Syrian Refugees’ Experiences of the Pandemic in Canada: Barriers to Integration and Just Solutions
Bibliographic record
Abstract
Research has shown high levels of housing precarity among government-assisted refugees (GARs) connected to difficult housing markets, limited social benefits, and other social and structural barriers to positive settlement (Lumley-Sapanski, 2021). The COVID-19 pandemic has likely exacerbated this precarity. Research to date demonstrates the negative consequences of the COVID-19 pandemic for refugees and low-income households, including both health-related issues and economic challenges, that may exacerbate their ability to obtain affordable, suitable housing (Jones & Grigsby-Toussaint, 2020; Shields & Alrob, 2020). In this context, we examined Syrian government-assisted refugees’ experiences during the pandemic, asking: how the COVID-19 pandemic has impacted Syrian refugees’ experiences of housing stability. To examine this issue, we interviewed 38 families in Calgary, London, and Fredericton. Using a qualitative descriptive methodology for analysis and interpretation (Thorne et al., 1997), we found the liminality of settling as a GAR has been compounded by isolation, further economic loss, and new anxieties during the pandemic. Ultimately, for many participants, the pandemic has thwarted their housing stability goals and decreased their likelihood of improving their housing conditions. Based on our findings, we discuss potential policy and practice relevant solutions to the challenges faced by refugees in Canada during the pandemic and likely beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".