Dohányzási szokások a marosvásárhelyi orvostanhallgatók körében
Bibliographic record
Abstract
Introduction: Smoking is a major problem worldwide, especially among students. Smoking is one of the most dangerous social phenomena and has a significant impact on public health. Even though students with medical knowledge are expected to play a preventive role, the prevalence of smoking among them is significant. Objective: The aim of this study was to assess the smoking habits, knowledge and attitudes of medical students in Târgu Mureș, comparing them with national and global statistical data. Material and method: A cross-sectional study was conducted among medical students at the University of Medicine, Pharmacy, Science and Technology of Târgu Mureș between January and March 2025 using an anonymous, online questionnaire. IBM-SPSS v. 23 software was used for statistical calculations. Significant difference was considered at p<0.05. Results: From a total of 226 students, the prevalence of smoking was 39.38%. 21.2% of them reported smoking daily, and 4 students smoked more than one pack per day. 61.1% of students tried electronic cigarettes. The main motivation for trying them was curiosity (41.2%), but stress management (11.1%) and peer pressure (11.1%) also played an important role (p<0.001). The first time they tried a tobacco product was mostly (35.8%) between the ages of 15 and 18 years, but 13.3% tried it before the age of 15 years. 83.6% of students are aware of the role of nicotine in causing addiction. The health risks of smoking (e.g., cancer, cardiovascular and respiratory diseases) were recognized by 90.7% of students. A quarter of students are aware of the EVALI (electronic cigarette or vaping use-associated lung injury) phenomenon. 65.5% of the students did not participate in a prevention program, and 67.3% obtain information about smoking from the Internet. Conclusion: The prevalence of smoking among students is worrying, especially because they will be our future doctors. There is a need to launch plans to reduce smoking among students, which can be incorporated into courses and special programs. The results obtained show that, despite numerous campaigns, a significant proportion of medical students don’t receive reliable information about smoking. Orv Hetil. 2025; 166(38): 1496–1506.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".