Going on a journey: understanding palliative care nursing
Bibliographic record
Abstract
AIM: To describe the qualitatively different ways a group of Australian nurses understood their experience of being a palliative care nurse. DESIGN: The research approach chosen was phenomenography. Fifteen nurses caring for people in a specialist palliative care unit in regional Australia were interviewed and transcribed interview data were analysed in order to identify understanding of experience. FINDINGS: The research identified and described five ways of understanding the experience of being a palliative care nurse: doing everything you can; developing closeness; working as a team; creating meaning about life; and maintaining myself. CONCLUSION: The group of palliative care nurses involved in this research understood their experience as journeying with their patients through the final phases of the person's life. The journey involved the patient, his/her family and members of the healthcare team. The journey was described further as a process of personal development which influenced how nurses construct meaning about life and maintain a sense of self. The experiences described reveal a great deal about palliative care nursing and provide useful knowledge and insights to assist practitioners, managers and educators.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".