Deterring Driving under the Influence of Cannabis: Knowledge and Beliefs of Drivers in a Remedial Program
Bibliographic record
Abstract
As provincial and territorial governments across Canada adjust to the federal legalization of cannabis for non-medical use, strategies to deter driving under the influence of cannabis (DUIC) are increasingly attracting attention. Development and evaluation of legal and other measures designed to deter DUIC would benefit from improved understanding of knowledge and beliefs that underpin individuals’ engagement in and avoidance of DUIC. In 2017, we conducted 20 interviews with clients of a remedial program for officially processed (i.e., convicted or suspended) impaired drivers. Eligible study participants reported having driven a motor vehicle within an hour of using cannabis in the past year. Using a thematic analytic approach, we observed vague awareness of the content of drug-impaired–driving laws; perceived low likelihood of getting caught by police for DUIC, with some beliefs that enforcement would increase after legalization; and a range of opinions on four key deterrent strategies (i.e., roadside spot-check programs, legal limits for tetrahydrocannabinol, zero tolerance for novice drivers, and remedial programs). Many participants raised concerns about the accuracy of roadside testing procedures and fairness to drivers. Our findings provide new support for elements of legislation and programming that might effectively deter DUIC.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".