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Record W4200429249 · doi:10.1177/14705958211039071

‘Every day is a challenge’: Expatriate acculturation in the United Arab Emirates

2021· article· en· W4200429249 on OpenAlexaboutno aff
Alison Thirlwall, Dawn Kuzemski, Mahshid Baghestani, Margaret Brunton, Sharon Brownie

Bibliographic record

VenueInternational Journal of Cross Cultural Management · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsExpatriateAcculturationExploratory researchContext (archaeology)Public relationsLanguage barrierWork (physics)Foreign nationalQualitative researchPolitical scienceEconomic growthNursingBusinessPsychologySociologyMedicineGeographyImmigrationSocial scienceEngineering

Abstract

fetched live from OpenAlex

The United Arab Emirates (UAE) has a very small population of national citizens, so it relies on foreign workers who bring a range of cultures with them, resulting in a unique multi-cultural context. Unlike Western countries, such as the UK, Canada and Australia, workers are unable to permanently migrate to the UAE, so instead they hold temporary, expatriate status. This exploratory study focuses on the experiences of internationally qualified, expatriate nurses in hospitals in Al Ain, gathered by qualitative interviews. Twenty-one registered nurses participated in this study. The nurses faced challenges associated with language requirements and differing cultural expectations, and displayed limited acculturation, which compromised their ability to provide appropriate care for patients. The temporary nature of the work, cultural expectations, language difficulties and potential improvements are discussed. The findings have important implications for organizations that employ large groups of staff from overseas in all sectors. This article contributes to knowledge of expatriates’ challenges in the UAE and highlights the difficulties of working in a diverse environment, leading to a range of actions being recommended for managers.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.064
GPT teacher head0.402
Teacher spread0.338 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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