Characteristics of adolescent cannabis use and social context predicting problematic use: A decision tree analysis
Bibliographic record
Abstract
Identifying characteristics of adolescent cannabis use and the surrounding social context that may predict problematic use is important for delivering harm-reduction approaches in both clinical and public health settings. We used a decision tree analysis to identify combinations of various use characteristics (e.g., frequency, mode, duration, initiation age, polysubstance use) and social context risk factors (e.g., peer and household use, solitary use, ease of access) characterizing the highest risk groups for three indicators of problematic use (unsuccessful quit attempt, excessive use, feeling addicted). We analyzed data from 8,915 cannabis ever-users from a large sample of secondary school students (mean age 15.5) in Québec, Canada, who completed the COMPASS survey. Using cannabis at least 2-4 times per month was the most important predictor of problematic use and independently characterized the highest risk groups. Initiating use before age 14 and engaging in solitary use also predicted increased problematic use risk among adolescents whose sustained past-year use did not meet the 2-4 times per month threshold. No sociodemographic differentiation emerged among high-risk groups. When screening for risky cannabis use or promoting safer cannabis behaviours among adolescents, health care providers and public health groups should consider factors with the greatest potential to influence detrimental trajectories among this age group.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".