Evaluating the One-Year Impact of School e-Cigarette Use Interventions among Current Youth e-Cigarette Users in the COMPASS Study, 2017/18–2018/19
Bibliographic record
Abstract
There is a lack of evidence for the impact of school-based e-cigarette interventions among current e-cigarette users. This natural experimental evaluation study evaluated the one-year impact of various school-based e-cigarette prevention/cessation programs among a sample of current youth e-cigarette users. The COMPASS study sample included n = 3586 current e-cigarette users from n = 90 schools with data collected between 2017 and 2019. Student e-cigarette use patterns were categorized as “escalated”, “maintained”, and “reduced” based on the change in past 30-day e-cigarette use between baseline and follow-up. Intervention schools added e-cigarette use “prevention”, “cessation”, or “protection” programs, while control schools did not make any changes. Logistic regression models identified how each category of added programs was associated with e-cigarette use patterns. About one quarter of schools added an e-cigarette use prevention/cessation program over one year. Student e-cigarette use patterns between control and intervention groups differed in proportion ranging from a decrease of 3.35% to an increase of 5.80%. Regression models did not identify any significant differences in the odds of escalating or reducing e-cigarette use in intervention relative to control schools. While many schools implemented new e-cigarette programs over one year, none of the interventions led to significant changes in e-cigarette escalation or reduction among current youth e-cigarette users. Additional studies are needed to evaluate the impact of e-cigarette interventions among current e-cigarette users.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".