<i>“You Have to Be There”</i> : Emotional Labor, Cultural Ethics, and the Realities of Black Families of people with Dementia in Canada During COVID-19
Bibliographic record
Abstract
report. Despite this forecast, there remains a significant empirical gap regarding the lived realities of Black individuals with dementia and their care partners. This study addresses that gap by exploring the everyday realities of dementia care within Black communities in the Greater Toronto and Hamilton Area, particularly during the COVID-19 pandemic. Utilizing a qualitative interpretive lens, the study purposively sampled 24 participants (12 older adults with moderate dementia and their 12 care partners) for in-depth, dyadic interviews. Data were analyzed thematically using a reflexive lens. Findings indicate that care partners, whether care partners or healthcare professionals, take on extensive responsibilities in decision-making, daily care provision, and emotional support, which are shaped by pre-existing systemic inequities, including exposure to racial discrimination, and inadequate access to culturally responsive services. The pandemic further intensified these stressors, yet participants demonstrated resilience by drawing on communal resources and informal networks. The findings revealed that care-partners of people with dementia operate within a system that both relies on and marginalizes their labour. Yet, care-partners resisted these exclusions through collective resilience and community-based strategies. This study advocates for improving equitable access to services, reducing systemic barriers that delay recognision, and acknowledging the essential contributions of Black care-partners in Canadian caregiving framework.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".