Co-morbid substance use behaviors among youth: any impact of school environment?
Bibliographic record
Abstract
BACKGROUND: Substance use is common among youth; however, our understanding of co-morbid tobacco, alcohol and marijuana use remains limited. The school-environment may play an important role in the likelihood a student engages in high risk substance use behaviors, including co-morbid use. PURPOSE: This study aims to: (i) describe the prevalence of co-morbid substance use behaviors among youth; (ii) identify and compare the characteristics of youth who currently use a single substance, any two substances, and all three substances; (iii) examine if the likelihood of co-morbid use varies by school and; (iv) examine what factors are associated with co-morbid use. METHODS: This study used nationally representative data collected from students in grades 9 to 12 (n = 41,886) as part of the 2006-2007 Canadian Youth Smoking Survey (YSS). Demographic and behavioral data were collected including, current cigarette, alcohol and marijuana use. Results. 6.5% (n = 107,000) reported current use of all three substances and 20.3% (n = 333,000) of any two substances. Multi-level analysis revealed significant between school variability in the odds a student used all three substances and any two substances; accounting for 16.9% and 13.5% of the variability, respectively. Co-morbid use was associated with sex, grade, amount of available spending money and perceived academic performance. CONCLUSIONS: Co-morbid substance use is high among youth; however, not all schools share the same prevalence. Knowing the school characteristics that place particular schools at risk for student substance use is important for tailoring drug and alcohol education programs. Interventions that target the prevention of co-morbid substance use are required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".