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 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 | 0.027 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".