Self-harm and risk of motor vehicle crashes among young drivers: findings from the DRIVE Study
Bibliographic record
Abstract
BACKGROUND: Some motor vehicle crashes, particularly single-vehicle crashes, may result from intentional self-harm. We conducted a prospective cohort study to assess the risk that intentional self-harm poses for motor vehicle crashes among young drivers. METHODS: We prospectively linked survey data from newly licensed drivers aged 17-24 years to data on licensing attempts and police-reported motor vehicle crashes during the follow-up period. We investigated the role of recent engagement in self-harm on the risk of a crash. We took into account potential confounders, including number of hours of driving per week, psychological symptoms and substance abuse. RESULTS: We included 18 871 drivers who participated in the DRIVE Study for whom data on self-harm and motor vehicle crashes were available. The mean follow-up was 2 years. Overall, 1495 drivers had 1 or more crashes during the follow-up period. A total of 871 drivers (4.6%) reported that they had engaged in self-harm in the year before the survey. These drivers were at significantly increased risk of a motor vehicle crash compared with drivers who reported no self-harm (relative risk [RR] 1.42, 95% confidence interval [CI] 1.15-1.76). The risk remained significant, even after adjustment for age, sex, average hours of driving per week, previous crash, psychological distress, duration of sleep, risky driving behaviour, substance use, remoteness of residence and socio-economic status (RR 1.37, 95% CI 1.09-1.72). Most of the drivers who reported self-harm and had a subsequent crash were involved in a multiple-vehicle crash (84.1% [74/88]). INTERPRETATION: Engagement in self-harm behaviour was an independent risk factor for subsequent motor vehicle crash among young drivers, with most crashes involving multiple vehicles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".