Epidemiology of drug driving: protocol from a national Canadian study measuring levels of cannabis, alcohol and other substances in injured drivers
Bibliographic record
Abstract
BACKGROUND: Drug driving is an emerging global road safety problem. As the prevalence of alcohol-impaired driving decreases, and as more jurisdictions decriminalize or legalize cannabis, it is increasingly important for policy makers to have accurate information on the prevalence and pattern of drug driving. Unfortunately, this data is not widely available and the World Health Organization identifies lack of accurate data on the prevalence of drug driving as an important knowledge gap. METHODS: In this paper, we discuss the limitations of current methods of monitoring drug use in drivers. We then present a novel methodology from a multi-centre study that monitors the prevalence and pattern of drug use in injured drivers across Canada. This study uses "left-over" blood taken as part of routine medical care to quantify cannabis and other drugs in non-fatally injured drivers who present to participating emergency departments after a collision. Toxicology testing is done with waiver of consent as we have procedures that prevent results from being linked to any individual. These methods minimize non-response bias and have the advantages of measuring drug concentrations in blood obtained shortly after a collision. DISCUSSION: Our methods can be applied in other jurisdictions and provide a consistent approach to collect data on drug driving. Consistent methods allow comparison of drug driving prevalence from different regions. Data from this research can be used to inform policies designed to prevent driving under the influence of cannabis and other impairing drugs.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".