Traditional smoking and e-smoking among medical students and students-athletes – popularity and motivation
Bibliographic record
Abstract
Background.Of particular interest is a study of the consumption rates of some psychoactive drugs in a specific group of medical students and students-athletes, who are advocates of a healthy lifestyle according to their occupation.Objectives.The purpose of this paper was the evaluation of the prevalence of tobacco smoking and e-cigarette smoking (vaping) among medical students and students-athletes and the research of students' motivation and attitudes towards smoking in its various forms.Material and methods.1,725 medical students and students-athletes were surveyed.All the respondents were divided into 4 groups: exclusive tobacco smokers, exclusive e-cigarette smokers, dual smokers (both e-cigarette and tobacco cigarette users), non-smoking students who hadn't smoked for at least 12 months.Results.1,515 of the surveyed students (87.8%) declared themselves as non-smokers, 160 (9.3%) respondents smoked traditional cigarettes.E-cigarettes were used much less often than traditional cigarettes -50 respondents (2.8%).One-time tobacco smoking was recorded in the medical history of 992 (57.5%) of students; e-smoking -780 (45.2%).Statistically, men appeared to have been twice as common as women among both tobacco and e-cigarette smokers.Dual smokers used traditional cigarettes less often than electronic cigarettes.This group more often chose e-liquid with a higher level of nicotine.An attempt to stop smoking was the most important stimulus of opting for vaping (90.5%).Conclusions.Among the students of both groups, vaping is not frequent and not a popular practice compared to traditional tobacco smoking.Everyday smokers prevailed among dual smokers and not among exclusive e-cigarette smokers.E-smokers, more often than other respondents, believe that vaping is safe for their health and the health of others.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".