Evaluating risks, monitoring cannabis use, and planning to get home safely: Exploring self-regulation processes associated with cannabis use and driving
Bibliographic record
Abstract
OBJECTIVE: Preventing Cannabis-impaired driving involves understanding how users assess risk, monitor their use, and plan to get home safely. While extant research has shown substantial heterogeneity in patterns of cannabis use among different user groups, far less research has examined self-regulation among users. The current study aims to identify sub-groups of individuals who used or have used cannabis based on how they perceive risks, monitor their impairment, and plan to avoid driving under the influence of cannabis (DUIC) to examine how the different profiles relate to DUIC outcomes. METHODS: = 13.67; 63% female). Risk perception, impairment monitoring, planning ability, DUIC-related behaviors, Cannabis use and related problems were assessed through an online self-reported survey. RESULTS: Latent profile analysis identified three groups of self-regulators based on their level of risk perception, monitoring, and plan to avoid DUI. The majority (51%) of participants showed moderate self-regulation with average levels of risk perception, monitoring, and planning. A "highly self-regulated" group (20%) had the highest risk perception, monitoring, and planning. A "low self-regulated" group (29%) had the lowest risk perception, less confidence in monitoring, and lower DUI planning. There were significant differences between the profiles and DUIC outcomes. Cannabis users (including both historical and current users) with high self-regulation were less likely to be passengers of drivers under the influence and more likely to intervene to stop friends from driving while impaired, compared to those with low or moderate self-regulation. However, there were no profile differences in reports of having ever driven under the influence of cannabis. CONCLUSIONS: Differences in risk perception, monitoring, and planning are associated with self-regulatory abilities. Understanding diverse self-regulation patterns among people who have used cannabis can help identify and mitigate risky behaviors, including DUI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".