A Roadside Survey of Alcohol and Drug Use Among Drivers in British Columbia
Bibliographic record
Abstract
OBJECTIVE: The purpose was to determine the prevalence of alcohol and drug use among a random sample of nighttime drivers. METHODS: Drivers were randomly selected from the traffic stream in three cities in British Columbia and asked to provide a breath sample to determine alcohol content and a sample of oral fluid to be tested for the presence of psychoactive drugs. The survey was conducted between the hours of 9:00 p.m. and 03:00 a.m. on Wednesday through Saturday nights in June 2008. RESULTS: Of the 1533 vehicles selected, 89 percent of drivers provided a breath sample and 78 percent provided a sample of oral fluid. Key findings include: 10.4 percent of drivers tested positive for drug use. 8.1 percent of drivers had been drinking. 15.5 percent of drivers tested positive for alcohol, drugs, or both. Cannabis and cocaine were the drugs most frequently detected in drivers. Alcohol use among drivers was most common on weekends and during late-night hours; drug use was more evenly distributed across all survey nights and times. Alcohol use was most common among drivers aged 19 to 24 and 25 to 34; drug use was more evenly distributed across all age groups. Though driving after drinking has decreased substantially since previous surveys, the number of drivers with elevated alcohol levels (over 80 mg/dL) was higher than in the past. CONCLUSIONS: The finding that drug use is more common than alcohol use among drivers highlights the need for a unique and separate societal response to the use of drugs by drivers commensurate with the extent of safety risks posed to road users. The observed differences between driving after drug use and driving after drinking have implications for enforcement and prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".