Using decision trees to examine risk profiles for cannabis use among large samples of underage youth before and after cannabis legalization in Canada
Bibliographic record
Abstract
• Cannabis never use increased in a 4-year period spanning cannabis legalization. • Current cannabis use decreased in a 4-year period spanning cannabis legalization. • Risk factors for current cannabis use changed from pre- to post-legalization. • Internalizing mental health conditions were important risk factors post-legalization. This paper compares risk profiles for cannabis use among large samples of youth in the school years preceding (2017–18, T 1 ) and four years following (2021–22, T 2 ) cannabis legalization in Canada. COMPASS Study data from students across 85 secondary schools that participated in both the T 1 and T 2 waves were used. A novel classification tree approach examined current cannabis use (past 30-day), modelling complex interactions among multiple risk factors simultaneously in the T 1 and T 2 samples. At T 1 , 15.0 % of students reported current cannabis use, compared to 12.3 % of students at T 2 . The classification tree at T 1 identified six unique risk profiles. The highest risk group (Pr = 0.269) was large (30.4 % of the sample) and comprised students who placed lower value on getting good grades and spent 45 min or more per day texting. The classification tree at T 2 identified 11 unique risk profiles. The highest risk group (Pr = 0.27) was large (18.8 % of the sample) and comprised students who again placed lower value on getting good grades but also reported not eating breakfast daily and having elevated anxiety. Cannabis never use increased and current cannabis use slightly decreased among underage youth in a 4-year period spanning cannabis legalization. The relative importance ranking of risk factors for predicting current cannabis use changed considerably from T 1 to T 2 . This suggests that prevention efforts need to adapt over time to target the relevant risk factors associated with cannabis use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".