E-Cigarettes for Smoking Cessation: Why do Users Continue with E-Cigarettes?
Bibliographic record
Abstract
The advent of e-cigarettes (vaping) well over a decade ago, was welcomed as a tool to aid cigarette smoking cessation. Whilst it has served its aims for many, there remains several who switched to vaping but did not cease cigarette smoking. They also continued with vaping behaviours. The aim of this study is to identify and gain a better understanding of why their vaping behaviours continue. With that in mind, a qualitative study with focus groups was undertaken. A purposive convenience sample of 17 participants who are patrons of 3 vaping centres in the city of Kuala Lumpur were recruited. Four focus groups were formed from the sample of 17 participants. Data derived from the focus groups identified seven themes which emerged as motivating factors for continued vaping behaviours. They comprised of social acceptance; attraction to flavours; a sense of accomplishment; financial savings; convenience compared to smoking; perceived low health risk; and behavioural substitution. Findings suggest that vaping behaviours could be conceptualised by Choice Theory based on psychosocial needs of survival, achievement, love and belonging, freedom and fun. They highlight the role of psychosocial factors that could be considered of importance in informing policy and practices for smoking cessation programmes and activities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".