Prevalence of smoking among Iranian university students: a systematic review and meta-analysis
Bibliographic record
Abstract
The tobacco epidemic is one of the biggest threats to public health globally. This study evaluates the prevalence of smoking among Iranian university students. A comprehensive search was conducted in international databases (Web of Science, Scopus, PubMed) and domestic databases (Magiran, SID, Irandoc). Cross-sectional studies in Farsi and English from 2012 to 2023 were included. The Newcastle–Ottawa scale was used to assess the quality of the articles. Heterogeneity was examined using Cochran’s test and the I 2 index. Due to high heterogeneity, a random effects model was employed to estimate smoking prevalence, and a funnel plot was used to assess publication bias. Out of 840 articles identified through the search, 149 records were removed as duplicates. Of the remaining 691 records screened for relevancy to the review question, 635 were excluded. Fifty-six reports were sought for full-text retrieval, and 54 full-text articles were successfully retrieved. However, 11 reports were excluded due to invalid data or insufficient information. Ultimately, 43 studies reporting the prevalence of smoking or the number of smokers in general, by gender, were included in the analysis. Specifically, 28 studies were included for males, 23 for females, and 43 for overall conditions. The overall prevalence of smoking was estimated to be 16% for both sexes combined, 26% for males, and 7% for females. The prevalence of smoking among male and female students is high. Due to the high heterogeneity of the studies, the results of this meta-analysis should be interpreted with caution. The study is registered in Prospero with the code CRD42021240264.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".