Use of electronic cigarettes among Romanian university students: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Because electronic cigarettes are relatively new, data on usage patterns and factors which influence them are sparse. Hence, this study aims at assessing awareness, beliefs about electronic cigarettes and experimentation with them among university students from Romania- a country where the sales and marketing of these products are widespread. Secondly, correlates of electronic cigarette experimentation will also be investigated. METHODS: A cross-sectional study was performed by means of anonymous questionnaires among 480 students, aged 19-24, from Cluj-Napoca, Romania, between April-May 2013. RESULTS: The results show that 92.5% of the students have heard about e-cigarettes; out of these, one quarter (53.3% of the smokers, 25% of the ex-smokers, 5.5% of the non-smokers) have tried electronic cigarettes at least once during lifetime. The results of the multinomial logistic regression point out that the correlates of electronic cigarette experimentation were: male gender, being a smoker of traditional cigarettes, having friends who experimented with electronic cigarettes, having stronger beliefs that electronic cigarettes could help them quit smoking and being less convinced that they are used only by smokers. The explained variance was 59%. CONCLUSIONS: The results underline the importance of addressing the issue of e-cigarette use through health education programs and regulatory interventions, since e-cigarettes are a reality faced by the Romanian youth.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".