Prevalence and Socio-demographic Correlates of Cigarette Smoking, Alcohol Use, and Unsafe Sexual Behavior among Ethnic Fijian Secondary Schoolgirls.
Bibliographic record
Abstract
INTRODUCTION: The HEALTHY Fiji Study examines the impact of social transition on health risk behaviors among school-going ethnic Fijian adolescent girls. The primary aim of the present study was to assess prevalence and socio-demographic correlates of three risk behaviors, alcohol use, cigarette smoking, and unsafe sexual behavior in the study population. METHODS: We used an adapted version of the Global School-based Health Survey (GSHS) to assess health risk behaviors in a school-based sample of ethnic Fijian girls (n=523) in June and July 2007. We calculated prevalence of risk behaviors and then examined their relation to socio-demographic variables in logistic regression models. RESULTS: Prevalence estimates for any current alcohol use and cigarette smoking (20.1% and 17.6%) and lifetime history of sexual intercourse (20.8%) indicate that substantial percentage of this study sample has engaged in one of these health risk behaviors. Alcohol use was associated with two other risk behaviors, recurrent cigarette smoking and lifetime history of sexual intercourse. Although prevalence of alcohol use was lower than in several other Pacific populations, it was higher than previously reported among Fijian girls. CONCLUSIONS: The prevalence of alcohol use, cigarette smoking, and unsafe sexual behaviors in this study population warrants concern. Comparison with estimates from previous health behavior surveys in Fiji suggest that mode of assessment may impact prevalence estimates for health risk behaviors.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".