The Seismic Shift in End-of-Life Care: Palliative Care Challenges in the Era of Medical Assistance in Dying
Bibliographic record
Abstract
Background: Concerns regarding personal, professional, administrative, and institutional implications of medical assistance in dying (MAiD) are of particular interest to palliative and hospice care providers (PHCPs), who may encounter additional moral distress and professional challenges in providing end-of-life (EOL) care in the new legislative and cultural era. Objective: To explore PHCPs' encountered challenges and resource recommendations for caring for patients considering MAiD. Design: Qualitative thematic analysis of audio-recorded semistructured interviews with PHCPs. Setting/Subjects: Multidisciplinary PHCPs in acute, community, residential, and hospice care in Vancouver, Canada, with experience supporting patients who have made MAiD inquiries or requests. Measurements: Interviews were deidentified, transcribed verbatim, and coded by four researchers using a common coding scheme. Key themes were analyzed. Results: Twenty-six PHCP participants included physicians ( n = 7), nurses ( n = 12), social workers ( n = 5), and spiritual health practitioners ( n = 2). Average interview length was 52 minutes (range 35–90). Analysis revealed four broad challenges associated with providing EOL care after MAiD legalization: (1) moral ambiguity and provider distress, (2) family distress, (3) interprofessional team conflict, and (4) impact on palliative care. Participants also recommended three types of resources to support clinicians in delivering quality EOL care to patients contemplating MAiD: (1) education and training, (2) pre- and debriefing for team members, and (3) tailored bereavement support. Conclusions: PHCPs encountered multilevel MAiD-related challenges, but noted improvement in organizational policies and coordination. Resources to enhance training, pre- and debriefing, and tailored bereavement may further support PHCPs in providing high-quality EOL care as they navigate the legislative and cultural shifts.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".