More support needed: Evaluating the impact of school e-cigarette prevention and cessation programs on e-cigarette initiation among a sample of Canadian secondary school students
Bibliographic record
Abstract
Given the recent increase in e-cigarette use among adolescents, there is a need to further explore how school programs are associated with e-cigarette initiation. The objective of this quasi-experimental study was to evaluate the impact of multiple school-based e-cigarette prevention and cessation programs on e-cigarette initiation among Canadian adolescents. This study used data from Year 6 (2017/18) and Year 7 (2018/19) of the COMPASS study in British Columbia, Alberta, Ontario, and Quebec, Canada. Students in grades 9 to 11 who had never tried e-cigarettes at baseline were included (n = 13,269). Schools (n = 88) reported whether they added programming that addressed e-cigarette or tobacco prevention or cessation. Generalized estimating equations were used to identify how added programs were associated with e-cigarette initiation at follow-up. At one-year follow-up (2018/19), 23% of schools added programs. Our evaluation results suggest that none of the activities taken by schools to prevent or reduce vaping among students significantly prevented vaping onset. In fact, female students at schools that reported adding a theme week had higher odds of e-cigarette initiation (OR 1.68 [95% CI 1.31-2.16]) and male students at schools that reported a cessation program had higher odds of e-cigarette initiation (OR 1.20 [95% CI 1.01-1.44]). These results suggest that schools may not know how to address e-cigarette use and that there can be risks to students if programs are not carefully implemented. Results point to the need for additional support to ensure that schools are taking evidence-based approaches that support all students.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".