Toxicology findings from drivers suspected of drug-impaired driving in Ontario (2008–2019)
Bibliographic record
Abstract
OBJECTIVE: This study examines the results of toxicological tests performed on blood and urine samples collected from suspected drug-impaired drivers in Ontario from 2008 to 2019. The report examines the results of toxicological analysis of the samples submitted, the characteristics of those drivers from whom samples were collected, and the temporal and situational circumstances that led to police investigations and sample collection to better understand drug-impaired driving behavior and to assist in the development and implementation of countermeasure strategies and programs. METHODS: Blood and urine samples were sent to the Center of Forensic Sciences where they were analyzed using standardized comprehensive toxicological analysis to test for a wide variety of potentially impairing drugs. Demographic and temporal information for each case from which a sample was collected were also examined to describe the circumstances and characteristics of these driving incidents. RESULTS: During the 12-year period examined, 5,388 samples collected from suspected drug-impaired drivers were analyzed. The number of samples collected increased substantially following the implementation of the Drug Evaluation and Classification Program (DECP) in July 2008, the enactment of legislation facilitating the collection of blood samples from suspects, and the legalization of cannabis for nonmedical purposes in 2018. The number of samples submitted shows temporal correlation with the number of police officers certified as Drug Recognition Experts (DRE) in the province. Over the 12-year period of this study, cannabis was the most frequently detected substance in drivers (52.8% of cases), followed by cocaine (44.3%) and methamphetamine (24.8%). In 80% of cases, more than one substance was detected. CONCLUSIONS: Examining the characteristics of suspected drug-impaired drivers, the temporal circumstances, and the drug findings throughout the large geographic area of Ontario and over the extended period of this study enhances our understanding of drug-impaired driving behavior. These characteristics can assist in the development and/or evaluation of enforcement strategies and enhanced countermeasure activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".