The Prevalence of Cigarette and E-cigarette Smoking Among Students in Central and Eastern Europe—Results of the YUPESS Study
Bibliographic record
Abstract
Electronic cigarettes (e-cigarettes) are an alternative to traditional tobacco cigarette smoking. The aim of this study was to assess the prevalence of cigarette smoking and e-cigarette use among university students from Central and Eastern Europe and to investigate personal characteristics associated with cigarette and e-cigarette smoking. A questionnaire-based cross-sectional survey was performed between 2017–2018 among university students in five European countries: Belarus, Lithuania, Poland, Russia, and Slovakia. The questionnaire included 46 questions related to the frequency and habits of traditional cigarettes and e-cigarettes use. Completed questionnaires were obtained from 14,352 students (8800 medical; aged 20.9 ± 2.4 years) with an overall response rate of 72.2%. Two-thirds of the respondents had smoked a traditional tobacco cigarette and 43.7% had used an e-cigarette. Overall current smoking status included 12.3% traditional cigarette smokers, 1.1% e-cigarette users, and 1.8% were dual users with the remainder being non-smokers. Smoking status differed between the research centres (p < 0.001). Females were less likely to try either cigarettes (OR = 0.83) or e-cigarettes (OR = 0.62) and were less likely to be current cigarette (OR = 0.64), e-cigarette (OR = 0.34), or dual users (OR = 0.33) than males. Perception of e-cigarettes significantly differed between smokers and non-smokers (p < 0.001). Among university students, cigarettes are more popular than e-cigarettes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".