Investigating the Age of Becoming a Smoker and Its Related Factors Among Student Population: a Web-based Study
Bibliographic record
Abstract
Abstract Background: Preventing smoking at an early age is one of the primary solutions to reduce the likelihood of becoming a smoker in adulthood. This study aimed to investigate the age of becoming a smoker and its related factors and to assess the change in the age trend of becoming a smoker among students in Iran.Methods: A cross-sectional web-based survey was performed from July to August 2019 in Tabriz, Iran. A proportional cluster sampling in all universities of the city was implemented, according to the number of students in each university. The data were collected from 3640 students via an online survey questionnaire. Data analyses were performed by using Stata (version 16). The statistical level of significance was set at 0.05. Results: The average (±SD) age of becoming a smoker in the students was 18.8 (6 2.6) years. The age of becoming a smoker has decreased over time. A linear regression model showed that male and undergraduate students become smokers 0.77 and 0.50 years earlier than other students, respectively (P <0.001). Older age, being single, and later smoking initiation increase the average age of becoming a smoker by 0.2 years, 0.77 years, and 0.54 years, respectively (P <0.001).Conclusion: The age of becoming a smoker has decreased over time. Prevention programs should target males and undergraduate students. Furthermore, since students have become smokers earlier than their peers in the past, there is a direct relationship between smoking initiation and the age of becoming a smoker.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".