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Record W2473668682 · doi:10.1177/1010539516654540

Prevalence of Physical Activity and Sitting Time Among South Korean Adolescents

2016· article· en· W2473668682 on OpenAlexafffund
Eun‐Young Lee, Valerie Carson, Justin Y. Jeon, John C. Spence

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2016
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchCenters for Disease Control and Prevention
KeywordsSittingOverweightDemographyScreen timeMedicinePhysical activityNational Health and Nutrition Examination SurveyLow incomeGerontologySedentary behaviorHousehold incomeEnvironmental healthObesityGeographyPhysical therapyPopulationSocioeconomics

Abstract

fetched live from OpenAlex

This study aimed to describe physical activity (PA) and sitting time, and to examine associations between sociodemographic factors, weight status, PA, and sitting time among South Korean adolescents (12-18 years). Findings are based on self-report data from 638 participants in the 2013 Korea National Health and Nutrition Examination Survey. Only 4.9% of adolescents accumulated 60 minutes of moderate-to-vigorous PA daily. Adolescents spent 532.4 ± 9.3 mins/d sitting. After controlling for age and sex, individuals in higher income groups compared with the lowest income group, living in nonmetro Seoul compared with metro Seoul, and who were overweight compared with nonoverweight were more likely to meet PA guidelines. Participants in the highest income group compared with lowest income group, and residing in nonmetro Seoul compared with metro Seoul were more likely to be in the high sitting time group (>720 min/d) (P < .05). Increasing PA and reducing sitting should be a public health priority 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 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)0.001
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 score0.071
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.292
Teacher spread0.266 · 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

Citations22
Published2016
Admission routes2
Has abstractyes

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