Critical Care Nurses’ Experiences With Death and Dying: A South African Perspective
Bibliographic record
Abstract
The aim of this study was to explore experiences of South African critical care nurses regarding grief, death and dying in a critical care environment. Data was collected using semi-structured interviews and was analyzed using Giorgi’s thematic data analysis method. Available literature suggests that critical care nurses have varied experiences in relation to their experiences in relation to end-of -life patient care. However, few studies have examined the involvement of South African intensive care nurses’ in caring for the dying patient, their grief, their reactions to death in the workplace and the extent to which their nursing practice is based on shared beliefs, experiences and attitudes. Findings from this study revealed many predisposing factors and circumstantial occurrences shaping both, the nature of care of the dying and subsequent grief that, affected the nurse. Repeated exposure to grief, leads to occupational stress and burn out, causing emotional disengagement from caring for the dying, which ultimately affect the quality of care rendered for both the dying patient and their family. Issues, such as communication, multicultural diversity, education and coping mechanisms are essential in nursing education and practice and nurses caring for the critically ill or dying patient, need to have support networks and strategies put in place, not only to assist in providing care, but also for their own emotional support and well-being.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".