An Ethics Framework for Medical Assistance in Dying: Supporting Ethical Decision‐Making in the Practice of MAiD
Bibliographic record
Abstract
This paper presents an Ethics Framework for MAiD (Medical Assistance in Dying) to support the integration of evidence-informed, values-based, inclusive and transparent ethical decision-making into everyday MAiD practice. As with other areas of clinical practice, ethical decision-making is an intrinsic part of MAiD. While clinicians connected to academic medical centers or large hospitals may have access to the expertise of an ethicist, those working independently, or in community-based, rural or remote settings may wrestle with ethical issues alone. Without a process to guide ethical reflection and analysis, clinicians navigating complex MAiD cases risk moral distress and uncertainty, and may inadvertently make decisions that are biased, narrow or ill-informed. The proposed Ethics Framework for MAiD includes a description of core values and principles relevant to the delivery of MAiD and a process guide to support the application of values and principles to cases. Use of the framework is illustrated through a simplified complex MAiD case. This Ethics Framework for MAiD is applicable to both clinical patient cases and organizational ethics issues, and adaptable to any jurisdiction and any legal or practice context. It may also be used by ethicists when conducting formal ethics case consultations involving MAiD. The goal of the paper is to empower MAiD assessors, providers, health professionals, program managers and ethicists to address ethical issues arising in everyday practice through the introduction of a pragmatic ethics framework specifically tailored to assisted dying.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.090 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.057 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".