bicycle–motor vehicle collisions
Notice bibliographique
Résumé
Objective: The objective of this study was to identify driver characteristics associated with youth bicycle–motor vehicle collisions in Alberta, Canada.Methods: Edmonton and Calgary police collision report data from the years 2010–2014 were used. From these data, motor vehicle collisions involving youth (<18 years old) were identified (cases). The controls were drivers who, over the same period, were involved in separate motor vehicle–only collisions but deemed not at fault using an automated culpability analysis. Control selection used the quasi-induced exposure method, assuming that not-at-fault drivers in collisions are representative of the typical driver (source population). Descriptive statistics, including proportions, medians, and interquartile ranges (as appropriate) were used to describe the characteristics of the case and control drivers. Purposeful variable selection techniques were used to inform multivariable logistic regression models and results are presented as adjusted odds ratios (aORs) and 95% confidence intervals (CIs).Results: Four hundred twenty-three drivers involved in youth bicycle–motor vehicle collisions were identified, as were 243,927 not-at-fault control drivers. Drivers >54 years old had higher odds of involvement in youth bicycle–motor vehicle collisions than drivers between 25 and 39 years old (aOR = 1.37; 95% CI, 1.03, 1.82). Compared to driving between 3:01 p.m. and 6:00 p.m., driving between 12:01 a.m. and 6:00 a.m. (aOR = 0.27; 95% CI, 0.11, 0.66), between 6:01 a.m. and 9:00 a.m. (aOR = 0.61; 95% CI, 0.44, 0.85), or between 9:01 a.m. and 12:00 p.m. (aOR = 0.26; 95% CI, 0.16, 0.41) had lower odds of bicyclist collision, whereas driving between 6:01 p.m. and 12:00 a.m. had higher odds (aOR = 1.34; 95% CI, 1.01, 1.79). Driving a truck/van had lower odds of bicyclist collision compared to driving a passenger car (aOR = 0.67; 95% CI, 0.48, 0.94).Conclusions: Culpability analysis is typically applied to motorists to identify transient exposures; however, this study used culpability analysis to select control drivers who could be compared with drivers involved in youth bicycle–motor vehicle collisions. This study highlights motorist characteristics in youth bicycle–motor vehicle collisions. In doing so, we hope to inform primary prevention strategies for motorists and the environment that will reduce collisions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 ».