Interventions for Preventing E-Cigarette Use Among Children and Youth: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Many nonregulatory interventions targeting children and youth have been implemented at three levels: directed at the individual (e.g., interactive video games), delivered to students at school (e.g., campus bans), and launched in the community (e.g., mass media campaigns). This systematic review aims to synthesize the evidence on the effectiveness of interventions aimed at preventing e-cigarette initiation among children and youth. METHODS: MEDLINE, CINAHL, Embase, APA PsycINFO, and Web of Science Core Collection were searched for papers published between January 1, 2004 and September 1, 2022 that reported more than one outcome on vaping prevention among individuals aged less than 21-years-old: vaping prevalence/incidence, initiation intentions, knowledge/attitudes, and other tobacco product use prevalence/initiation intentions. Interventions were at the individual, school, or community level. The risk of bias was assessed using ROBINS-I and RoB 1. RESULTS: Thirty-nine publications met the eligibility criteria. Fourteen individually-based (4 parental monitoring, 3 video games, 2 text messages, 3 graphic message themes, 2 healthcare), 19 school-based (14 educational and skill interventions, 5 vape-free policies/bans), and 6 community-based (3 social media, 3 mass media campaigns) interventions were reported. E-cigarette initiation prevention was observed with high perceived parental monitoring; however, the cross-sectional study designs precluded causal claims. There was promising but limited evidence that social-emotional skills curricula and peer leader programming prevented vaping initiation. DISCUSSION: Some individual- and school-based interventions showed promise for preventing e-cigarette initiation among children and youth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".