Effective interventions to prevent youth vaping behaviours: a rapid review
Bibliographic record
Abstract
OBJECTIVE: To identify effective policies and non-policy interventions preventing youth vaping behaviour initiation and assess their effectiveness by the level of intrusiveness and subpopulations. DESIGN: This systematic rapid review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. DATA SOURCES: Searches on MEDLINE and APA-PsycINFO for studies published between January 2019 and November 2023. ELIGIBILITY CRITERIA: Observational, intervention or mixed-method studies and quantitative systematic reviews/meta-analyses measuring the impact of interventions on youth (6-18 years) who never vaped or who had experimentally vaped. DATA EXTRACTION AND SYNTHESIS: . We applied PROGRESS-Plus (Place of residence, Race/ethnicity/culture/language, Occupation,Gender/sex, Religion, Education, Socioeconomic status, Social capital, and additional context-specific factors) for an equity analysis. Methodological quality was assessed using the Effective Public Health Practice Project Quality Assessment Tool. RESULTS: 20 studies were included: 45% were experiments or quasiexperiments, 85% reported data from the USA, 65% were non-policy interventions and 40% and 35% measured susceptibility and attitudes and behaviours related to vaping, respectively. Considering the level of intrusiveness, 45% of the studies provided information and 25% eliminated choices. Overall, the certainty of evidence was low. The effectiveness of interventions regarding their level of intrusiveness varied by each outcome. No clear pattern was found between the level of intrusiveness and intervention effectiveness, suggesting that overall, the studied interventions positively changed youth vaping behaviours. Some interventions had positive effects on multiple outcomes. Equity-related findings suggested that younger youth may be less responsive to the interventions. Recommendations for action are provided. CONCLUSIONS: We suggest that combining multiple interventions targeting different levels of intrusiveness and outcomes may be more effective in preventing youth vaping behaviours. Also important is to tailor programmes to younger youth to better meet their needs.
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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 | 0.034 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".