Modeling The Underlying Tobacco Smoking Predictors Among 1st Year University Students In Iran
Bibliographic record
Abstract
Background: There are scant studies on the prevalence and determinants of tobacco smoking among 1 st year university students in Iran. We aim to determine the prevalence of substance abuse and identify factors related with tobacco smoking in 1 st year students of Qazvin University of Medical Sciences (QUMS). Methods: A self-administered questionnaire was used to collect information on sociodemographic, cigarette smoking, hookah smoking, and related risk factors among 521 1 st year students in QUMS between January and February 2014. We used logistic regression to determine factors associated with substance abuse among students. Results: The descriptive statistics indicated that the prevalence of lifetime cigarette and hookah smoking was 8.6% (confidence interval [CI] 95%: 6.5–11.4) and 35.5% (CI 95%: 31.5–39.7), respectively. After adjustment for other factors, being male, the presence of any smoker in the family and having smoker friends were factors associated with cigarette and hookah smoking among students. Our findings also revealed the co-occurrence of risk-taking behaviors among students. Conclusions: Our study showed considerably low prevalence of tobacco smoking among 1 st year students. Longitudinal studies are necessary to approve the observed results of this study and thus allow for a certain generalization of the observations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".