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Record W3117842745 · doi:10.5539/gjhs.v13n2p48

Impact of Migration on the Eating Habit and Physical Activity Patterns among Saudi Students living in South Korea

2020· article· en· W3117842745 on OpenAlexvenueno aff
O. A. Hakim

Bibliographic record

VenueGlobal Journal of Health Science · 2020
Typearticle
Languageen
FieldNursing
TopicNutrition, Health and Food Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsHabitDescriptive statisticsPhysical activityCross-sectional studyMealDemographyGerontologySignificant differenceMedicinePsychologyEnvironmental healthPhysical therapySocial psychology

Abstract

fetched live from OpenAlex

This study investigates the effect of migration on eating habits and physical activity patterns of Saudi migrants living in South Korea. A cross-sectional study was conducted, and an online survey was prepared to assess participants’ demographic details, including; eating habits and physical activity pattern compared to pre-migration among 198 Saudi students. Data was analyzed through descriptive statistics and chi square. Saudi migrant students practice healthy habits such as; low frequency consumption of snacks and regular exercise habits. No significant difference was observed in the number and type of meal taken per day between the participants who lived less than three years in South Korea, in comparison to those who were living for more than three years. A significant increase in excessive exercise among students who lived in South Korea for more than three years in contrast to the students who lived less than three years. Findings suggested that more attention is needed to identify the nutritional need of international students living in South Korea.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.386
Teacher spread0.345 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes1
Has abstractyes

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