Making a case for the inclusion of refractory and severe mental illness as a sole criterion for Canadians requesting medical assistance in dying (MAiD): a review
Bibliographic record
Abstract
BACKGROUND: Following several landmark rulings and increasing public support for physician-assisted death, in 2016, Canada became one of a handful of countries legalising medical assistance in dying (MAiD) with Bill C-14. However, the revised Bill C-7 proposes the specific exclusion of MAiD where a mental disorder is the sole underlying medical condition (MAiD MD-SUMC). AIM: This review explores how some persons with serious and persistent mental illness (SPMI) could meet sensible and just criteria for MAiD under the Canadian legislative framework. METHODS: We review the proposed Bill C-7 criteria (capacity, voluntariness, irremediability and suffering) as well as the nuances involved in separating a well-reasoned request for assisted suicide from what might be solely a manifestation of a SPMI. FINDINGS: In this paper, we argue against the absolute exclusion of patients with SPMIs from accessing MAiD. Instead, we propose that in some circumstances, MAiD MD-SUMC may be justifiable while remaining the last resort. Conducting MAiD eligibility assessments removes the need to introduce diagnosis-specific language into MAiD legislation. Competent psychiatric patients who request MAiD should not be treated any differently from other eligible candidates. Many individuals with psychiatric disorders will be incapable of consenting to MAiD. The only ethical option is to assess eligibility for MAiD on an individual basis and include as legitimate candidates those who suffer solely from psychiatric illness who have the decisional capacity to consent to MAiD.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".