Study of smoking behavior and smoking-related attitudes among preclinical medical students
Bibliographic record
Abstract
UNLABELLED: Despite the well-known fact that smoking is the most important cause of premature death, prevention of smoking in young people is still an unresolved public health problem. Smoking among medical students in particular should receive a special attention since smoking-related attitudes among health care professionals may act as role models among the patients. OBJECTIVE: The main goal of the present study was to detect preclinical (year 1 and year 2) medical students' smoking status, smoking frequencies and smoking-related attitudes in Szeged. METHOD: The whole sample consisted of 212 students, approximately 50% of the grades reported on their smoking status and smoking-related attitudes. Attitudes were measured by using a scale of The Students' Health and Lifestyle Study, The University of Western Ontario (Canada) that previously had been adapted. RESULTS: The data show that frequency of smokers did not decrease but slightly increased as compared to the research results from the 1990s. Neither year 1 nor year 2 students showed gender differences according to their smoking status. More nonsmoking students accepted their own role model. CONCLUSIONS: Besides interdisciplinary courses on the addictions, more practice-oriented and patient-centered courses are also needed to get the students familiar with basic principles of devices for smoking cessation. In addition, there is also a need for a program helping smoking medical students stop smoking.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".