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Record W2777646751 · doi:10.3390/bs8010002

The Length of Residence is Associated with Cardiovascular Disease Risk Factors among Foreign-English Teachers in Korea

2017· article· en· W2777646751 on OpenAlexaboutno aff
Obiang-Obounou Brice

Bibliographic record

VenueBehavioral Sciences · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsnot available
Fundersnot available
KeywordsAcculturationOverweightResidenceMedicineObesityEthnic groupImmigrationDemographyRisk factorDiseaseGerontologyEnvironmental healthGeographyInternal medicine

Abstract

fetched live from OpenAlex

Cardiovascular disease (CVD) is a group of disorders that involve the heart and blood vessels. Acculturation is associated with CVD risk factors among immigrants in Western countries. In this study, the association between acculturation and CVD risk factors was examined among English teachers from Europe and the USA living in Korea. English teachers were defined as those who reported their profession as “English Teacher”. Only English teachers from Europe (UK, and Ireland, n = 81) and North America (Canada and USA, n = 304) were selected. The length of residence and eating Korean ethnic food were used as proxy indicators for acculturation. Gender was associated with hypertension: 17.6% of males self-reported to have the cardiovascular risk factor when compared to females (7.4%). The length of residence in Korea was associated with hypertension (p = 0.045), BMI (p = 0.028), and physical inactivity (p = 0.046). English teachers who had been residing in Korea for more than five years were more likely to report hypertension (OR = 2.16; p = 0.011), smoking (OR = 1.51; p = 0.080), and overweight/obesity (OR = 1.49; p = 0.009) than participants who had been living in Korea for less than five years. This study found evidence of the healthy immigrant effect and less favorable cardiovascular risk profiles among English teachers who have lived in Korea for over five years.

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)0.001
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 score0.005
Threshold uncertainty score0.834

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.038
GPT teacher head0.289
Teacher spread0.251 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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