Vulnerable to Precarity: COVID-19 and the Experience of Difference by Newcomers, Immigrants, and Migrant Workers in Canada
Bibliographic record
Abstract
When COVID-19 struck Canada in 2020, immigrants, newcomers, and migrant (agricultural) workers were among those most vulnerable to the pandemic. Their experiences of the pandemic were accentuated by an exacerbation of pre-existing racial and other forms of discrimination. The article emerged from a systematic review and thematic synthesis of the broadly defined literature on immigrants, newcomers, and migrant workers’ experiences of multifaceted challenges amid the COVID-19 pandemic in Canada. We established inclusion criteria and systematically searched for articles in databases, including JSTOR Journals, Social Work Abstract (EBSCOhost), PsycINFO, and other grey literature published between March 2020 and January 2023. The findings suggest that immigrants, newcomers, and migrant workers in Canada experienced systemic inequalities, which worsened their socio-economic status, placing them at higher risks of poor health outcomes. The following themes that underscore the experiences of immigrants, newcomers, and migrant workers in Canada were identified: a) that immigrants, newcomers, and migrant workers in Canada experienced negative socio-economic impacts due to COVID-19, b) that immigrants, newcomers, and migrant workers in Canada experienced aggravated precarious and inequitable employment during COVID-19, c) that immigrants, newcomers, and migrant workers in Canada experienced COVID-19 related racial discrimination, and d) that COVID-19 negatively impacted immigrants, newcomers, and migrant workers’ mental health and well-being. Important directions for future research, including for studies that prioritize new immigrants, are provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".