Medication and driving: A comparison between Canada, United States, and Europe
Bibliographic record
Abstract
Background. Drug-impaired driving is a prominent road safety concern and research surrounding this issue is being conducted at an unprecedented pace. However much less is known about driving under the influence of prescription medication, and how to manage this road safety issue, especially in light of an aging population. Further research to support the development of tailored enforcement strategies and educational campaigns that address the risks associated with prescription medication use and driving is needed. Objectives. The objective of this study was to compare the rates of drivers in Canada, Europe and the United States who self-declare driving after taking medication with a warning that it may influence driving ability to determine what quantitative differences exist between these three regions. The effects of demographics and personal beliefs on this self-reported behaviour were also examined. Methods. Self-reported use of prescription medication with a warning that it may influence driving ability, and personal acceptability of this behaviour were measured as part of the E-Survey of Road Users’ Attitudes (ESRA 2; www.esranet.eu). ESRA 2 is a joint international initiative of 26 research centres and road safety institutes; the project has surveyed road users in 38 countries on 5 continents. The descriptive analysis compared rates of this self-reported behaviour and opinions regarding personal acceptability by region. A multivariate model predicting driver’s self-reported use of prescription medication with a warning that it may influence driving ability was estimated. Results. At the time of submission of this abstract, data collection for the cross-sectional online survey is ongoing. Data collection will begin in December 2018; final analysis results will be available in February, 2019.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".