The experience of providing end-of-life care to a relative with advanced dementia: An integrative literature review
Bibliographic record
Abstract
The number of people with dementia is growing at an alarming rate. An abundance of research over the past two decades has examined the complex aspects of caring for a relative with dementia. However, far less research has been conducted specific to the experiences of family caregivers providing end-of-life care, which is perplexing, as dementia is a terminal illness. This article presents what is known and highlights the gaps in the literature relevant to the experiences of family caregivers of persons with dementia at the end of life. A thorough search of the Cumulative Index to Nursing and Allied Health Literature (CINAHL) and PubMed databases from 1960 to 2011 was conducted. Ten studies were identified that specifically addressed the experience of family caregivers providing end-of-life care to a relative with advanced dementia. Common themes of these studies included: 1) the experience of grief, 2) guilt and burden with decision making, 3) how symptoms of depression may or may not be resolved with death of the care receiver, 4) how caregivers respond to the end-stage of dementia, and 5) expressed needs of family caregivers. It is evident from this literature review that much remains to be done to conceptualize the experience of end-of-life caregiving in dementia.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".