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Record W2593399534 · doi:10.5430/jnep.v7n8p27

“They are friendly but they don’t want to be friends with you”: A narrative inquiry into Chinese nursing students’ learning experience in Australia

2017· article· en· W2593399534 on OpenAlexvenueno aff
Carol Chunfeng Wang, Lisa Whitehead, Sara Bayes

Bibliographic record

VenueJournal of Nursing Education and Practice · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeNursingFocus groupPsychologyNurse educationMedical educationAnxietyPedagogyMedicineSociology

Abstract

fetched live from OpenAlex

There is increasing interest in the phenomena of international student mobility and the growing global demand for skilled nurses. Little is known, however, about the learning experiences of Chinese nursing students at Australian universities. This study begins to address this gap. A narrative inquiry methodology was employed. In-depth interviews and focus group discussions, along with field notes and observations were conducted with six Chinese undergraduate nursing students studying undergraduate nursing in Western Australia. Chinese nursing students in Australia experienced fear and anxiety, driven by unfamiliarity with the hospital environment, education methods, and assessment expectations. Clinical placement experiences in Australian health services were identified by participants as the most stressful learning experience. Forming friendships with domestic students was difficult and rare for these students: none made friends with local students or joined university groups. Despite the challenges they experienced, the participants were motivated and adaptive to a new culture and learning methods, and all, demonstrated academic success. This study provides new knowledge about the learning experiences of Chinese nursing students at Australian universities. Many of the issues identified relate to the wider discussion around effective support for international 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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.179
GPT teacher head0.587
Teacher spread0.409 · 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.

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

Citations8
Published2017
Admission routes1
Has abstractyes

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