Indicators of cigarette smoking dependence and relapse in former smokers who vape compared with those who do not: findings from the 2016 International Tobacco Control Four Country Smoking and Vaping Survey
Bibliographic record
Abstract
Abstract Background and Aims It has been proposed that many smokers switch to vaping because their nicotine addiction makes this their only viable route out of smoking. We compared indicators of prior and current cigarette smoking dependence and of relapse in former smokers who were daily users of nicotine vaping products (‘vapers’) or who were not vaping at the time of survey (‘non‐vapers’). Design Cross‐sectional survey‐based comparison between vaping and non‐vaping former smokers, including a weighted logistic regression of vaping status onto predictor variables, adjusting for covariates specified below. Setting United States, Canada, Australia and England. Participants A total of 1070 people aged 18+ years from the 2016 International Tobacco Control (ITC) Four Country Smoking and Vaping Wave 1 Survey who reported having ever been daily smokers but who stopped less than 2 years ago and who were currently vapers or non‐vapers. Measurements Dependent variable was current vaping status. Predictor variables were self‐reported: (1) smoking within 5 minutes of waking and usual daily cigarette consumption, both assessed retrospectively; (2) current perceived addiction to smoking, urges to smoke and confidence in staying quit. Covariates: country, sample sources, sex, age group, ethnicity, income, education, current nicotine replacement therapy use and time since quitting. Findings Vapers were more likely than non‐vapers to report: (1) having smoked within 5 minutes of waking [34.3 versus 15.9%, adjusted odds ratio (aOR) = 3.74, 95% confidence interval (CI) = 1.99, 7.03), χ 2 = 16.92, P < 0.001]; having smoked > 10 cigarettes/day (74.4 versus 47.2%, aOR = 4.39, 95% CI = 2.22, 8.68), χ 2 = 18.18, P < 0.001); (2) perceiving themselves to be still very addicted to smoking (41.3 versus 26.2%, aOR = 2.89, 95% CI = 1.58, 5.30, χ 2 = 11.87, P < 0.001) and feeling extremely confident about staying quit (62.1 versus 36.6%, aOR = 3.22, 95% CI = 1.86, 5.59, χ 2 = 17.36, P < 0.001). Vapers were not more likely to report any urges to smoke than non‐vapers (27.7 versus 38.8%, aOR = 0.86, 95% CI = 0.44, 1.65, χ 2 = 0.21, P = 0.643). Conclusions While former smokers who currently vape nicotine daily report higher levels of cigarette smoking dependence pre‐ and post‐cessation compared with former smokers who are current non‐vapers, they report greater confidence in staying quit and similar strength of urges to smoke.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".