Effects of vaping on physical and mental health in at-risk populations (VAPE): mixed-methods study of motivations for and perspectives on vaping in patients with opioid use disorder
Bibliographic record
Abstract
BACKGROUND: Vaping is increasing in popularity. Vape products are offered in a wide variety and promise to reduce harms associated with cigarette smoking, among other claims. The motivations for vaping in patients with substance use disorder are largely unknown. AIMS: To describe perceptions and motivations regarding vaping among patients with opioid use disorder (OUD) who vape. METHOD: A convergent mixed-methods study design was used, and individual, semi-structured interviews were conducted with 41 individuals with OUD who were receiving medication for OUD and also vaped. An inductive data-driven approach was employed to characterise perspectives on vaping. RESULTS: The mean ages at which participants had been introduced to vaping and initiated regular vaping were 33.95 years (s.d. 12.70) and 34.85 years (s.d. 12.38), respectively. Daily vaping (85%) of nicotine, flavoured nicotine or cannabis was common, with 27% reporting vaping both nicotine and cannabis. Qualitative analysis identified 14 themes describing motivations for vaping, including viewing vaping as a smoking cessation tool, convenience and popularity among youth. CONCLUSIONS: Mixed-methods findings indicated that patients with OUD who vape perceived vaping to be healthier, cleaner and more convenient than cigarette and cannabis smoking, without appreciating the health risks. The perspectives reflected the importance of health education, guidelines and screening tools for vaping and could provide direction for healthcare providers and future vaping cessation programmes.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".