Youth Smoking Behavior and Policy Attitudes: A Study of High-School Students in the Maldives
Bibliographic record
Abstract
Saifudheen Faroog,1 Zainudheen Faroog,1 Abdul Rehman Zia Zaidi,2 Baraa Alghalyini2 1College of Medicine, Alfaisal University, Riyadh, Saudi Arabia; 2Department of Family and Community Medicine, College of Medicine, Alfaisal University, Riyadh, Saudi ArabiaCorrespondence: Baraa Alghalyini, Department of Family and Community Medicine, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia, Tel +966-555984622, Email balghalyini@alfaisal.eduBackground: Tobacco use significantly impacts health and economic sectors. In the Maldives, 4 out of 10 men smoke daily, despite anti-tobacco policies. The Maldives Global Youth Tobacco Survey (GYTS) shows fluctuating cigarette smoking prevalence among secondary school students: 6.9% (2004), 3.8% (2007), 4.3% (2011), and 4.7% (2019). No studies have investigated smoking prevalence and attitudes toward anti-smoking policies among higher-secondary students in Addu City. This study examines smoking habits, susceptibility, and attitudes toward anti-smoking regulations to support policy development.Methods: We conducted an observational cross-sectional study using a self-administered survey based on the GYTS and the Canadian Student Tobacco, Alcohol and Drugs Survey (CSTADS), involving 335 high school students in Addu City. Variables included sociodemographic factors, ever-smokers, current smokers, age at first cigarette, smoking dependency, use of other tobacco products, smoking susceptibility, willingness to quit, and reasons to quit or not smoke. Results with p-values < 0.05 were statistically significant.Results: 22.8% of the students had tried smoking, with 4.74% currently smoking, predominantly males. Additionally, 32.2% had tried e-cigarettes. Smoking susceptibility was 44.2%. Seven students showed smoking dependency, with a significant gender difference (75.4% boys vs 33.3% girls, p < 0.05). Among smokers, 20% wanted to quit, and 70% cited cost as a deterrent. Only 20% of smokers supported a total ban on smoking in media compared to 49.8% of non-smokers (p = 0.03). Non-smokers significantly supported anti-smoking measures (73% vs 12.5% of smokers).Conclusion: Cigarette smoking among high school students in Addu is below the national average, but the high number of ever-smokers and interest in smoking and e-cigarettes suggest potential future increases. Policymakers should enact stronger legislation, enforce age restrictions, raise tobacco taxes, and implement comprehensive smoking cessation programs to address tobacco use effectively.Keywords: cigarette smoking, higher-secondary school students, cigarette smokers, susceptible non-smokers, non-susceptible non-smokers, Anti-smoking policies, Maldives
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".