Drug use among fatally injured drivers in Canada
Bibliographic record
Abstract
Over the past several decades, information provided by coroners and medical examiners on the use of alcohol by drivers who die in motor vehicle crashes has been instrumental in monitoring the extent of the problem, evaluating the impact of programs and policies and generally furthering our understanding of the magnitude and characteristics of the alcohol-crash problem. The purpose of this study was to examine the results of toxicological tests performed on fatally injured drivers of motor vehicles in Canada to determine the extent and type of drug use as well as the characteristics of the people and the circumstances involved. Data on alcohol and drug use from coronersr and medical examinersr files on drivers of motor vehicles who died in crashes from 2000 through 2010 in Canada. Psychoactive substances were grouped according to the system used by the Drug Evaluation and Classification program. Among drivers who died within six hours of the crash, 96 per cent were tested for alcohol and 58.8 per cent were tested for drugs. Of those tested, 33.7 per cent were positive for a psychoactive drug; 38.5 per cent were positive for alcohol. Overall, 56.7 per cent of fatally injured drivers were positive for alcohol, drugs, or both. The most commonly detected substances were central nervous system depressants and cannabis. The present findings provide greater understanding of the involvement of drugs in serious crashes, revealing differences in the characteristics of drivers and crashes involving alcohol versus drugs that have implications for prevention and enforcement.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".