Youth health-risk behavior assessment in Fiji: the reliability of Global School-based Student Health Survey content adapted for ethnic Fijian girls
Bibliographic record
Abstract
OBJECTIVE: The Global School-based Student Health Survey (GSHS) is an assessment for adolescent health-risk behaviors and exposures, supported by the World Health Organization. Although already widely implemented - and intended for youth assessment across diverse ethnic and national contexts - no reliability data have yet been reported for GSHS-based assessment in any ethnicity or country-specific population. This study reports test-retest reliability for GSHS content adapted for a female adolescent ethnic Fijian study sample in Fiji. DESIGN: We adapted and translated GSHS content to assess health-risk behaviors as part of a larger study investigating the impact of social transition on ethnic Fijian secondary schoolgirls in Fiji. In order to evaluate the performance of this measure for our ethnic Fijian study sample (n=523), we examined its test-retest reliability with kappa coefficients, % agreement, and prevalence estimates in a sub-sample (n=81). Reliability among strata defined by topic, age, and language was also examined. RESULTS: Average agreement between test and retest was 77%, and average Cohen's kappa was 0.47. Mean kappas for questions from core modules about alcohol use, tobacco use, and sexual behavior were substantial, and higher than those for modules relating to other risk behaviors. CONCLUSIONS: Although test-retest reliability of responses within this country-specific version of GSHS content was substantial in several topical domains for this ethnic Fijian sample, only fair reliability for the module assessing dietary behaviors and other individual items suggests that population-specific psychometric evaluation is essential to interpreting language and country-specific GSHS data.
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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 | 0.006 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".