A Medical Student–Delivered Smoking Prevention Program, Education Against Tobacco, for Secondary Schools in Brazil: Study Protocol for a Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Smoking is the largest preventable cause of morbidity and mortality in Brazil. Education Against Tobacco (EAT) is a large network of medical students in 13 countries who volunteer for school-based prevention in the classroom setting. A recent quasi-experimental EAT study conducted in Germany showed significant short-term smoking cessation effects on 11- to 15-year-old adolescents. OBJECTIVE: The aim of this study is both to describe and to provide the first randomized long-term evaluation of the EAT intervention involving a photoaging app for its effectiveness to reduce the smoking prevalence among 12- to 17-year-old pupils in Brazilian public schools. METHODS: A randomized controlled trial will be conducted among approximately 1500 adolescents aged 12 to 17 years in grades 7-11 of public secondary schools in Brazil. The prospective experimental study design includes measurements at baseline and at 6 and 12 months postintervention. The study groups will consist of randomized classes receiving the standardized EAT intervention (90 minutes of mentoring in a classroom setting) and control classes within the same schools (no intervention). The questionnaire measures smoking status, gender, social, and cultural aspects as well as predictors of smoking. Biochemical validation of smoking status is conducted via random carbon monoxide measurements. The primary end point is the difference of the change in smoking prevalence in the intervention group versus the difference in the control group at 12 months of follow-up. The differences in smoking behavior (smoking onset, quitting) between the 2 groups as well as effects on the different genders will be studied as secondary outcomes. RESULTS: The recruitment of schools, participating adolescents, and medical students was conducted from August 2016 until January 2017. The planned period of data collection is February 2017 until June 2018. Data analysis will follow in July 2018 and data presentation/publication will follow shortly thereafter. CONCLUSIONS: This is the first evaluative study of a medical student-delivered tobacco prevention program in Brazil and the first randomized trial on the long-term effectiveness of a school-based medical student-delivered tobacco prevention program in general. CLINICALTRIAL: ClinicalTrials.gov NCT02725021; https://clinicaltrials.gov/ct2/show/NCT02725021 (archived by WebCite at http://www.webcitation.org/6njy3nNml).
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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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.073 | 0.013 |
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".