A comparison of drug use by fatally injured drivers and drivers at risk
Bibliographic record
Abstract
Research has documented the prevalence of drug use among drivers involved in serious crashes. Although the overrepresentation of alcohol among drivers involved in serious crashes has been repeatedly demonstrated in numerous studies, relatively few studies have attempted to determine the magnitude of the risks posed by drivers who have used drugs. The purpose of this study was to examine the extent to which drugs may present a risk to road safety by comparing the prevalence of drug use among drivers at risk and drivers who die in motor vehicle crashes. Data on alcohol and drug use from coronersr and medical examinersr files on drivers of motor vehicles who died in crashes were compared with data on drug use among drivers who participated in roadside surveys in British Columbia, Canada conducted between 2008 and 2012 as a means to help establish the contributory role of drugs in driver fatalities. The results show increased probability of fatal crash associated with the use of alcohol or drugs and greatly increased risks associated with the use of alcohol and drugs in combination. Alcohol remains a primary substance of concern for road safety. Cannabis also presents increased risks for drivers as does the combined use of cannabis and alcohol. These findings will contribute to program and policy initiatives to improve road safety.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".