3E.002 Identifying modifiable factors related to novice driver fault in motor vehicle collisions
Bibliographic record
Abstract
Background Motor vehicle collision is a leading cause of injury and mortality in teens. Graduated drivers licensing (GDL) is a common practice to help mitigate risk associated with younger and inexperienced drivers. However, gaps and inconsistencies exist across regions in how restrictive GDL rules are. Methods This study used police collision report data from Alberta, Canada for the years 2010–2016. An automated, previously validated, culpability analysis tool was applied to collisions involving drivers between 16 and 19 years of age to score fault. Factors that increase odds of fault in all-collisions were identified using logistic regression. Results There were 45,938 motor vehicle collisions involving young drivers. of these, approximately 71% of young drivers were identified as at-fault. Crude analyses indicate that driving between 2300 hrs and 600 hrs increase odds of being at-fault (OR= 1.39; 95% CI: 1.27–1.51). Odds of being at-fault in collision were lower with the presence of an adult passenger over 20 years of age (OR= 0.62; 95% CI: 0.57–0.67) or a single peer of similar age (OR= 0.90; 95% CI: 0.83–0.97). Other passenger categories (younger passenger or multiple teens) were not significantly associated with young driver culpability. Conclusion Passenger type and time of day may both be contributing to young driver fault in collisions. Future directions include multivariable analysis as well as analysis on teen driver fault in severe injury collisions. Learning Outcomes There exists a potential opportunity for policy regulations that may modify or reduce exposure to factors contributing to teen driver culpability in motor vehicle collisions.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.132 | 0.008 |
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".