Smoking Cessation and Vaping Cessation Attempts among Cigarette Smokers and E-Cigarette Users in Central and Eastern Europe
Bibliographic record
Abstract
Our aim is to assess the smoking cessation and vaping cessation activity, including quit attempts and willingness to quit among university students in Central and Eastern Europe, as well as to investigate personal characteristics associated with smoking cessation and vaping cessation attempts. Data were collected by questionnaire which included 46 questions on cigarette and e-cigarette use. Questionnaires were obtained from 14,352 university students (aged 20.9 ± 2.4 years; cooperation rate of 72.2%). For the purposes of this analysis, only data from exclusive cigarette smokers (n = 1716), exclusive e-cigarette users (n = 129), and dual users (216) were included. Of all cigarette smokers, 51.6% had previously tried to quit smoking and 51.5% declared a willingness to quit cigarette smoking in the near future. Among all e-cigarette users only 13.9% had ever tried to quit using the e-cigarette and 25.2% declared a willingness to give up using e-cigarette in the near future. The majority of the group did not use pharmacotherapy to quit cigarette (87.5%) or e-cigarette (88.9%) use. Our results indicate that while most university students have some desire to quit conventional smoking, those who use e-cigarettes do not have the same desire.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".