Enhancing nursing’s role in community-based palliative care: closing gaps to improve patient outcomes
Bibliographic record
Abstract
BACKGROUND: Nursing-led palliative care is essential for improving the quality of life of patients with life-limiting illnesses by addressing their physical, emotional and social needs. Effective symptom management, facilitated by nursing interventions, plays a critical role in this process. AIM: This study aimed to evaluate the effectiveness of a community-based palliative care program designed to enhance the role of nursing in closing existing gaps in care. METHODS: A quasi-experimental design with pre- and post-test assessments was conducted at the Oncology and Palliative Care Unit of Zagazig University Hospitals, Egypt. A convenience sample of 140 adult patients diagnosed with cancer or other life-limiting illnesses was recruited. Data were collected using three standardised instruments: the Palliative Outcome Scale (POS), the Edmonton Symptom Assessment Scale (ESAS) and the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). The community-based palliative care programme was delivered over eight weeks and focused on symptom management, emotional support, educational resources, and social and spiritual care. RESULTS: The intervention resulted in a significant reduction in symptom severity, with marked improvements in physical symptoms (mean score reduction from 3.5 to 1.0), psychological symptoms (from 5.3 to 2.5), emotional and spiritual needs (from 4.0 to 1.5), all statistically significant (p < 0.001). EORTC QLQ-C30 results showed improved quality of life, with physical functioning scores increasing from 60.0 to 80.0 and emotional functioning scores increasing from 55.0 to 75.0. Participants also reported improved perceptions of social support and general well-being, indicating an overall improvement in quality of life. CONCLUSION: This study highlights the positive impact of structured, nurse-led community palliative care interventions on patient outcomes and highlights the importance of nursing involvement in community engagement to provide comprehensive support for people with cancer and other life-limiting conditions. RECOMMENDATIONS: It is recommended that healthcare providers incorporate standardised assessment tools into routine palliative care practice and consider implementing similar community-based care programmes to improve the quality of care for patients with serious illness. CLINICAL TRIAL: No clinical trial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".