Resilience Amidst Adversity: Experiences of Black Older Adults During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The paucity of research and policy on the impact of coronavirus disease 2019 (COVID-19) on the experiences of Black older adults in Canada and around the world has intensified the enduring impacts of racism on their health and well-being. To bridge this gap, our study explored the mental health of Black older adults in Montreal during the early period of the pandemic. RESEARCH DESIGN AND METHODS: Using an Afro-emancipatory mixed-method research design, we collected and analyzed data from 3 sources: a survey, focus group interview with service providers from Black community organizations, and individual interviews with Black older adults. RESULTS: Our findings reveal that Black older adults struggled with mental health challenges, including loss, grief, and intergenerational tensions, and encountered systemic barriers in accessing services. Despite these adversities, participants demonstrated remarkable resilience, drawing upon their faith and community networks for support. DISCUSSION AND IMPLICATIONS: This study illuminates the complex experiences of Black older adults during the pandemic and underscores the imperative of addressing mental health and systemic barriers. Understanding ongoing challenges is crucial for developing targeted interventions and policies that promote long-term resilience and equitable healthcare for Black older adults.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".