Factors That Contribute to the Mental Health of Black Youth during COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: The mental health of Black youth during the COVID-19 pandemic is potentially influenced by various systemic factors, including racism, socioeconomic disparities, and access to culturally sensitive mental health support. Understanding these influences is essential for developing effective interventions to mitigate mental health disparities. METHODS: Our project used a community-based participatory (CBP) research design with an intersectional theoretical perspective. An advisory committee consisting of fourteen Black youth supported all aspects of our project. The research team consisted of experienced Black researchers who also trained six Black youths as research assistants and co-researchers. The co-researchers conducted individual interviews, contributed to data analysis, and mobilized knowledge. Participants were recruited through the advisory committee members and networks of Black youth co-researchers and sent an email invitation to Black community organizations. Forty-eight Black identified were interviewed between the ages of 16 and 30 in Canada. The data was analyzed thematically. We kept a reflexive note throughout all aspects of the project. RESULTS: Participants reported significant challenges with online schooling, including a lack of support and access to resources. Lockdowns exacerbated stress, particularly for those living in toxic living/home environments. Financial burdens, such as food insecurity and precarious employment, were prevalent and exacerbated mental health challenges. Additionally, experiences of anti-Black racism and police brutality during the pandemic heightened stress and anxiety among participants. CONCLUSIONS: The findings underscore the complex interplay of systemic factors in shaping the mental health of Black youth during the COVID-19 pandemic. Addressing these disparities requires targeted interventions that address structural inequities and provide culturally competent support to mitigate the impact on mental well-being.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".