Suicide-risk implications in legalising medical assistance in dying for mental disorders
Bibliographic record
Abstract
The implementation of Medical Assistance in Dying where mental disorder is the sole underlying medical condition (MAID MD-SUMC) in Canada has sparked significant concerns, particularly following the passage of Bill C-7 in 2021. This paper delves into complex concerns surrounding MAID MD-SUMC by reviewing the 2023 summary report of the special Joint Committee established to assess Canada’s preparedness for this profound policy change. The report highlights key challenges, especially in the areas of assessing medical irremediability, distinguishing MAID MD-SUMC from suicidality, and safeguarding vulnerable populations. A central concern identified is the difficulty in differentiating acute suicidality from underlying mental disorders, a task complicated by the need to accurately assess decisional capacity in individuals experiencing suicidal ideation. The report also underscores the risks associated with stigmatising vulnerable groups and the potential for indirect harm through suggestion, accommodation, and contagion effects. These findings emphasise the importance of addressing the recommendations outlined in this paper during the interim period before full implementation in 2027, with a focus on continuous reassessment and the development of robust safeguards. The ethical advancement of MAID policies hinges on prioritising the rights and safety of all individuals, ensuring that safeguards align with the complex and multifaceted nature of suicide risk.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".