Challenges to improving end of life care of people with advanced dementia in the UK
Bibliographic record
Abstract
The end of life care received by patients with advanced dementia and their carers is of increasing importance as the incidence of dementia is set to rise in the next 30 years. Currently, inappropriate admissions to hospital are common in the UK and patients are less likely to be referred to palliative care services, receive less pain control but undergo more invasive interventions compared to their cognitively intact counterparts. Patients and families are seldom informed of the terminal nature of dementia and advance care planning discussions are rare. The aim of this study was to improve the understanding of end of life care needs for this patient group and their carers, and to use this information to devise an intervention to improve care. Qualitative data were obtained from relatives of 20 patients with advanced dementia admitted to an inner London teaching hospital acute National Health Service (NHS) Trust and 21 health care professionals involved in their care. Framework analysis was used to analyse the transcripts. The results showed that participants’ understanding of dementia and its likely progress was poor. Provision of information regarding the future was rare despite high information needs. Attitudes regarding end of life care were often driven by the participant’s illness awareness. These attitudes served to guide the decision making process and appear to be a major barrier to the provision of more appropriate care. Implications for patient care are discussed and suggestions for future interventions are made.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".