Sociodemographic predictors of tobacco smoking among expatriate and national adolescents in the United Arab Emirates
Bibliographic record
Abstract
BACKGROUND: Tobacco use among adolescents is an important public health concern as it causes various forms of smokingrelated health problems and can create a gateway for other substance abuse. AIM: This study examined the prevalence, profile and predictors of tobacco use among expatriate and national adolescents living in the United Arab Emirates (UAE). METHODS: Using a cross-sectional study design (2007-2009), we collected data on the prevalence of tobacco use in 6363 adolescents aged 13-20 years, including current smokers of cigarettes, midwakh, shisha and any other form of tobacco. We also collected demographic, socioeconomic, residential and behavioural data. RESULTS: In the previous 30 days, 505 (8.9%) participants had smoked cigarettes, 355 (6.3%) had smoked midwakh, 421 (7.4%) had smoked shisha and 380 (6.4%) had smoked any other form of tobacco. Overall, 818 (14.0%) adolescents were current smokers, who reported occasional or daily use of at least one form of tobacco in the past 30 days. Results consistently indicated that the prevalence of tobacco use was higher among men than women, regardless of age and tobacco form. Among men, cigarette smoking was the most popular, whereas shisha was the most smoked form of tobacco among women. Being male and ever having used illegal drugs consistently emerged as significant predictors of all forms of tobacco use. CONCLUSION: There is a need for continued public health strategies and education campaigns to discourage adolescents in the UAE from using tobacco.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".