Identifying modifiable factors related to novice adolescent driver fault in motor vehicle collisions
Bibliographic record
Abstract
Novice adolescent drivers have a higher propensity to engage in risky driving and are at higher odds of being involved in collisions. Graduated driver licensing programs have been instituted to help novice drivers gain experience while avoiding higher risk driving circumstances. This study examines modifiable risk factors contributing to novice adolescent driver fault in collisions. Police traffic collision report data from municipalities in Alberta for the years 2010–2016, inclusive, were used. Fault in collision was assigned using an automated and previously validated tool for assigning culpability. Factors contributing to novice adolescent (16-19 years of age) fault in collision were examined using multivariable logistic regression. Novice adolescent drivers had higher adjusted odds ratios (aOR) of being at-fault in collision when driving from 01:00-05:00 (aOR = 1.38; 95% Confidence Interval [CI]: 1.26-1.50). Novice adolescent drivers had lower odds of fault when driving with an adult (aOR= 0.62; 95% CI: 0.57-0.68) or a single peer (aOR= 0.87; 95% CI: 0.80-0.94), but higher odds of causing a severe collision with a single peer present (aOR= 2.23; 95% CI: 1.21-4.11). Impairment of the teen driver was reported in 25% of all fatal collisions, and 40% of late-night fatal collisions. The findings support policies that allow driving with a single adult or peer passenger during daytime hours. Driving during late-night hours should be restricted for novice adolescent drivers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".