Unveiling the Impact: A Scoping Review of the COVID-19 Pandemic’s Effects on Racialized Populations in Canada
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to examine the impact of the COVID-19 pandemic on racialized communities and individuals in Canada. METHODS: This review followed the Joanna Briggs Institute (JBI) methodology and the PRISMA-ScR guidance on reporting scoping reviews. Ovid MEDLINE ALL, Embase Classic + Embase, CINAHL (Ebsco platform), PsycINFO, and Cochrane were searched for documents that were published after March 2020 and that reported on the social and economic impacts and health outcomes of the COVID-19 pandemic on generally healthy racialized populations that reside in Canada. SYNTHESIS: A total of 39 documents were included in this review. Our results show racialized communities faced greater social, economic, and health impacts from the pandemic. These impacts were manifested in the form of high COVID-19 morbidity and mortality rates, increased discrimination, worsening mental health, difficulty in accessing healthcare, and challenges related to accessing food and basic necessities. CONCLUSION: Canadian racialized groups have been inequitably affected by the COVID-19 pandemic due to pre-existing inequalities and emerging discrimination. Responsive policy action and robust pandemic preparedness efforts are indispensable in adopting a proactive stance to prevent racialized populations from bearing a disproportionate burden of negative health crises in the future. This necessitates addressing pre-existing disparities and targeting social and economic vulnerability areas. By doing so, we can mitigate the reported social, economic, and health impacts experienced by racialized groups, including challenges related to accessing basic necessities, deteriorating mental health, and barriers to healthcare access.
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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 | 0.019 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.020 | 0.031 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".