MétaCan
Menu
Back to cohort
Record W4200580894 · doi:10.5539/gjhs.v14n2p8

Exploring Physical and Social Wellness of High School Students in Suva, Fiji

2021· article· en· W4200580894 on OpenAlexvenueno aff
Latileta Odrovakavula, Masoud Mohammadnezhad

Bibliographic record

VenueGlobal Journal of Health Science · 2021
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionDescriptive statisticsPsychologyPerceptionGerontologyCross-sectional studyMedicineNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.201
GPT teacher head0.548
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Journal of Health ScienceSame topicSports and Physical Education ResearchFrench-language works237,207