Meaningful connections in dementia end of life care in long term care homes
Bibliographic record
Abstract
BACKGROUND: Most persons with dementia die in long term care (LTC) homes, where palliative approaches are appropriate. However, palliative approaches have not been widely implemented and there is limited understanding of staff and family experiences of dying and bereavement in this context. METHOD: This descriptive qualitative study explored family and staff experiences of end of life and end of life care for persons with dementia in LTC homes. Eighteen focus groups were conducted with 77 staff members and 19 relatives of persons with dementia at four LTC homes in four Canadian provinces. RESULTS: Three themes emerged: knowing the resident, the understanding that they are all human beings, and the long slow decline and death of residents with dementia. DISCUSSION: Intimate knowledge of the person with dementia, obtained through longstanding relationships, was foundational for person-centred end of life care. Health care aides need to be included in end of life care planning to take advantage of their knowledge of residents with dementia. There were unmet bereavement support needs among staff, particularly health care aides. Persons with dementia were affected by death around them and existing rituals for marking deaths in LTC homes may not fit their needs. Staff were uncomfortable answering relatives' questions about end of life. CONCLUSIONS: Longstanding intimate relationships enhanced end of life care but left health care aides with unmet bereavement support needs. Staff in LTC homes should be supported to answer questions about the trajectory of decline of dementia and death. Further research about residents' experiences of deaths of other residents is needed.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".