Does perceived vaping addiction predict subsequent vaping cessation behaviour among adults who use nicotine vaping products regularly?
Bibliographic record
Abstract
BACKGROUND: This study aimed to investigate whether perceived vaping addiction is a predictor of quitting nicotine vaping product (NVP) use among adults who have ever smoked and currently vape exclusively or predominantly in four countries: Australia, Canada, the US, and England. METHOD: Data analysed (n = 574) came from participants (aged 18 + ) who completed the International Tobacco Control (ITC) Four Country Smoking and Vaping survey in 2018 and again in 2020. Baseline inclusion criteria were: (1) currently not smoking or non-daily smoking; and (2) using NVPs daily/weekly for a period of at least 4 months. Association of self-reported baseline levels of perceived vaping addiction with making any attempts to quit vaping and successful attempts reported at follow-up were examined using logistic regression models, controlling for potential sociodemographic and smoking/vaping-related confounders. RESULTS: Participants who perceived themselves as being addicted to vaping were less likely to attempt to quit vaping than those who perceived themselves as not addicted. Among those who tried, those who perceived themselves being addicted were also less likely to succeed than those who perceived themselves as not addicted. No significant country differences in associations were observed for both outcomes. CONCLUSIONS: Perceived vaping addiction was shown to have predictive utility for vaping cessation behaviours, possibly acting as an indicator of task difficulty, and thus may serve as a useful screening tool for identifying NVP users who may benefit from tailored cessation support programs if they want to stop using these products.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".