Palliative and end-of-life care for people living with dementia in rural areas: A scoping review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: People living with dementia deserve to experience the benefits of receiving palliative care and end-of-life services and supports, yet they often do not receive this care compared to those with other terminal diseases. People living with dementia in rural areas often face additional challenges to accessing such care. The purpose of this scoping review was to systematically review and synthesize the literature on palliative and end-of-life care for people with dementia living in rural areas, and to identify and describe key findings and gaps in the literature. METHODS: A collaborative research team approach was used in an iterative process across all stages of this review. Systematic, comprehensive searches were conducted across ten databases and eight targeted websites for relevant peer-reviewed, original research and other less formal literature, published in English, which yielded a total of 4476 results. After duplicate removal, screening, and review, 24 items were included for synthesis. RESULTS: All items were described and illustrated by frequency distribution, findings were grouped thematically, and five key themes emerged, including: 1) Knowledge about dementia, 2) Availability, accessibility, and utilization of palliative and end-of-life care services and supports, 3) Decision-making about care, the value of a person-centered approach and collaborative support, 4) Perspectives on artificial nutrition, hydration, and comfort care, and 5) Quality of life and death. The main gap identified was literature pertaining to rural populations, especially from locations other than the United States. The influence of rurality on relevant findings was mixed across rural-urban comparison studies, as was the effect of sex and gender across the literature. CONCLUSIONS: Several areas were highlighted including the importance of increasing knowledge about dementia, having early conversations about advanced care and treatment options, providing a person-centered approach, and the potential for using technology to address rural access issues. These findings can be used to inform future research and policy and the development of services, supports, and strategies for rural people living with dementia. Further research is recommended.
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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".