“They are friendly but they don’t want to be friends with you”: A narrative inquiry into Chinese nursing students’ learning experience in Australia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".