Public Health Interventions Targeting the Prevention of Adolescent Vaping: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: Despite a brief decline during the COVID-19 pandemic, vaping behaviors among adolescents continue to be an international public health concern because of associated health risks and harms. A thorough investigation of existing interventions preventing adolescent vaping is needed to help address this public health crisis and reduce serious and avoidable vaping-related health risks. We reviewed the literature to identify public health interventions aimed at preventing adolescent vaping and summarized their key components and outcome measures. DESIGN: We conducted a scoping review of the literature using the Joanna Briggs Institute methodology searching in MEDLINE, CINAHL, Embase, PubMed, PsycINFO, ProQuest, and Web of Science. Two reviewers screened 589 records for relevance. Studies from any location, reported in English, and described vaping prevention interventions targeting adolescents were included. Records were excluded if they were reported in other languages, published outside the review timeframe, lacked an evaluation, focused on cessation-based interventions, or were review articles. Data extracted included intervention type, key components, and outcome measures. RESULTS: Thirty-eight included articles were identified and categorized into three intervention categories: school-based, public education/risk communication campaigns, and public policies/government regulations. Key components of the interventions included format, duration, and topics. Formats varied from single to multi-sessions. Prominent topics covered included risks and harms associated with vaping, and the most frequently reported outcome measures used were knowledge, attitudes, and beliefs. CONCLUSION: Our findings summarize existing public health interventions found in the literature and insights into approaches used to address the global adolescent vaping crisis.
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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 | 0.017 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".