“There’s a Certain Loneliness of Being in a Space That Does Not Relate to You”: The Resilience and Mental Health Experiences of International Students During the COVID-19 Pandemic
Bibliographic record
Abstract
Research indicates that the adverse effects on post-secondary students from the COVID-19 pandemic are unprecedented on a global scale. Specifically, there is limited research that focuses on international students’ mental wellness, resilience, and well-being experiences during the COVID-19 pandemic. This study qualitatively explores the resilience and mental wellness experiences of international university students at a mid-size, research-intensive, public university in British Columbia, Canada. Nine international students, between the ages of 18 and 30, participated in narrative-style interviews. Data were analyzed by using thematic analysis and applying a resilience lens framework. The findings highlight students’ mental wellness challenges and the key factors that were instrumental for supporting their mental wellness and enacting their resilience. These findings help to mitigate the negative impacts that can result from studying during a pandemic and offers recommendations for universities on how to support international students’ overall wellbeing, particularly during significant disruption and isolation.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".