Typologies of Canadian young adults who drive after cannabis use: A two‐step cluster analysis
Bibliographic record
Abstract
Young adults that drive after cannabis use (DACU) may not share all the same characteristics. This study aimed to identify typologies of Canadians who engage in DACU. About 910 cannabis users with a driver's license (17-35 years old) who have engaged in DACU completed an online questionnaire. Two-step cluster analysis identified four subgroups, based on driving-related behaviors, cannabis use and related problems, and psychological distress. Complementary comparative analysis among the identified subgroups was performed as external validation. The identified subgroups were: (1) frequent cannabis users who regularly DACU; (2) individuals with generalized deviance with diverse risky road behaviors and high levels of psychological distress; (3) alcohol and drug-impaired drivers who were also heavy frequent drinkers; and (4) well-adjusted youths with mild depressive-anxious symptoms. Individuals who engaged in DACU were not a homogenous group. When required, prevention and treatment need to be tailored according to the different profiles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".