Development and Validation of a Crash Culpability Scoring Tool
Notice bibliographique
Résumé
OBJECTIVE: Several traffic safety research techniques require researchers to separate crash-involved drivers into culpable and nonculpable. Nonculpable drivers are assumed to be randomly involved in crashes by external factors and to approximate a noncollision control population. If this is true, factors that increase crash risk should be found more often in culpable than in nonculpable drivers. Though a culpability scoring tool has been developed for research purposes, that tool does not adequately address winter driving conditions (Robertson and Drummer 1994). Moreover, traditional culpability scoring requires assessors to read and score individual collision reports. The purpose of this study is to develop and validate an automated, rule-based Canadian culpability scoring tool that is capable of rapidly scoring police crash reports from large administrative datasets. METHODS: We used an iterative approach to develop and validate our tool. First, the Robertson-Drummer culpability scoring tool was modified to include the extensive police report data collected in the British Columbia Traffic Accident System (TAS) and to account for winter driving conditions. This was done in consultation with traffic safety experts. The scoring tool was automated, employing a rule-based decision model that avoids interpretation of free-text reports. The scoring tool was applied to 73 collisions (134 drivers). Two experts also reviewed these collisions and determined the culpability of each driver. Discrepant cases were discussed to understand why the scoring tool differed from the expert assessment and the scoring tool was modified accordingly. The final tool was compared with expert assessment on another sample of 96 crashes. The tool was also applied to a sample of 2086 crash-involved drivers with known blood alcohol concentrations (BACs) and the adjusted odds of culpability were calculated for several BAC ranges. RESULTS: The final scoring tool included 7 factors and had content validity for traffic safety experts. It had excellent agreement with expert scoring on the first set of collisions (kappa = 0.83, 95% confidence interval [CI]: 0.75-0.91) and on the second set (kappa = 0.84, 95% CI: 0.77-0.92). When applied to crash-involved drivers with known BAC levels, the scoring tool exhibited predictive validity: the odds of culpability increased with higher BACs, consistent with the known dose effect of BAC on crash risk. CONCLUSIONS: We have developed an automated culpability scoring tool contextualized to Canadian driving conditions. This tool will allow road safety researchers to assess collision responsibility in large administrative data sets derived from police reports.
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 ».