The nurse practitioner role is ideally suited for palliative care practice: A qualitative descriptive study
Bibliographic record
Abstract
Palliative care (PC) is an approach to caring for individuals with life-threatening health conditions, with the focus being best quality of life (Hawley, 2017). It involves effective symptom management to address the physiological manifestations of diseases; interventions to promote the social, spiritual, and emotional well-being of patients and their families; and end-of-life (EOL) care to support patients’ greatest comfort and dignity when death is imminent (World Health Organization, 2011; Worldwide Palliative Care Alliance [WPCA], 2014). Palliative care is recommended not only for cancer, but for any chronic life-threatening condition; for example, heart and kidney failure and various neurological diseases (Hawley, 2017). With the high prevalence and expected growth of chronic diseases not only in Canada, but worldwide, the need for PC is great now and will become even more so in the future (Canadian Hospice Palliative Care Association [CHPCA], 2014; Public Health Agency of Canada, 2016; WPCA, 2014).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| 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".