Resilience during the COVID-19 Pandemic: Vulnerabilities and Capacities of International Students
Bibliographic record
Abstract
Background: The COVID-19 pandemic has amplified the vulnerabilities of marginalized groups such as migrants, ethnic minorities, and low socioeconomic populations. International students are often members of these marginalized groups. Yet, international students’ vulnerabilities and capacities during disasters remain overlooked. The aim of this study was to explore the material, social, and attitudinal vulnerabilities and capacities of international students studying at a postsecondary institution in Calgary, Canada during the COVID-19 pandemic. Methods: We conducted a qualitative descriptive study involving 11 semi-structured interviews with international students who were studying at a postsecondary institution in Calgary at the time the pandemic disrupted in-person learning. Data were analyzed using deductive thematic analysis and guided by the Capacities and Vulnerabilities Analysis framework. Results: International students’ 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 feeling there is “nowhere to go”, feeling like a burden, and perceiving 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, the perception that “it’s not just about you”, and reflexivity. Conclusion: While international students were vulnerable to the impacts of the COVID-19 pandemic, their capacities uniquely facilitated their ability to cope. International students’ capacities should be leveraged in disaster responses to sustainably alleviate 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| 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".