Characteristics of people who report both driving after drinking and driving after cannabis use
Bibliographic record
Abstract
After alcohol, cannabis is the recreational drug most often found among dead or injured drivers. While the effects of alcohol on driving risks are well-described, the effects of cannabis on driving risks are less well understood. We have observed, in survey data, that the chances of past year collisions among drivers who report driving after drinking alcohol (DUIA) are significantly increased, and similarly that the chances of past year collisions among drivers who report driving after cannabis use (DUIC) are also significantly increased. Recently, we examined drivers who reported both DUIA and DUIC (DUIC+A) and found that the past-year collision risk in this group, at 30.5 per cent, was 2-4 times that seen among drivers who reported either behaviour by itself. This DUIA+C group may be an important risk group, and the purpose of this study was to explore factors that might distinguish this group from other drivers. Data were derived from the CAMH Monitor, an ongoing population survey of Ontario adults (18 years and older). Preliminary analyses reveal important differences by age group, with younger drivers being significantly more likely to report DUIA, DUIC and DUIA+C. Drivers who reported any DUIC were also more likely to report DUIA+C than drivers who reported any DUIA. Our analyses provide further confirmation that individuals who fall in this DUIA+C group are an important group from road safety perspectives. Further analyses will consider the potential impact of frequency of substance use, substance related problems, and indicators of mental health problems on the likelihood of an individual being in the DUIA+C group.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".