The Involvement of Caregivers in the End-of-life Care of an Older Adult Living in a Long-term Care Home: A Qualitative Case Study with Nurses and Relatives
Bibliographic record
Abstract
BACKGROUND: A key role of nurses working in long-term care homes (LTCHs) is to promote the involvement of care partners in end-of-life (EOL) care. However, studies on the involvement of care partners in EOL care in LTCHs have focused on care planning and decision-making. While care partners can participate in other ways, it's unclear how they are currently involved in EOL care by staff. PURPOSE: We aimed to explore the involvement of care partners in the EOL care of an older adult living in a LTCH. METHODS: A qualitative case study was conducted. Data was collected from a sample of four nurses and three care partners, using sociodemographic questionnaires, individual semi-structured interviews, documents pertaining to the LTCH's philosophy for EOL care, and a field diary. RESULTS: The results of a thematic analysis showed the broad scope of care partners' possible involvement, including contributing to care, obtaining information, and being present. As there was some variation in care partners' desire to be involved, nurses seemed to rely on them to convey their wishes. To promote this involvement, some strategies aimed at health professionals and managers were suggested. CONCLUSIONS: These results can guide improvement in clinical practices and raise awareness on the EOL care experiences of care partners.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".