Factors associated with substance use among preclinical medical students in Turkey: a cross-sectional study
Bibliographic record
Abstract
Introduction: Medical students experience high levels of stress due to their rigorous training, which can negatively affect their mental health. This study aimed to investigate substance use habits of medical students at Istanbul University-Cerrahpaşa and the association on their mental health and demographic factors. Methods: This cross-sectional survey study was conducted in March-April 2022 among preclinical medical students (years 1-3 of a 6-year program). A confidential, anonymous online survey consisting of four sections on sociodemographic and educational characteristics, nicotine use and dependence [Fagerström Test for Nicotine Dependence (FTND)], alcohol use [Alcohol Use Disorders Identification Test (AUDIT)], mental health status [12-item General Health Questionnaire (GHQ-12)], was distributed to 1131 students via WhatsApp and Telegram text messages. Mann-Whitney U and Kruskal Wallis tests compared variables’ distribution in the questionnaire categories. Spearman's correlation assessed associations between scales. Significance was p < 0.05. Results: The study included 190 medical students. A total of 26.3% of the participants were smokers, with 8.4% showing moderate to high levels of nicotine dependence. An estimated 45.8% and 8.4% reported low-risk consumption and risky usage of alcohol, respectively. There were statistically significant associations between substance use and demographic factors such as sex, GPA, and religious belief. The study found a statistically significant correlation between FTND scores and GHQ-12 scores, and, between FTND scores and AUDIT scores. Conclusion: The findings of this study will inform the development of interventions to improve the mental health and academic performance of medical students at Istanbul University-Cerrahpaşa. Furthermore, it will raise awareness about the importance of addressing substance use among medical students in Turkey.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".