The relationship between caregivers’ perceptions of end-of-life care in long-term care and a good resident death
Bibliographic record
Abstract
OBJECTIVE: Quality end-of-life (EOL) care is critical for dying residents and their family/friend caregivers. While best practices to support resident comfort at EOL in long-term care (LTC) homes are emerging, research rarely explores if and how the type of care received at EOL may contribute to caregivers' perceptions of a good death. To address this gap, this study explored how care practices at EOL contributed to caregivers' perceptions of a good resident death. METHOD: This study used a retrospective cross-sectional survey design. Seventy-eight participants whose relative or friend died in one of five LTC homes in Canada completed self-administered questionnaires on their perceptions of EOL care and perceptions of a good resident death. RESULTS: Overall, caregivers reported positive experiences with EOL care and perceived residents to have died a good death. However, communication regarding what to expect in the final days of life and attention to spiritual issues were often missing components of care. Further, when explored alongside direct resident care, family support, and rooming conditions, staff communication was the only aspect of EOL care significantly associated with caregivers' perceptions of a good resident death. SIGNIFICANCE OF RESULTS: The findings of this study suggest that the critical role staff in LTC play in supporting caregivers' perceptions of a good resident death. By keeping caregivers informed about expectations at the very end of life, staff can enhance caregivers' perceptions of a good resident death. Further, by addressing spiritual issues staff may improve caregivers' perceptions that residents were at peace when they died.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".