Medical assistance in dying for people living with mental disorders: a qualitative thematic review
Bibliographic record
Abstract
BACKGROUND: Medical assistance in dying (MAiD) sparks debate in several countries, some of which allow or plan to allow MAiD where a mental disorder is the sole underlying medical condition (MAiD-MD). Since MAiD-MD is becoming permissible in a growing number of jurisdictions, there is a need to better understand the moral concerns related to this option. Gaining a better understanding of the moral concerns at stake is a first step towards identifying ways of addressing them so that MAiD-MD can be successfully introduced and implemented, where legislations allow it. METHODS: Thus, this article aims (1) to better understand the moral concerns regarding MAiD-MD, and (2) to identify potential solutions to promote stakeholders' well-being. A qualitative thematic review was undertaken, which used systematic keyword-driven search and thematic analysis of content. Seventy-four publications met the inclusion criteria. RESULTS: Various moral concerns and proposed solutions were identified and are related to how MAiD-MD is introduced in 5 contexts: (1) Societal context, (2) Healthcare system, (3) Continuum of care, (4) Discussions on the option of MAiD-MD, (5) MAiD-MD practices. We propose this classification of the identified moral concerns because it helps to better understand the various facets of discomfort experienced with MAiD-MD. In so doing, it also directs the various actions to be taken to alleviate these discomforts and promote the well-being of stakeholders. CONCLUSION: The assessment of MAiD-MD applications, which is part of the context of MAiD-MD practices, emerges as the most widespread source of concern. Addressing the moral concerns arising in the five contexts identified could help ease concerns regarding the assessment of MAiD-MD.
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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 | 0.039 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".