Scoping review of guidance on cessation interventions for electronic cigarettes and dual electronic and combustible cigarettes use
Bibliographic record
Abstract
BACKGROUND: Although evidence-based smoking cessation guidelines are available, the applicability of these guidelines for the cessation of electronic cigarette and dual e-cigarette and combustible cigarette use is not yet established. In this review, we aimed to identify current evidence or recommendations for cessation interventions for e-cigarette users and dual users tailored to adolescents, youth and adults, and to provide direction for future research. METHODS: We systematically searched MEDLINE, Embase, PsycINFO and grey literature for publications that provided evidence or recommendations on vaping cessation for e-cigarette users and complete cessation of cigarette and e-cigarette use for dual users. We excluded publications focused on smoking cessation, harm reduction by e-cigarettes, cannabis vaping, and management of lung injury associated with e-cigarette or vaping use. Data were extracted on general characteristics and recommendations made in the publications, and different critical appraisal tools were used for quality assessment. RESULTS: A total of 13 publications on vaping cessation interventions were included. Most articles were youth-focused, and behavioural counselling and nicotine replacement therapy were the most recommended interventions. Whereas 10 publications were appraised as "high quality" evidence, 5 articles adapted evidence from evaluation of smoking cessation. No study was found on complete cessation of cigarettes and e-cigarettes for dual users. INTERPRETATION: There is little evidence in support of effective vaping cessation interventions and no evidence for dual use cessation interventions. For an evidence-based cessation guideline, clinical trials should be rigorously designed to evaluate the effectiveness of behavioural interventions and medications for e-cigarette and dual use cessation among different subpopulations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".