Complexity of Nurse Practitioners’ Role in Facilitating a Dignified Death for Long-Term Care Home Residents during the COVID-19 Pandemic
Bibliographic record
Abstract
Due to the interplay of multiple complex and interrelated factors, long-term care (LTC) home residents are increasingly vulnerable to sustaining poor outcomes in crisis situations such as the COVID-19 pandemic. While death is considered an unavoidable end for LTC home residents, the importance of facilitating a good death is one of the primary goals of palliative and end-of-life care. Nurse practitioners (NPs) are well-situated to optimize the palliative and end-of-life care needs of LTC home residents. This study explores the role of NPs in facilitating a dignified death for LTC home residents while also facing increased pressures related to the COVID-19 pandemic. The current exploratory qualitative study employed a phenomenological approach. A purposive sample of 14 NPs working in LTC homes was recruited. Data were generated using semi-structured interviews and examined using thematic analysis. Three categories were derived: (a) advance care planning and goals of care discussions; (b) pain and symptom management at the end-of-life; and (c) care after death. The findings suggest that further implementation of the NP role in LTC homes in collaboration with LTC home team and external partners will promote a good death and optimize the experiences of residents and their care partners during the end-of-life journey.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 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".