How does Medical Assistance in Dying affect end-of-life care planning discussions? Experiences of Canadian multidisciplinary palliative care providers
Bibliographic record
Abstract
BACKGROUND: More than a dozen countries have now legalized some form of assisted dying, and additional jurisdictions are considering similar legislations or expanding eligibility criteria. Despite the persistent controversies about the relationship between medicine, palliative care, and assisted dying, many people are interested in assisted dying. Understanding how end-of-life care discussions between patients and specialist palliative care providers may be affected by such legislation can inform end-of-life care delivery in the evolving socio-cultural and legal environment. AIM: To explore how the Canadian Medical Assistance in Dying legislation affects end-of-life care discussions between patients and multidisciplinary specialist palliative care providers. DESIGN: Qualitative thematic analysis of semi-structured interviews. PARTICIPANTS: = 4). RESULTS: Qualitative thematic analysis identified five notable considerations associated with Medical Assistance in Dying affecting end-of-life care discussions: (1) concerns over having proactive conversations about the desire to hasten death, (2) uncertainties regarding wish-to-die statements, (3) conversation complexities around procedural matters, (4) shifting discussions about suffering and quality of life, and (5) the need and challenges of promoting open-ended discussions. CONCLUSION: Medical Assistance in Dying challenges end-of-life care discussions and requires education and support for all concerned to enable compassionate health professional communication. It remains essential to address psychosocial and existential suffering in medicine, but also to provide timely palliative care to ensure suffering is addressed before it is deemed irremediable. Hence, clarification is required regarding assisted dying as an intervention of last resort. Furthermore, professional and institutional guidance needs to better support palliative care providers in maintaining their holistic standard of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".