A comparison of the prevalence of cannabis and alcohol use among drivers and passengers in British Columbia and Ontario, Canada
Bibliographic record
Abstract
• A higher percentage of motor vehicle passengers than drivers had used alcohol. • No differences between passengers and drivers were observed for THC. • Alcohol detection was higher in drivers from Ontario than in drivers from BC. • THC detection was lower in drivers from Ontario than in drivers from BC. • These findings may be due to differences in traffic policy or societal norms. Similar to drink driving, the prevalence of driving under the influence of cannabis (DUIC) is expected to depend on the availability and cost of cannabis which would impact cannabis use in both drivers and passengers, and factors that specifically target cannabis use in drivers such as the deterrent effect of traffic laws and driver’s opinion about the risks and acceptability of DUIC. To disentangle these effects, we aimed to compare the prevalence of alcohol and tetrahydrocannabinol (THC) detection 1) in drivers vs. passengers involved in motor vehicle accidents and 2) in drivers and passengers from BC vs. Ontario. Chart review and toxicology data from an ongoing prospective study of moderately injured motor vehicle occupants were analyzed. Log-binomial regression models were used to obtain prevalence ratios (PRs). This manuscript reports on data from 3004 drivers and 941 passengers. Approximately half (55.1%) were male, and the mean (SD) age was 43.8 (19.1) years. Alcohol and THC detection prevalence was 14.2% and 12.4%, respectively. Passengers had higher prevalence of alcohol than drivers (aPR [95% CI]: 1.22 [1.06, 1.40]). No difference in THC prevalence was observed between drivers and passengers. Ontario drivers had higher prevalence of alcohol detection than BC drivers (aPR [95% CI]: 1.33 [1.13, 1.58]) but lower prevalence of THC detection (aPR [95% CI]: 0.80 [0.64, 0.99]). Among passengers, no significant interprovincial differences were observed for alcohol or THC detection. These findings may be partially explained by differences in provincial traffic laws, public opinion, and overall consumption rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".