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Record W3035867985 · doi:10.5539/hes.v10n3p34

Challenges in Acculturation among International Students from Asian Collectivist Cultures

2020· article· en· W3035867985 on OpenAlexaffvenue
Kyunghee Ma, Ronald Pitner, Izumi Sakamoto

Bibliographic record

VenueHigher Education Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAcculturationCollectivismPsychologyMental healthSocial psychologyCoping (psychology)StressorContext (archaeology)Conceptual frameworkPopulationEthnic groupClinical psychologySociologyPolitical scienceSocial scienceIndividualismPsychotherapist

Abstract

fetched live from OpenAlex

Many international students coming to a U.S. university, especially those coming from a collectivist culture such as Asia, experience acculturation stress due to encountering different cultural norms and values. Lack of available resources may limit their coping ability, and prolonged exposure to acculturation stress may result in a decline in mental health. Asian international students may be at greater risk of developing mental health complications due to additional stressors derived from their cultures such as family recognition through success, emphasis on emotional self-control, and stigma toward mental illness. In this context, accumulated and unresolved acculturation stress may increase psychological vulnerabilities. Despite its relevance, there is no conceptual framework examining acculturation experiences of this student population. This article aims to present a conceptual framework of the acculturation process of Asian international students. Such a framework is important because it not only provides a holistic understanding of the acculturation process for Asian international students, but also provides an avenue for a comprehensive empirical inquiry. Furthermore, research-based evidence will help inform a more effective and inclusive university policy addressing the various needs of international students in order to provide intervention when necessary.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.150
GPT teacher head0.445
Teacher spread0.296 · 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

Citations77
Published2020
Admission routes2
Has abstractyes

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