Achieving Excellence in Palliative Care: Perspectives of Health Care Professionals
Bibliographic record
Abstract
Caring for individuals at the end of life in the hospital environment is a challenging proposition. Understanding the challenges to provide quality end of life care is an important first step in order to develop appropriate approaches to support and educate staff members and facilitate their capacity remaining "caring." Four studies were undertaken at our facility to increase our understanding about the challenges health professionals experience in caring for patients at end of life and how staff members could be supported in providing care to patients and families: (1) In-depth interviews were used with cancer nurses (n = 30) to explore the challenges talking about death and dying with patients and families; (2) Surveys were used with nurses (n = 27) and radiation therapists (n = 30) to measure quality of work life; (3) and interprofessional focus groups were used to explore what it means "to care" (five groups held); and (4) interprofessional focus groups were held to understand what "support strategies for staff" ought to look like (six groups held). In all cases, staff members confirmed that interactions concerning death and dying are challenging. Lack of preparation (knowledge and skill in palliative care) and lack of support from managers and colleagues are significant barriers. Key strategies staff members thought would be helpful included: (1) Ensuring all team members were communicating and following the same plan of care, (2) providing skill-based education on palliative care, and (3) facilitating "debriefing" opportunities (either one-on-one or in a group). For staff to be able to continue caring for patients at the end of life with compassion and sensitivity, they need to be adequately prepared and supported appropriately.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".