Identification and validation of end-of-life nursing care competencies for Kenyan nurses: A modified Delphi technique
Bibliographic record
Abstract
Background: The number of patients requiring end-of-life care in acute care hospitals in Kenya continues to increase due to increases in non-communicable diseases. Despite advances in the field of palliative care (PC), its utilization is deficient in our setting, particularly at the end of life. Many patients and family members have unmet needs during the end-of-life phase. The aim of this study was to identify and validate end-of-life nursing competencies required by non-specialized nurses in Kenya. Validation of competencies for end-of-life care would help to develop relevant professional development programs in palliative care for these nurses. Methods: A two-round modified Delphi study was conducted. A 20-member panel of specialists in end-of-life care was involved in the identification and validation of the end-of-life nursing care competencies. Results: The results highlighted a total of eight core competencies namely: palliative nursing care; pain management; symptom management; ethical-legal issues; psychosocial, cultural and spiritual considerations; communication; loss, grief and bereavement; and death and dying. Additionally, 92 sub-competencies were identified that general nurses should possess within three domains of learning: knowledge (43 competencies), attitude (17 competencies), and practice/skills (32 competencies). Conclusions: The study forms a basis from which the identified end-of-life competencies can be utilized for continuous professional development programs for general nurses in acute care hospitals. Such programs could enhance the capacities of the general nurses and promote the integration of end-of-life care in acute care hospitals, thus improving the outcomes in quality of life for patients and families.
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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 | 0.054 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".