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Record W2554523963 · doi:10.5539/ijps.v8n4p107

Understanding Workplace Adaptation as an Acculturation Process: A Qualitative Examination of South Korean Highly Skilled Workers in Japan

2016· article· en· W2554523963 on OpenAlexvenueno aff
Geonsil Lee, Joonha Park, Lauren Ban

Bibliographic record

VenueInternational Journal of Psychological Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsAcculturationImmigrationPsychologyCoping (psychology)Social psychologyJob stressMental healthAdaptation (eye)Qualitative researchDemographic economicsJob satisfactionPolitical scienceSociologyClinical psychologySocial science

Abstract

fetched live from OpenAlex

<p>Although study on job stress and coping among Highly Skilled Migrants (HSMs) has been increasing around Anglo European countries, little is known about Asian migrants working in Asian countries. The present study examined stress factors among South Korean HSMs in Japan and explored their coping strategies in relation to acculturation processes. Semi-structured interviews with eight participants found three main domains affecting work adaptation-related stress: acculturation and adjustment, life events, and job stress. Job demand, relationship formation, and company climate were identified as major job stress factors. HSMs tended to perceive job stress factors as being related to a cultural difference or unique characteristics of Japanese organizations. This qualitative study addresses an initial step towards researching Asian migrant workers in Japan society, suggesting importance of incorporating culture-specific issues in acculturation processes with their job adjustment issues. It is necessary for immigration policy makers to encourage reciprocal understandings between migrants and local colleagues for improving mental health and well-being of both groups in organizations.</p>

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.280
GPT teacher head0.468
Teacher spread0.188 · 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 designQualitative
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

Citations7
Published2016
Admission routes1
Has abstractyes

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