Evaluation of a medical student-delivered smoking prevention program utilizing a face-aging mobile app for secondary schools in Germany: The Education Against Tobacco cluster-randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: To reduce smoking uptake in adolescents, the medical students' network Education Against Tobacco (EAT) has developed a school-based intervention involving a face-aging mobile app (Smokerface). METHODS: A two-arm cluster-randomized controlled trial was conducted, evaluating the 2016 EAT intervention, which employed the mobile app Smokerface and which was delivered by medical students. Schools were randomized to intervention or control group. Surveys were conducted at baseline (pre-intervention) and at 9, 16, and 24 months post-intervention via paper & pencil questionnaires. The primary outcome was the difference in within-group changes in smoking prevalence between intervention and control group at 24 months. RESULTS: Overall, 144 German secondary schools comprising 11,286 pupils participated in the baseline survey, of which 100 schools participated in the baseline and at least one of the follow-up surveys, yielding 7437 pupils in the analysis sample. After 24 months, smoking prevalence was numerically lower in the intervention group compared to control group (12.9 % vs. 14.3 %); however, between-group differences in change in smoking prevalence between baseline and 24-months follow-up (OR=0.83, 95 %-CI: 0.64-1.09) were not statistically significant (p = 0.176). Intention to start smoking among baseline non-smokers declined non-significantly in the intervention group (p = 0.064), and remained essentially unchanged in the control group, but between-group differences in changes at the 24-months follow-up (OR=0.88, 0.64-1.21) were not statistically significant (p = 0.417). CONCLUSION: While a trend towards beneficial effects of the intervention regarding smoking prevalence as well as intention to start smoking among baseline non-smokers was observed, our smoking prevention trial demonstrated no significant effect of the intervention.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".