Clinical guidance for e-cigarette (vaping) cessation: Results from a modified Delphi panel approach
Bibliographic record
Abstract
Individuals seek help to stop their use of e-cigarettes from their healthcare practitioners. However, there is a paucity of published work addressing e-cigarette cessation methods empirically, and what evidence that is available is weak. Therefore, we developed an expert informed clinical resource to guide practitioners helping their clients quit using e-cigarettes. We conducted a modified Delphi process between September and December 2021 to reach consensus on clinical recommendations for e-cigarette cessation. Expert and Peer Panel members (n = 28) voted and provided feedback on the recommendations through three rounds of structured surveys, a discussion board, and one intermediate survey. The penultimate knowledge products underwent usability testing and were finalized based on user feedback. The Expert Panel maintained a 100% response rate for rounds 1 and 2 and 96% for round 3; the Peer Panel achieved a 100% response rate for all three rounds of the modified Delphi process. Consensus was reach on 24 recommendations and 2 statements spanning eight domains: severity and dependence; general approaches; treatment approaches; dual use; pharmacotherapy strategies; behavioural therapy strategies; harm reduction; and relapse prevention. Two additional 'no agreement' statements that did not reach consensus are included in the guidance resource. The recommendations were also contextualized for the following groups: adults; youth; people who are pregnant, breastfeeding and/or chestfeeding; and people with mental illness and/or substance use issues. The recommendations listed in the resource provide general clinical guidance on e-cigarette cessation to assist healthcare practitioners in the treatment planning process.
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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 | 0.139 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".