Providing care and sharing expertise: Reflections of nurse-specialists in palliative home care
Bibliographic record
Abstract
OBJECTIVE: This study explored the experiences, perspectives, and reflections of five nurse-specialists in palliative home care, whose dual role includes caring for patients in their daily practice as well as sharing their knowledge, skills, expertise, and experiences with other home care nurses in the community. METHODS: A qualitative research design, incorporating face-to-face semistructured interviews, was used. Interviews were based on open-ended questions such as: "What is your experience in providing palliative home care to patients and their families? How do you feel about sharing your expertise and experiences with home care nurses?" Data were content analyzed using the constant comparative method. RESULTS: Three major themes and a number of subthemes emerged: (1) acknowledging one's own limitations and humanness: (a) calling for backup, (b) learning as we go along, (c) coping with emotional demands, and (d) interacting with family members; (2) building a collaborative partnership: (a) working collaboratively, (b) sharing information, (c) guiding home care nurses, and (d) being nonjudgmental; and (3) teamwork and implementing palliative home care teams. SIGNIFICANCE OF RESULTS: Nurse-specialists play a key role in palliative home care as both carers and as resources of expert knowledge for other home care nurses caring for palliative patients. As the population ages, the health care system will be faced with increasing requests for high-quality palliative home care. The results of this study demonstrate that, from the perspective of the nurse-specialists of NOVA-Montréal (a nonprofit social and health service organization), nurse-specialists can work collaboratively with home care nurses to improve patients' quality of care and their quality of life. Moreover, patients and their families would benefit from the more widespread establishment of palliative care teams within community health organizations.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".