Exploring perceptions among people who drive after cannabis use: Collision risk, comparative optimism and normative influence
Bibliographic record
Abstract
INTRODUCTION AND AIMS: While the perceived risks of driving under the influence of cannabis (DUIC) have been a focus of recent drug-driving research, relevant concepts from the social cognition literature have rarely been applied to inform understanding of DUIC. This study aims to expand knowledge of perceived collision risk and social influences associated with DUIC and driving after other substance use. DESIGN AND METHODS: Semi-structured interviews were conducted with 20 participants of a remedial program for impaired drivers. Thematic analysis began with two independent coders. Early discussion of emergent themes resulted in the identification of applicable social cognition concepts, resulting in selective coding and interpretation. RESULTS: Many participants identified DUIC as less risky than driving under the influence of alcohol or other drugs. Mixed perceptions regarding the dangerousness of DUIC were expressed, with some participants denying increased collision risk except among novice cannabis users. Comparative optimism bias was also expressed by participants who perceived themselves as less likely than others to be involved in a collision when DUIC. In view of normative influence, friends were generally seen as more accepting of DUIC than family, and there were indications that the opinions of others who use cannabis were regarded as more credible than the opinions of those who do not use the drug. DISCUSSION AND CONCLUSIONS: Comparative optimism bias and normative influence may contribute to perceived risks associated with DUIC and may, therefore, be useful concepts to employ to increase the effectiveness of public health and road safety initiatives.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".