The Burden of Smoking Among Medical Students in Jordan: Insights from a National Quota Sampling Study
Bibliographic record
Abstract
BACKGROUND: Smoking is a leading cause of preventable illness and death. Although medical students and physicians are generally aware of the health risks associated with smoking, some still engage in the habit. AIM: This study aimed to assess the prevalence of smoking among medical students in Jordan. METHODS: A quota sampling design was employed, involving 1,311 medical students from six Jordanian universities. Data were collected using a self-administered questionnaire comprising two sections: socio-demographic information and smoking-related variables. Quantitative data were initially recorded in continuous form and subsequently categorized for statistical analysis. RESULTS: The overall prevalence of smoking among the surveyed medical students was 19.67%. Among smokers, 83.3% were male and 16.5% were female. The highest prevalence of smoking in both sexes was observed in the 20–25 age group. Among male smokers, 41.6% reported smoking 20 or more cigarettes per day, whereas 67.6% of female smokers reported smoking 1–9 cigarettes per day. Additionally, 60.4% of male smokers and 50.0% of female smokers expressed a desire to quit. Notably, the success rate of quitting was higher among female smokers than among their male counterparts. CONCLUSION: Smoking prevalence was higher among male medical students compared to females. Peer influence was a more significant factor in smoking initiation among males, while family influence was more prominent among females. Although a greater proportion of male smokers expressed an intention to quit, female smokers had a higher rate of successful cessation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".