A Systematic Review of Predictors of Vaping Cessation Among Young People
Bibliographic record
Abstract
BACKGROUND AND AIMS: Understanding the factors influencing vaping cessation among young people is crucial for targeted interventions. This review aimed to summarize the individual and environmental factors that predict vaping cessation-related behaviors in the young population. METHODS: We systematically searched five databases for studies investigating predictors of vaping cessation behaviors among young people aged 10-35 years. Studies that examined predictors of cessation of cigarettes, other tobacco products, cannabis vaping, and studies evaluating efficacy of cessation interventions were excluded. Quality in Prognosis Studies tool was used to assess risk of bias. RESULTS: We found 24 studies analyzing predictors of intention to quit vaping (n = 15), quit attempts (n = 11), and vaping abstinence (n = 7). Most studies had low risk of bias, except for study attrition. We identified 107 predictors and grouped them into "probable," "possible," "insufficient evidence," "probably unrelated," and "inconsistent direction" categories. For "probable" predictors, we found 11 for intention to quit, eight for quit attempts and five for vaping abstinence. Overall, harm perception of vaping, current other tobacco products use, frequency of use, and level of nicotine dependence were common "probable" predictors across three outcomes, with low harm perception of vaping, dual use, and poly tobacco use associated with decreased intention to quit and quit attempts in younger population (~10-19 years). CONCLUSIONS: Predictive modeling studies investigating vaping cessation-related behaviors among young people are still limited. Future research should specifically study the natural history of vaping in youth in different jurisdictions, populations, and age groups to expand our knowledge in this area. IMPLICATIONS: We identified and categorized predictors of intention to quit vaping, quit attempts, and vaping abstinence among young people. While the "probable" predictors can inform public health and policymakers to plan targeted vaping cessation programs for high-risk populations, raising public harm perception of vaping and encouraging to quit other tobacco products might increase intention to quit and quit attempts among younger population. However, the "possible," "insufficient evidence" and "inconsistent direction" predictors need further testing by future prospective longitudinal research. Additionally, we emphasized the significance of appropriate study designs, conducting research across various jurisdictions, and different population groups to obtain comprehensive insights.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".