“It’s not just about you”: International students’ vulnerabilities and capacities during the first phase of the COVID-19 pandemic in Canada
Bibliographic record
Abstract
In Canada, the COVID-19 pandemic was initially characterized by emergency government responses that disrupted daily life, especially for marginalized groups. This study explored the vulnerabilities and capacities of international students studying at a university in Calgary, Canada during the first phase of the pandemic. Guided by the Capacities and Vulnerabilities Analysis framework, we thematically analyzed 11 semi-structured interviews with international students. We found that material vulnerabilities included balancing finances, housing conditions, lack of information, food inaccessibility, reliance on public transport, and poor mental health, social vulnerabilities included lack of social support, culture shock, and racism, and attitudinal vulnerabilities included "nowhere to go", feeling like a burden, and perception of Canada as safe. Material capacities included financial support, knowledge about pandemic, and mental health supports, social capacities included local social support and multilingualism, and attitudinal capacities included resilience, religious and spiritual beliefs, "it's not just about you", and reflexivity. We found overlapping and complex relationships between vulnerabilities and capacities, indicating that while international students' vulnerabilities were exacerbated and introduced challenges during the pandemic, students uniquely leveraged their capacities to offset and recover from challenges. Findings from this study may be informative for stakeholders involved in disaster responses, especially universities and governments, to support international students' capacities and address their vulnerabilities.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.017 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".