Attitudes toward driving after cannabis use: A systematic review and meta-analysis
Bibliographic record
Abstract
• Attitudes toward driving after cannabis use are predominantly unfavorable. • There is variability in attitudes towards driving after cannabis use across studies. • Attitudes are more favorable among samples of cannabis users. As cannabis policies become more permissive, there is concern that driving after cannabis use (DACU) will increase. From a prevention perspective, understanding whether attitudes toward DACU are positive or negative can guide messaging to reduce DACU. This meta-analysis summarizes quantitative data on attitudes toward DACU safety. Four electronic databases were searched from their inception to February 2024 for studies reporting quantitative data on attitudes toward cannabis use and driving. A total of 1,099 records were retrieved. We summarized data from studies reporting the proportion of respondents endorsing a response option(s) and studies reporting means and standard deviations of endorsed Likert-scale response options using inverse-variance methods. Most respondents had unfavorable views on DACU safety. Among 32 studies, the pooled proportion of respondents endorsing negative attitudes toward DACU was 0.69 (95% CI: 0.62; 0.75). Only ten studies reported the mean and standard deviation of Likert-scale responses; attitudes in these studies were in line with results from the analysis of proportions. Although most people have negative attitudes toward DACU, it is concerning that around one third do not. Prevention initiatives can capitalize on the association between attitude and behaviour to design public messaging.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".