Uncovering risky behaviors of expatriate teenagers in the United Arab Emirates: A survey of tobacco use, nutrition and physical activity habits
Bibliographic record
Abstract
BACKGROUND: Tobacco use and unhealthy lifestyle habits amongst youth contribute to most major health issues in the United Arab Emirates (UAE) and worldwide. However up to date and comprehensive statistics are not available on the current behavior, experimentation and environmental influences on teenagers in the UAE's expatriate community, who are greatly impacted by the country's culture and environment, as well as bringing influences from their cultures of origin. Expatriates comprise a majority of the UAE population, making them an important subset of the population to study. METHOD: To address this gap in knowledge, a survey was conducted to collect information on tobacco use, physical activity and nutrition behaviors, anti-tobacco media/legislation effectiveness and health education gaps. RESULTS: Our results provide a summary on each of these topics with regards to ninth grade expatriates in the UAE. We offer the first statistics on dokha use in this age group and uncover signs of underlying eating disorders. CONCLUSIONS: In conclusion, we call for a tobacco use, nutrition and physical activity intervention targeted at this age group of UAE expatriates.
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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 | 0.001 |
| 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".