Using Intersectionality Theory to Explore the Impact of COVID-19 Pandemic on Black Canadian People's Health
Bibliographic record
Abstract
There is a general reluctance to confront the pervasive reality of anti-Black racism that further produces false narratives of inequities in the healthcare system relative to Black communities, especially in Western countries, including Canada. Despite Canada’s orientation towards an anti-Black racist agenda that aims to acknowledge the social determinants of health (SDOH) disparities experienced by the Black community during the coronavirus disease 2019 (COVID-19) pandemic, a greater robust discussion is warranted to address this longstanding discourse. In this conceptual paper, we draw upon intersectionality theory to shed light on the social determinants and inequities in health for Black Canadians. Informed by the literature, the authors discuss the historical context of systemic barriers and social injustices Black people face that are uniquely rooted in systems of oppression and anti-Black racism. Additionally, the importance of collecting and analyzing race-based data to prioritize the health concerns of Black people is emphasized. The article also espoused the need for healthcare service providers to advocate for culturally responsive and appropriate interventions like the Africentric model to inform policies, practices, and programs that promote the wellness of Black populations in Canada and beyond. Implications for healthcare service providers are highlighted with emphasis placed on a commitment to cultural humility in the support delivered within this diverse community. The paper concludes with a higher level of consideration to be given to the structural challenges experienced by Black Canadians in the healthcare system as we move towards a collective understanding to better serve this racialized group.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.027 | 0.037 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".