Decision Making at the End of Life in Dementia: How Family Caregivers Perceive Their Interactions With Health Care Providers in Long-Term-Care Settings
Bibliographic record
Abstract
Makingend-of-lifecare decisions in the context of dementiais complex. As people with advanced dementia are in capable of deciding about their own care, family caregivers often become involved with health care providers in the decision-making process to ensure the best care for their loved one. Using a grounded theory approach, the experience of family caregivers in making such end-of-life care decisions was explored. Twenty-four caregivers were interviewed. The results show that caregivers evoke five dimensions when considering these decisions. One dimension, the relationship with health care providers, emerged as vital to their experience. Four elements of this relationship are presented in this article: quality of the relationship, frequency of contact, congruence of their values and beliefs with those of health care providers, and the level of trust. In an era that promotes partnership with families in long-term-care settings, care standards are needed in order to guarantee family participation in achieving quality dementia 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".