Living fully, choosing wisely: Exploring patient-centred approaches to palliative care and MAiD – Part II, the role of the oncology nurse
Bibliographic record
Abstract
This paper is Part II of a case study that follows Nancy, an oncology patient, through her diagnosis of advanced cancer, involvement of early palliative care (EPC), and consideration of Medical Assistance in Dying (MAiD), and her oncology nurse, Laura. Highlighted is the importance of open communication, emotional support, and involvement of the patient’s family in the process, to the level that is acceptable to the individual seeking or considering MAiD. The paper also addresses the ethical considerations for nurses when personal beliefs conflict with professional responsibilities. Practical insights and suggestions for oncology nurses are offered for promoting a compassionate and respectful approach to end-of-life care.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| 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".