Examining the Impacts of the COVID-19 Pandemic on Iraqi Refugees in Canada
Bibliographic record
Abstract
The COVID-19 pandemic has exacerbated health and social inequities among migrant groups more than others. Higher rates of poverty, unemployment, living in crowded households, and language barriers have placed resettled refugees at a higher risk of facing disparities during the COVID-19 pandemic. To understand how this most vulnerable population has been impacted by the ongoing pandemic, this study reports on the responses of 128 Iraqi refugees in the city of London, Ontario, to a survey on the economic, social, and health-related impacts that they have faced for almost two years since the beginning the pandemic. The analysis of the survey indicated that 90.4% of the study population reported having health concerns during the pandemic while 80.3% expressed facing financial distress. The results also show that 58.4% of respondents experienced some form of social isolation. These all suggest that refugees are faced with several barriers which can have a compounding effect on their resettlement experience. These findings provide resettlement and healthcare providers with some information that may assist in reducing the impact of COVID-19 and other possible health security emergencies on resettled refugees and their communities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".