A multilevel analysis examining the association between school-based smoking policies, prevention programs and youth smoking behavior: evaluating a provincial tobacco control strategy
Bibliographic record
Abstract
This paper examined how smoking policies and programs are associated with smoking behavior among Grade 10 students (n = 4709) between 1999 and 2001. Data from the Tobacco Module from the School Health Action Planning and Evaluation System were examined using multilevel logistic regression analyses. We identified that (i) attending a school with smoking prevention programs only was associated with a substantial risk of occasional smoking among students with two or more close smoking friends and (ii) attending a school with both smoking prevention programs and policies was associated with substantial risk of occasional smoking among students who did not believe there were clear smoking rules present. Students attending schools where year of enrollment in high school starts in Grade 9 were more likely to be regular and occasional smokers. Each 1% increase in Grade 12 smoking rates increased the odds that a Grade 10 student was an occasional smoker. It appears that grade of enrollment, senior student smoking behavior, close friend's smoking behavior and clear rules about smoking at school can impact school-based tobacco control programming. These preliminary study findings suggest the need for further research targeting occasional smoking behavior and the transition stage into high school.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".