3190 End-of-life dementia care: a qualitative study of the experiences and perceptions of minority ethnic and economically disadvantaged communities
Bibliographic record
Abstract
Abstract Background Dementia is a leading cause of death globally. However, people living with dementia are often underrepresented in specialist palliative care services. Existing research on palliative care for people with dementia frequently fails to include people from minority ethnic groups and those living in poverty. Aims This study explored the experiences and perceptions of end-of-life dementia care among underserved groups in England. The study also investigated how ethnicity, religion, and socioeconomic status influence these experiences. Methods Ten workshops were conducted, involving 29 Experts-by-Experience (EbE) with professional or personal experience of caring for people living with dementia from disadvantaged communities. Qualitative data from these workshops were analysed thematically. Results The findings highlight cultural, socioeconomic, and systemic barriers to accessing quality end-of-life care. Participants noted pervasive fear, stigma, and mistrust surrounding dementia and end-of-life care. Financial concerns were frequently described as major drivers of inequities in care. Conclusions This study reveals that individuals from minority ethnic and disadvantaged communities face significant challenges in accessing equitable, high-quality end-of-life dementia care. Future research should co-create culturally sensitive interventions with these communities to address disparities in care.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".