Tracing Chinese international students’ psychological and academic adjustments in uncertain times: An exploratory case study in the United Kingdom
Bibliographic record
Abstract
The worldwide spread of COVID-19 has exerted tremendous influences on the wellbeing of international students and the sustainable development of higher education. The current study adopts an 8-month exploratory case study to trace eight Chinese international students' psychological and academic adjustments in the United Kingdom amid the COVID-19 pandemic. Emerging from the qualitative data constitutive of semi-structured interviews, self-reflection writings, memoing, together with stimulated-recall interviews, findings have demonstrated the three main types of obstruction for such students' adjustments in the foreign land including COVID-specific challenges (i.e., the threat of infect, reduced access to university facilities and resources); COVID-enhanced challenges (i.e., anxiety exacerbated by parents and social media use, anti-Asian racism and hate incidents); and language barriers and cultural differences as long-standing issues. Students' previous lockdown experience, individual resilience, development of monocultural friendship patterns, and institutional provision and support are all factors that have contributed to their ability to overcome or at least mitigate the psychological and academic difficulties. The study offers insight into the impacts of COVID-19 on international students, providing implications that could contribute to the sustainable adjustments of international students in times of disruptive events and inform future responses to global health crises from individual and higher education perspectives.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".