Impact of the COVID-19 Pandemic on Black Communities in Canada
Bibliographic record
Abstract
The COVID-19 pandemic has resulted in differential impacts on the Black communities in Canada and has unmasked existing race-related health inequities. The purpose of this study was to illuminate the impacts of the COVID-19 pandemic on Black people in Canada. Historically, social inequalities have determined the impacts of pandemics on the population, and in the case of the COVID-19 pandemic, disproportionate infections and mortalities have become evident among racialized communities in Canada. This qualitative descriptive study utilized an intersectionality framework. We invited Black stakeholders across Canada to participate in semi-structured interviews to deepen our knowledge of the impacts of the COVID-19 pandemic on Black communities in Canada. A total of 30 interviews were recorded, transcribed verbatim, and analyzed using content analysis. Our findings fell into three categories: (1) increased vulnerability to COVID-19 disease, (2) mental impacts, and (3) addressing impacts of the COVID-19 pandemic. The findings show the underlying systemic inequities in Canada and systemic racism exacerbated health inequities among the Black communities and undermined interventions by public health agencies to curb the spread of COVID-19 and associated impacts on Black and other racialized communities. The paper concludes by identifying critical areas for future intervention in policy and practice.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".