Medically Assisted Dying Practices: What Role for Clinical Ethicists?
Bibliographic record
Abstract
Medically assisted dying (AD) practices have been legalized in several jurisdictions throughout the world over the last two decades. Because of this increased trend, more individuals now have access to a self-chosen death. Despite its legalization and the diversity of frameworks governing AD, it remains fraught with ethical challenges. However, there is a dearth of literature regarding the specific roles clinical ethicists (CEs) may have in AD provision. We sought to address this literature gap by: (1) Gathering healthcare professionals' (HPs) and CEs perspectives on how CEs may contribute; (2) Identifying how CEs may have been involved thus far; (3) Identifying promising practices and pitfalls related to their involvement. An exploratory qualitative study using focus groups, purposive and snowball sampling. Four online focus groups were held. Groups comprised of (1) HPs and (2) CEs from Quebec and Switzerland. Data was analyzed using thematic analysis. Altogether 21 persons participated, among them 10 ethicists and 11 HPs. Four major themes were identified: (1) Specific Roles for CEs; (2) CEs competencies deemed useful in AD provision; (3) Operationalization of CEs' involvement 5) Obstacles/Pitfalls associated to CEs' involvement in AD. Several roles for CEs have been identified that have been associated with specific ethical challenges that arise in AD. Findings indicate that CEs' integration in AD should be context dependent and should consider several misconceptions associated with the field of clinical ethics in general.
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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 | 0.032 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 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".