School and Community Predictors of Smoking: A Longitudinal Study of Canadian High Schools
Bibliographic record
Abstract
OBJECTIVES: We identified the most effective mix of school-based policies, programs, and regional environments associated with low school smoking rates in a cohort of Canadian high schools over time. METHODS: We collected a comprehensive set of student, school, and community data from a national cohort of 51 high schools in 2004 and 2007. Hierarchical linear modeling was used to predict school and community characteristics associated with school smoking prevalence. RESULTS: Between 2004 and 2007, smoking prevalence decreased from 13.3% to 10.7% in cohort schools. Predictors of lower school smoking prevalence included both school characteristics related to prevention programming and community characteristics, including higher cigarette prices, a greater proportion of immigrants, higher education levels, and lower median household income. CONCLUSIONS: Effective approaches to reduce adolescent smoking will require interventions that focus on multiple factors. In particular, prevention programming and high pricing for cigarettes sold near schools may contribute to lower school smoking rates, and these factors are amenable to change. A sustained focus on smoking prevention is needed to maintain low levels of adolescent smoking.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".