Susceptibility to tobacco use and associated factors among youth in five central and eastern European countries
Bibliographic record
Abstract
Abstract Background Tobacco use among young people still remains a major public health problem. Thus, the aim of this study was to perform a cross-country comparison for the factors associated with susceptibility to tobacco use among youth from five central and eastern European countries. Methods The data used in the current analysis, focusing on youth (aged 11–17 years), who have never tried or experimented with cigarette smoking, was available from the recent Global Youth Tobacco Survey (Czech Republic (2016), n = 1997; Slovakia (2016), n = 1998; Slovenia (2017), n = 1765; Romania (2017), n = 3718; Lithuania (2018), n = 1305). Simple, multiple logistic regression analyses and random-effect meta-analysis were conducted to identify factors associated with tobacco use susceptibility as the lack of a firm commitment not to smoke. Results Nearly a quarter of the students were susceptible to tobacco use in 4 of 5 countries. The following factors were identified, consistently across countries, as correlates of tobacco use susceptibility: exposure to passive smoking in public places (AOR from 1.3; p = 0.05 in Slovakia to 1.6; p 1.5; p 0.05). Youth who share the opinion that people who smoke have more friends were more susceptible to smoking in Romania (AOR 1.4; p = 0.04) but tend to be less susceptible in other countries. Exposure to advertisements at points of sale was significant correlate of tobacco use susceptibility in Slovakia and Slovenia (AOR 1.4 and 1.5 respectively; p
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".