Examining the Link Between Drinking-Driving and Depressed Mood
Bibliographic record
Abstract
OBJECTIVE: Because both alcohol and depressed mood exert deleterious effects on psychomotor performance, the possibility that people with depressed mood may be more likely to drive after drinking may have important implications for traffic safety. In this work, we examine the association between depressed mood and self-reported driving after drinking in a large representative sample of adults in Ontario. METHOD: Data are based on the 2001-2004 Centre for Addiction and Mental Health Monitor, an ongoing cross-sectional telephone survey of Ontario adults ages 18 and older (N=3,979). Logistic regression analysis was performed to identify the risk of driving after drinking two or more drinks in the previous hour within the past 12 months associated with scores on a screening measure of depressed mood (depression-anxiety and social functioning subscales of the 12-item General Health Questionnaire), while controlling for alcohol-use measures (weekly volume and frequency of heavy drinking), driving exposure, and demographic factors. RESULTS: Logistic regression analysis revealed that the odds of reporting driving after drinking within the past year increase significantly as depressed mood (specifically, depression-anxiety scores) increases. CONCLUSIONS: Additional research on the nature of the link between depressed mood and impaired driving should be undertaken, including assessing whether there exists any synergistic effects of depressed mood and alcohol on collision risk and considering the implications of this relationship for prevention and remedial activities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".