Adaptation of a Canadian culpability scoring tool to Alberta police traffic collision report data
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".