The provision of end-of-life care by medical-surgical nurses working in acute care: A literature review
Bibliographic record
Abstract
OBJECTIVE: Caring for terminally ill patients is complex, stressful, and at times distressing for nurses. Acute care hospitals continue to be the predominant place of death for terminally ill patients in most Western countries. The objective of the present literature review was to explore and gain an in-depth understanding of the experience of providing end-of-life (EOL) care by medical-surgical RNs working in acute care hospitals, to identify knowledge gaps, and to recommend future research. METHOD: A comprehensive literature review was conducted using the following electronic databases: CINAHL, MEDLINE, and PsyInfo (from 1992 to October 2012). RESULTS: The findings from the 16 reviewed studies suggest that nurses felt a strong commitment to help terminally ill patients experience a good death. Nurses reported feeling deeply rewarded and privileged to share the EOL experience with patients/families. Organizational and individual factors influenced nurses' experience. Important challenges were associated with managing the divergent needs of a mixed patient load (i.e., curative and palliative care patients) in a biomedical culture of care that is heavily oriented toward cure and recovery. In this culture, nurses' emotional work and ideals of good EOL care are often not recognized and supported. SIGNIFICANCE OF RESULTS: Managerial and organizational support that recognize the centrality of emotional work nurses provide to dying patients is needed. More research exploring ways to improve communication among nurses and medical colleagues is essential. Finally, a critical examination of the ideological assumptions guiding nurses' practice of EOL care within the context of acute care is recommended to help reveal their powerful influence in shaping nurses' overall understanding and experience of EOL care.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".