Bibliographic record
Abstract
Roadside surveys have been conducted periodically in British Columbia, Canada since 1995 as a surveillance tool to gather reliable and valid estimates of the prevalence of alcohol use by nighttime drivers. With concerns about the consequences of driving while under the influence of drugs coming to the forefront of public attention, the roadside survey conducted in 2008 was the first to introduce drugs into the testing protocol. Two subsequent surveys have also included drug testing. Using the combined data from three roadside surveys (conducted in 2008, 2010 and 2012), we examined the characteristics of drivers who tested positive for drugs and the circumstances under which the behaviour occurred. The method for all three roadside surveys followed a standard protocol. There were a total of 4711 drivers that voluntarily participated and provided both an oral fluid sample and a breath sample. It was found that 3928 (83.4 per cent) were negative for both drugs and alcohol, 382 (8.1 per cent) were positive for drugs only, 320 (6.8 per cent) were positive for alcohol only and 81 (1.7 per cent) were positive for both drugs and alcohol. Results indicate that the characteristics of drug-drivers and the patterns of drug use by drivers differed from the well-known patterns of drinking and driving. The most common drug detected was cannabis followed by cocaine. This information makes a vital contribution to the development of effective enforcement, public education and awareness programs.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".