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 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".