The impact of COVID-19 on the social and cultural integration of international students: a literature review
Bibliographic record
Abstract
Abstract This systematic literature review summarises the state-of-the-art evidence on the impact of COVID-19 on the integration of international students in their host countries and institutions. Conducted between January and May 2022, it analyses the responses to COVID-19 of the key actors involved in international student mobility: national/regional authorities, higher education institutions, and students. Findings reveal that governmental action and institutional measures were decisive in shaping international students’ integration experiences. Regarding governmental action, criticism of the policies adopted by Australia and the USA in relation to immigration and/or support stand out, in contrast to policies adopted by the Canadian authorities. Higher education institutions played an important role in mitigating the negative effects of COVID-19 on international students’ integration. These targeted different needs– material, well-being, and social– through different types of support: logistical and financial support, psychological support, and the provision of platforms for ongoing social interaction and exchange. Most studies, however, focus on the students themselves, the challenges they faced during the pandemic and their coping strategies. Common to international students’ lived experience was (dis)connectedness, with the following themes emerging as obstacles to their social and cultural integration: distress during lockdown periods, disruption of their social life and support networks, mental health issues, discrimination and racialised prejudice, and language barriers. The review concludes by proposing recommendations and by identifying avenues for future research.
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 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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".