Self-harm in adolescence and risk of crash: a 13-year cohort study of novice drivers in New South Wales, Australia
Bibliographic record
Abstract
INTRODUCTION: Self-harm and suicide are leading causes of morbidity and death for young people, worldwide. Previous research has identified self-harm is a risk factor for vehicle crashes, however, there is a lack of long-term crash data post licensing that investigates this relationship. We aimed to determine whether adolescent self-harm persists as crash risk factor in adulthood. METHODS: We followed 20 806 newly licensed adolescent and young adult drivers in the DRIVE prospective cohort for 13 years to examine whether self-harm was a risk factor for vehicle crashes. The association between self-harm and crash was analysed using cumulative incidence curves investigating time to first crash and quantified using negative binominal regression models adjusted for driver demographics and conventional crash risk factors. RESULTS: Adolescents who reported self-harm at baseline were at increased risk of crashes 13 years later than those reporting no self-harm (relative risk (RR) 1.29: 95% CI 1.14 to 1.47). This risk remained after controlling for driver experience, demographic characteristics and known risk factors for crashes, including alcohol use and risk taking behaviour (RR 1.23: 95% CI 1.08 to 1.39). Sensation seeking had an additive effect on the association between self-harm and single-vehicle crashes (relative excess risk due to interaction 0.87: 95% CI 0.07 to 1.67), but not for other types of crashes. DISCUSSION: Our findings add to the growing body of evidence that self-harm during adolescence predicts a range of poorer health outcomes, including motor vehicle crash risks that warrant further investigation and consideration in road safety interventions. Complex interventions addressing self-harm in adolescence, as well as road safety and substance use, are critical for preventing health harming behaviours across the life course.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".