Age of tobacco smoking initiation among Iranian adults based on National and Subnational data from the 2021 STEPS survey
Bibliographic record
Abstract
Tobacco smoking is a significant global public health challenge, responsible for 8 million deaths annually. This study aimed to analyze the age of smoking initiation and its sociodemographic determinants among Iranian adults, based on data from the 2021 STEPwise approach to surveillance (STEPS) survey, to inform targeted public health interventions. The analysis of 27,874 adults revealed that the average initiation ages for cigarette and hookah smoking were 22.2 years and 24.9 years, respectively. Men typically began smoking earlier than women, with average ages of 21.9 years for males and 27.7 years for females. No significant differences were observed in smoking initiation ages between rural and urban areas. Education level and marital status were also influential, with the youngest initiators having 7-12 years of education and being single. This study highlights the significant roles of gender, education, and urban-rural differences in smoking initiation in Iran. The findings underscore the need for targeted public health strategies conforming to the WHO's MPOWER initiatives and the Framework Convention on Tobacco Control (FCTC). Increased investment in public health, focusing on education and preventive measures, is crucial to reduce early smoking initiation and its associated health risks, ultimately supporting broader health policy goals.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".