Early substance use and the school environment: A multilevel latent class analysis.
Bibliographic record
Abstract
Background: Early substance use is associated with increased risks for mental health and substance use problems which are compounded when using several substances (i.e., polysubstance use). A notable increase in substance use occurs when adolescents transition from elementary to secondary schooling. Objective: This study seeks to characterize student and school classes of substance use. Methods: A cross-sectional multilevel latent class analysis and regression was conducted on a representative sample of 19,130 grade 6-8 students from 180 elementary schools in Ontario, Canada to: 1) identify distinct classes of student substance use; 2) identify classes of schools based on student classes; and 3) explore correlates of these classes, including mental health, school climate, belonging, safety, and extracurricular participation. Results: Two student and two school classes were identified. 4.1% of students were assigned to the high probability of early polysubstance use class while the remaining 95.9% were in the low probability class. Students experiencing depressive and externalizing symptoms had higher odds of being in the early polysubstance use class (Odds Ratio [OR]s=1.1-1.25). At the school level, 19% of schools had higher proportions of students endorsing polysubstance use. Perceptions of positive school climate, belonging, and safety increased the odds of students being in the low probability of early polysubstance use student-level class (ORs=0.85-0.93) and lower probability of early polysubstance use school-level class. Associations related to extracurricular participation were largely not statistically significant. Conclusions: 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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".