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Record W2962934324 · doi:10.5430/ijhe.v8n4p164

Longing for Independence, Yet Depending on Family Support: A Qualitative Analysis of Psychosocial Adaptation of Iranian International Students in Hungary

2019· article· en· W2962934324 on OpenAlexaffvenue
Sara Hosseini-Nezhad, Saba Safdar, Lan Anh Nguyen Luu

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Guelph
FundersNational Research, Development and Innovation OfficeEötvös Loránd Tudományegyetem
KeywordsSadnessThematic analysisPsychosocialHappinessPsychologyFeelingAdaptation (eye)Independence (probability theory)Qualitative researchSocial psychologyDevelopmental psychologySociologyAngerPsychotherapistSocial science

Abstract

fetched live from OpenAlex

International students experience psychosocial changes in response to their new environment, and their psychosocial adaptation is facilitated or hindered by various factors. This study aimed to examine the intercultural experiences of Iranian international students in Hungary. In-depth interviews were conducted with 20 Iranian students in Budapest, Hungary, and a thematic analysis employed to discern and interpret themes within the data. The thematic analysis identified three overarching themes: (1) Sojourn’s Experience as Self-Growth, (2) Uncertainty in Intercultural Interactions, and (3) Striving for Autonomous-Related Self. The data reported that Iranian students experienced more happiness in Hungary than sadness, and their motivation to live independently in Hungary while depending on family support acted as buffers against any negative psychological feelings. The findings of this study underline the significance of independence and family support as the influencing factors for psychosocial adaptation of Iranian students.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.320
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.050
GPT teacher head0.467
Teacher spread0.416 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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