COVID-19 among Black people in Canada: a scoping review
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic exacerbated health inequities worldwide. Research conducted in Canada shows that Black populations were disproportionately exposed to COVID-19 and more likely than other ethnoracial groups to be infected and hospitalized. This scoping review sought to map out the nature and extent of current research on COVID-19 among Black people in Canada. METHODS: Following a five-stage methodological framework for conducting scoping reviews, studies exploring the effects of the COVID-19 pandemic on Black people in Canada, published up to May 2023, were retrieved through a systematic search of seven databases. Of 457 identified records, 124 duplicates and 279 additional records were excluded after title and abstract screening. Of the remaining 54 articles, 39 were excluded after full-text screening; 2 articles were manually picked from the reference lists of the included articles. In total, 17 articles were included in this review. RESULTS: Our review found higher rates of COVID-19 infections and lower rates of COVID-19 screening and vaccine uptake among Black Canadians due to pre-COVID-19 experiences of institutional and structural racism, health inequities and a mistrust of health care professionals that further impeded access to health care. Misinformation about COVID-19 exacerbated mental health issues among Black Canadians. CONCLUSIONS: Our findings suggest the need to address social inequities experienced by Black Canadians, particularly those related to unequal access to employment and health care. Collecting race-based data on COVID-19 could inform policy formulation to address racial discrimination in access to health care, quality housing and employment, resolve inequities and improve the health and well-being of Black people in Canada.
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 | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.031 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 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".