Syrian refugees in Canada: a qualitative report of the impact of the COVID-19 pandemic on psychosocial adaptation
Bibliographic record
Abstract
Purpose Since March 2020, the global COVID-19 pandemic has disproportionately impacted refugees by compounding preexisting and systemic health, social and economic inequities. In Canada, approximately 50,000 Syrian refugees arrived between 2015 and 2020 and were in the process of rebuilding their lives when the pandemic started. This study aims to explore the impact of the COVID-19 pandemic for Syrian refugees in Canada and identify supports needed. Design/methodology/approach Drawing on frameworks for refugee psychosocial adaptation and social integration and a qualitative descriptive design, the study used thematic analysis to examine semi-structured interviews with 10 Syrians. Findings Findings indicated four themes that provide a snapshot of impacts relatively early in the pandemic: facing ongoing development, inequity and insecurity during integration; disruption of settlement, integration and adaptation due to the pandemic; ongoing adaptation and resilience during integration in Canada; and ongoing needs and solutions for integration and adaptation. Originality/value This study builds upon growing research concerning Syrian refugees and psychosocial adaptation, particularly during the pandemic. The findings highlight the impacts of the pandemic on a population already facing inequities in a resettlement country. While the findings emphasize the resilience of the Syrian refugee community, the study also demonstrates the need for ongoing supports and justice-oriented action to fulfill resettlement commitments, especially in the face of additional stressors like the COVID-19 pandemic. Implications for policy, practice and future research are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".