Immigrants’ and refugees’ experiences of access to health and social services during the COVID-19 pandemic in Toronto, Canada
Bibliographic record
Abstract
OBJECTIVE: In 2020, the World Health Organization reported that immigrants were the most vulnerable to contracting COVID, due to a confluence of personal and structural barriers. This study explored how immigrants and refugees experienced access to health and social services during the first wave of COVID-19 in Toronto, Canada. METHODS: This study analyzed secondary data from a qualitative study that was conducted between May and September 2020 in Toronto that involved semi-structured interviews with 72 immigrants and refugees from 21 different countries. The secondary data analysis was informed by critical realism. RESULTS: The vast majority of participants experienced fear and anxiety during the COVID-19 outbreak but through a combination of self-reliance and community support came to terms with the realities of the pandemic. Some even found the lifestyle changes engendered by the pandemic a positive experience. CONCLUSIONS: Self-reliance may hinder help-seeking and augment the threat of COVID-19. This is particularly a concern for the most vulnerable immigrants, who experience multiple disruptions in their health care, have limited material resources and social supports, and perhaps are still dealing with the challenges of settling in the new country.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".