Exploring Physical and Social Wellness of High School Students in Suva, Fiji
Bibliographic record
Abstract
BACKGROUND: Given the characteristics of the adolescence life stage, the physical and social wellness dimensions remain a challenge. The aim of this study was to determine adolescent perceptions of physical and social wellness in secondary schools in Fiji. MATERIALS & METHODS: Four purposively selected secondary schools in Suva, Fiji were part of this qualitative cross sectional study in 2019. Fijian students enrolled into years 11 to 13 in the selected schools were invited to participate. Participants were purposively sampled. Data was collected using a structured self-administered questionnaire. Descriptive statistics were generated through the Statistical Package for the Social Sciences software version 25. RESULTS: A total of 350 high school students, with the mean age of 17.5 (SD = ±0.9), participated in the study. The majority (48%) had very high scores for physical wellness and a fifth of the participants (28%) had low scores. In terms of social wellness, about a third (28.9%) scored low whilst the majority (40.3%) had high scores of social wellness. CONCLUSION: This study adds to the pool of knowledge for wellness increases chances of improvement in adolescent program development specifically in terms of physical and social wellness. Appropriate interventions are recommended to improve physical and social wellness in high school students in Fiji.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".