Are experimental smokers different from their never-smoking classmates? A multilevel analysis of Canadian youth in grades 9 to 12
Bibliographic record
Abstract
INTRODUCTION: Understanding the characteristics of experimental smoking among youth is critical for designing prevention programs. This study examined which student- and school-level factors differentiated experimental smokers from never smokers in a nationally representative sample of Canadian students in grades 9 to 12. METHODS: School-level data from the 2006 Canadian Census and one built environment characteristic (tobacco retailer density) were linked with data from secondary school students from the 2008-2009 Canadian Youth Smoking Survey and examined using multilevel logistic regression analyses. RESULTS: Experimental smoking rates varied across schools (p < .001). The location (adjusted odds ratio [AOR] = 0.66, 95% CI: 0.49-0.89) of the school (urban vs. rural) was associated with the odds of a student being an experimental smoker versus a never smoker when adjusting for student characteristics. Students were more likely to be experimental smokers if they were in a lower grade, reported low school connectedness, used alcohol or marijuana, believed that smoking can help people relax, received pocket money each week and had a family member or close friend who smoked cigarettes. CONCLUSION: School-based tobacco prevention programs need to be grade-sensitive and comprehensive in scope; include strategies that can increase students' attachment to their school; and address multi-substance use, tobacco-related beliefs and the use of pocket money. These programs should also reach out to students who have smoking friends and family members. Schools located in rural settings may require additional resources.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".