Early substance use and the school environment: A multilevel latent class analysis
Bibliographic record
Abstract
This study conducted multilevel latent class analysis and regression with a representative sample of 19,130 grade 6-8 students from 180 schools in Ontario, Canada to: 1) identify student classes of early substance use; 2) identify school types based on student classes; and 3) explore mental health and school correlates. Two student and two school classes were identified, reflective of lower and higher probabilities of early substance use. Students experiencing depressive and externalizing symptoms had higher odds of being in the early substance use class. Perceptions of positive school climate, belonging, and safety increased the odds of students being in the low probability of substance use class and schools with lower proportions of students using substances. Extracurricular participation was largely non-significant. Student and school substance use classes may serve as targets for tailored prevention and early interventions. Results support examining school-based interventions targeting school climate, belonging, and safety.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.076 | 0.393 |
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; both teacher heads agree on what is shown here.
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".