An Analysis of The Nurse Practitioner Role in Palliative Care
Bibliographic record
Abstract
The purpose of this study was to examine the nature and contribution of the nurse practitioner (NP) role in an oncology palliative clinic. Quantitative and qualitative data were collected from the practice of one NP. Data were obtained on the performance of the NP role functions. the characteristics of the patients seen by the NP, the interventions delivered by the NP, and the outcomes of care. Descriptive and content analyses were used to analyze the data. The results indicate that the NP in an oncology palliative care clinic engages primarily in the clinical component of the role. The emphasis is on symptom management, patient and family education and counseling, coordination of care, and maintaining continuity of care. The method followed in this case study to examine the contribution of the NP role could be used by other NPs to demonstrate the quality, effectiveness, and efficiency of their care.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".