Ex-Vapers’ Perspectives on Helpful and Unhelpful Influences During Their Quit Journeys
Bibliographic record
Abstract
There is limited understanding of what influences vaping cessation, especially as vaping regulations change, and different jurisdictions have different regulations. This study involves 281 ex-vapers (16-24 years) from Nova Scotia, Canada. A content analysis was used to understand and compare youth and young adults' (YA) experiences of quitting vaping. Both helpful and unhelpful factors for quitting vaping were identified; each category had five themes and twenty-one sub-themes. Helpful factors were consistent across both age categories and included planned and unplanned vaping control interventions, health concerns, social support, evidence-based support, and unassisted quitting methods. Similarly, the five themes identified as unhelpful factors were consistent for both age groups: negative personal implications, negative social influences, planned and unplanned vaping control interventions, the side effects of previous use, and simultaneous and alternative substance use. Policies that limit access and raise awareness about lung health and well-being can help youth quit vaping. For YAs, increasing awareness about social support and health concerns is crucial. Raising e-cigarette costs and reducing vaping normalization supports quitting for YAs. Stress reduction and training to handle social pressure could aid youth, while YAs might benefit from treatment for other substance use to help with nicotine quitting.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".