Features of the Nurse-Patient Relationship: Insights from a Qualitative Review Using Artificial Intelligence Interpretation
Bibliographic record
Abstract
INTRODUCTION: This qualitative literature review explored the intersection of art, creativity, and the nurse-patient relationship in the context of oncology nursing. It delved into the perceptions and reflections of nurses as captured by Generative Artificial Intelligence (GAI) analysis from two specialized nursing databases. METHODS: The protocol was registered on the Open Science Framework (OSF) Platform. A comprehensive search was conducted in CINAHL, the British Nursing Database, and the Nursing & Allied Health Database, using keywords related to art, cancer, creativity, nursing, and relationships. The extracted qualitative research studies were then analyzed using GAI to identify key themes and insights. RESULTS: The analysis revealed profound considerations regarding the role of nurses in oncology and palliative patient care. Nurses acknowledged the spiritual dimension through religious and spiritual practices, while emphasizing authentic presence and empathic communication. They actively addressed patient concerns, adapted to challenges, and engaged in continuous professional development. The insights from the GAI interpretation underscored the significance of empathy, creativity, and artistry in nurturing meaningful nurse-patient connections. CONCLUSIONS: The GAI-enabled exploration provided valuable insights into several dimensions of care, emphasizing the importance of spiritual sensitivity, empathic communication, and ongoing professional growth. As technology and human care converge, integrating artistry into the nurse-patient relationship could enhance patient experiences, improve outcomes, and enrich the oncology nursing practice.
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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 | 0.041 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".