“Dual Pandemics”: Intersecting Influences of Anti-Black Racism and the COVID-19 Pandemic on the Mental Health of Black Youth
Bibliographic record
Abstract
BackgroundThe intersection of the COVID-19 pandemic with systemic anti-Black racism in the form of police violence and the subsequent Black Lives Matter movement has created an especially critical juncture to examine the mental health of Black youth.PurposeThe purpose of this study was to understand the intersecting impacts of anti-Black racism and the COVID-19 pandemic on the mental health of Black youth.MethodsA youth-engaged research approach and intersectionality framework were utilized. Semi-structured interviews with Black youth across Canada (ages 16 to 30, n = 48) were conducted online via Zoom and analyzed using thematic analysis. Fourteen Black youth were hired to form a Black youth advisory committee, who guided the research process.ResultsThe "dual pandemics" of the COVID-19 pandemic alongside highly publicized incidents of racism and the subsequent Black Lives Matter movement negatively impacted participants' mental health. Four main themes emerged: (1) ongoing exposure to acts of anti-Black racism (2) compounding effect of racism on mental health; (3) high stress levels and fear; and (4) anger and emotional fatigue from lack of shared, long-term solutions.ConclusionThe simultaneous occurrence of the COVID-19 pandemic and instances of systemic anti-Black racism in the form of police violence presented "dual pandemics" for the Black community, profoundly impacting the mental health of Black youth and adding urgency and impetus to its prioritization. Results of this study indicate that it is critical to explore each event individually as well as the combined impact on the mental health of Black youth, particularly from a racial perspective.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".