Palliative Care Advanced Practice Nurses' Experiences with Medical Assistance in Dying
Bibliographic record
Abstract
Medical assistance in dying (MAID) is new to Canada and there have been over 1500 cases in Alberta since June 2016. The philosophy, intent, and approach to care of palliative care and MAID are different; MAID is not part of palliative care. Palliative care philosophy involves neither hastening nor postponing death. The differences between palliative care and MAID lead to inherent tensions for health care providers. Thus, palliative care advanced practice nurses (APNs) experience challenges while following patients and their families through the MAID process. Very little is known about palliative care APNs’ experiences with MAID and how they navigate MAID while working within the philosophy of palliative and hospice care. The purpose of this research was to understand experiences of palliative care APNs when caring for patients requesting or receiving MAID. Using hermeneutics as a methodology, I conducted interviews with guiding prompts to allow for the palliative care APNs’ story to unfold. I recruited palliative care APNs working for Alberta Health Services within the Calgary zone in urban and rural settings. By conducting research on this topic, challenges, and issues that palliative care APNs might be facing were explored. Through the data analysis, moral distress was apparent; the palliative care APNs experienced moral distress. Nurses who face moral distress can be negatively impacted spiritually, emotionally, and physically. Nurses also faced organizational challenges in navigating APN practice and the MAID team processes. There are opportunities for future research to be conducted so that educational and supportive tools can be developed in the future for nurses caring for patients receiving MAID; these include revising regulatory documents pertaining to MAID to incorporate the role of the palliative care APN and addressing the stigma associated with MAID.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".