Obscenity of Cigarette and Hookah Smoking in Iranian Adolescents: A Longitudinal School-based Study
Bibliographic record
Abstract
BACKGROUND: = 4820) aged 14-19 years from Tabriz (a metropolitan city in northwestern Iran), this study aimed at comparing the obscenity of cigarette and hookah smoking and assessing factors associated with obscenity of smoking. Moreover, we examined how the obscenity of cigarette and hookah smoking affected by the progress in the stages of cigarette and hookah smoking. METHODS: In this longitudinal study, a random sample of high-school students was selected in Tabriz in 2010. Using a valid and reliable self-administered questionnaire, the data from the sampled students were collected twice at two points in time, 12 months apart. Multivariate backward logistic regression was used to determine the effect of the transition in cigarette (or hookah) smoking stages on the obscenity of cigarette (or hookah) smoking. RESULTS: While 3,079 (63.9%) students expressed that obscenity of cigarette smoking is higher than obscenity of hookah smoking, 1,741 (36.1%) students expressed that obscenity of hookah smoking is higher than obscenity of cigarette smoking. The results of multivariate backward logistic regression indicated that the transition in cigarette (hookah) smoking stages was not related to the obscenity of cigarette (hookah) smoking. CONCLUSIONS: The results showed that obscenity of hookah smoking was less than obscenity of cigarette smoking, especially among females. Further study is required to understand the effect of obscenity on smoking and transition to different stages of cigarette and hookah smoking.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".