The Role Of Medical Education in Struggle Against Smoking: The Prevalance of Smoking And Related Factors in Medical Students, Çanakkale
Bibliographic record
Abstract
Introduction: The aim of this study is to investigate the prevalance of smoking and related factors among medical students of Canakkale Onsekiz Mart University Medical School. The results of our research are expected to shape the trainings about smoking prevention starting from our faculty and contribute to Global Health Professionals Survey data and discussions determined by WHO, CDC and Canadian Public Health Association. Methods: This is a cross-sectional study conducted at Canakkale Onsekiz Mart University Faculty of Medicine. The questionnaire including demographic characteristics and Beck Anxiety Inventory was applied between December 2018 - January 2019. The data of the study was analyzed with the statistical package program SPSS 20.0. Results: In this study, the number of medical students reached was 652. 52.6% of the students were female. 30.5% of the medical students were currently smoking. It was found that age (OR: 1.13 95% CI: 1.05-1.21), male gender (OR: 1.9 95% CI: 1.40-2.67) and boarding in high school (OR: 1.5 95% CI: 1.01-2.26) significantly increased the risk of smoking Discussion: The prevalence of smoking was high among Canakkale Onsekiz Mart University Faculty of Medicine students. The rate of smoking was increases during medical education. The literature suggests that smoking physicians cannot be effective in the struggle againts smoking. In medical education, trainings on struggle tobacco and tobacco products is insufficient. In addition, there should be gained to medical students with the knowledge and skills that can protect their own health and then advocate for anti-smoking campaigns in the community.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".