Unequal Burdens: A Literature Review on COVID-19’s Impact on Black Mental Health in Canada
Bibliographic record
Abstract
Abstract Introduction: For the Black community, pre-existing challenges in the Social Determinants of Health (SDOH) were further stressed by COVID-19 pandemic-related stressors. With poor access to healthcare resources and inequitable social circumstances, the mental health of the Black community continues to deteriorate. Objectives: The purpose of this study is to elucidate how the COVID-19 pandemic impacted the mental health of the Black community within Canada through the exacerbation of social stressors. Methods: A literature review was conducted using PubMed, Google Scholar, and broad Google searches relating to the mental health state of Black Canadians during the pandemic. Searches were limited to 2019-2025, yielding four articles meeting the inclusion criteria. Results: The literature review produced four articles, all of which identified compounding effects that placed strain on SDOH and were found to be associated with poor mental health outcomes within the Black community. Some of these effects create obstacles that hinder efforts to improve poor mental health outcomes. Conclusion: Historically strained SDOH made the Black community more susceptible to the negative impacts of pandemic-related stressors, and there are structural and systemic barriers in place that require intervention. Clinicians can provide culturally competent care to address specific concerns in the Black community. Community initiatives can also integrate Black advocates and organizations to ensure applicability and cultural sensitivity to the particular needs of the Black community and increase utilization.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.022 | 0.030 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".