Nurses’ Perceptions around Providing Palliative Care for Long-Term Care Residents with Dementia
Bibliographic record
Abstract
Providing palliative care for residents with dementia in long-term care (LTC) settings is problematic due to their declining verbal abilities and related challenges. The goal of this study was to explore nurses' perceptions around providing palliative care for such residents. Using a qualitative descriptive design, data were gathered from focus groups at three LTC facilities. Participants represented all levels of nursing staff. Concepts that emerged from the data were labelled, categorized, and coded in an iterative manner. Nurses appraise residents' general deterioration as a main factor in deciding that a resident is palliative. Nurses often employ creative strategies using limited resources to facilitate care, but are challenged by environmental restrictions and insufficient educational preparation. However, nurses said they do not wish for residents to be transferred to a hospice setting, as they wish to grieve with residents and their family members. Nurses aim to facilitate a "good death" for residents with dementia, while trying to manage multiple demands and deal with environmental issues. Supportive and educational initiatives are needed for nursing staff and families of dying residents.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".