Cultural Perspectives and Needs in Dementia Care: Dialogues with African and African-Caribbean Communities in Nottingham
Bibliographic record
Abstract
Introduction: Ethnic minority communities in the UK experience persistent inequities in dementia care, including underdiagnosis, lack of access to culturally appropriate services, and systemic exclusion from research.Initially conceived as a study on eHealth and intersectionality in dementia, this Niels Stensen Fellowship project evolved in response to community engagement, centring the voices and lived experiences of Black African and African-Caribbean communities in Nottingham.This study aimed to reflectively explore how these communities understand dementia, identify their priorities and challenges, and reconceptualize "needs" through participatory, community-based dialogue using the theoretical frameworks of Paulo Freire and Max-Neef.Method: Two dialogue events were held in collaboration with a local African-Caribbean church and African community centre, involving 38 participants.Conversations focused on experiences with dementia, caregiving roles, stigma, cultural meaning, intergenerational dynamics, and the evolving needs of community members.Inductive thematic analysis of transcripts was followed by a short theoretical analysis applying Max-Neef's taxonomy of fundamental human needs.Discussion: Participants voiced widespread mistrust of formal care systems, highlighted gendered expectations around caregiving, and articulated a strong desire for culturally embedded, relational models of dementia support.The experience of dementia was often shaped by cultural expectations and gender roles, and viewed through relational and intergenerational lenses.Needs were expressed not just as service gaps but as unmet existential and axiological needs for affection, protection, identity, participation, and freedom.Conclusion: This study challenges the dominant biomedical and Eurocentric frameworks in dementia research.It underscores the importance of dialogical, community-driven approaches that value lived experience and cultural specificity.Public health practice must go beyond inclusion rhetoric to genuinely co-create knowledge and services with communities.Emancipatory dementia care demands trust-building, reflective listening, and recognition of evolving needs grounded in human dignity.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.049 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".