Examining the predictive potential of depressed mood and alcohol misuse on risky driving
Bibliographic record
Abstract
AIMS: Male driving while impaired (DWI) offenders are at heightened risk for engaging in risky driving. Males in a depressed mood are also more prone to alcohol misuse, which may further contribute to risky driving. This manuscript investigates the predictive potential of combined depressed mood and alcohol misuse on risky driving outcomes 3 and 9 years after baseline in male DWI offenders. METHODS: At baseline, participants completed questionnaires assessing depressed mood (Major Depression scale of the Millon Clinical Multiaxial Inventory-III), alcohol misuse (Alcohol Use Disorders Identification Test), and sensation-seeking (Sensation Seeking Scale-V). Risky driving data (Analyse des comportements routiers; ACR3) were collected at follow-up 3 years after baseline. Driving offence data were obtained for 9 years after baseline. RESULTS: There were 129 participants. As 50.4% of the sample were missing ACR3 scores, multiple imputation was conducted. In the final regression model, R2 = 0.34, F(7,121) = 8.76, P < 0.001, alcohol misuse significantly predicted ACR3, B = 0.56, t = 1.96, P = 0.05. Depressed mood, however, did not significantly predict ACR3 and sensation-seeking was not a significant moderator. Although the regression model predicting risky driving offences at Year 9 was significant R2 = 0.37, F(10,108) = 6.41, P < 0.001, neither depressed mood nor alcohol misuse was a significant predictor. CONCLUSIONS: These findings identify alcohol misuse as a predictor of risky driving 3 years after baseline among male DWI offenders. This enhances our prediction of risky driving, extending beyond the widely researched acute impacts of alcohol by exploring chronic patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".