Tobacco Use Patterns Among University Students in Herat, Afghanistan: A Cross-sectional Study
Bibliographic record
Abstract
Background: Tobacco use is highly prevalent in Afghanistan, posing a significant challenge among young people, including university students. This study aims to investigate tobacco product usage patterns and associated factors among male students at Herat University, Afghanistan, addressing the critical need for understanding and addressing this public health issue. Methods: In this cross-sectional study conducted between April and May 2021, 640 male university students were surveyed using interview-based stratified random sampling to assess cigarette, smokeless tobacco (ST), hookah, and e-cigarette use alongside sociodemographic factors. Logistic regression identified significant predictors. Findings: The prevalence was 35.3% for cigarette smoking, 15% for ST use, 14.1% for e-cigarette vaping, and 35.5% for hookah smoking. In the cigarette model, predictors included age (OR=1.20), mother's education (secondary/high school OR=2.19; university OR=2.68), friends' use (OR=9.54), and employment status (OR=2.52). The hookah model highlighted friends' use (OR=31.05), marital status (OR=2.10), employment status (OR=1.76), and mother's education (secondary/high school OR=2.18; university OR=3.57) as predictors. In the ST model, predictors were friends' use (OR=20.12), employment status (OR=3.37), and mother's education (secondary/high school OR=2.91). Lastly, the e-cigarette model revealed the predictors of friends' use (OR=7.91) and employment status (OR=1.87). Conclusion: Tobacco use among Afghan male university students is significantly influenced by peer behavior, employment status, and parental education. Interventions should target accessibility and sociocultural attitudes and include educational programs and policy measures to reduce tobacco consumption in the university setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".