Adaptation of a Canadian culpability scoring tool to Alberta police traffic collision report data
Notice bibliographique
Résumé
Tona M. Pitta, Janet Aucoina, Alberto Nettel-Aguirreabc, Gavin R. McCormackad, Andrew W. Howardef, Paul Graffg, Brian H. Roweh & Brent E. Hagelabc*a Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; b Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; c Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada; d Faculty of Environmental Design, University of Calgary, Calgary, Alberta, Canada; e Department of Surgery, University of Toronto, Toronto, Ontario, Canada; f Department of Health Policy, Management & Evaluation, University of Toronto, Toronto, Ontario, Canada; g Alberta Transportation, Alberta, Canada; h Department of Emergency Medicine and School of Public Health, University of Alberta, Edmonton, Alberta, CanadaAssociate Editor Allison E. Curry oversaw the review of this article.Supplemental material for this article can be accessed on the publisher’s website.CONTACT Brent E. Hagel brent.hagel@albertahealthservices.ca Alberta Children’s Hospital, 2888 Shaganappi Trail NW, Calgary, AB T2B 6A8, Canada.AbstractObjective: The objective of this study was to adapt a previously validated Canadian Culpability Scoring Tool (CCST) to Alberta police report data.Methods: Police traffic collision reports from motor vehicle (MV) collisions in Calgary and Edmonton (Alberta, Canada) from 2010 to 2014 were used. Adaptation of the CCST was completed with input from personnel within Alberta Transportation, contributing to face and content validity. Two research assistants, given only the information necessary for scoring, evaluated 175 randomly selected MV–MV collisions. Interrater agreement was estimated using kappa (k) and reported with 95% confidence intervals (CIs). Discussion of disagreements between the research assistants and consultation from Alberta Transportation informed the algorithm used in the Alberta Motor Vehicle Collision Culpability Tool (AMVCCT). The AMVCCT was automated and applied to all motorists involved in collisions. Binary logistic regression was used to examine characteristics of the culpable and nonculpable drivers and their effects were reported using odds ratios (ORs) with 95% CIs.Results: Interrater agreement for the random sample was excellent (k = 0.95; 95% CI, 0.92–0.99). Of those drivers hospitalized, 1,130 (37.54%) were rated not culpable and 1,880 (62.46%) were rated culpable. The odds of being culpable were higher for males than for females (OR = 1.43; 95% CI, 1.23–1.66). The odds of being culpable were higher in those impaired by alcohol than those considered “apparently normal” (OR = 61.10; 95% CI, 22.66–164.75). The odds of being deemed culpable, when compared with drivers >54 years old, were higher for those <25 years old (OR = 1.72; 95% CI, 1.35–2.20) and lower for those in the 40- to 54-year-old age group (OR = 0.78; 95% CI, 0.63–0.96). Driving between 12 a.m. and 6 a.m. resulted in higher odds of being culpable compare with all other 6-h time blocks. Direction and statistical significance remained consistent when applying the tool to all MV collisions. Sensitivity analysis including the removal of single vehicle collisions did not affect the direction or statistical significance of the main results.Conclusions: The AMVCCT identified a culpable group that exhibited characteristics expected in drivers who are at fault in collisions. The age groups 25–39 and 40–54 demonstrated different results than the CCST. However, this is the only difference that exists in the findings of the AMVCCT compared to the CCST and could exist due to differences between the driving populations in Alberta and British Columbia. It is possible to adapt the CCST to provinces outside British Columbia and, in doing so, we can identify risk factors for collision contribution and not-at-fault drivers who represent the driving population.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,013 | 0,010 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».