Bibliographic record
Abstract
Since Canada legalized medical assistance in dying (MAID) in 2016, it has become one of the most permissive regimes in the world for euthanasia and assisted suicide. The number of deaths has risen rapidly and the categories of eligibility continue to expand. The country is poised, as of March 2024, to allow MAID for those whose sole underlying condition is mental illness, generating considerable debate. Advocates of MAID for mental illness often frame it as a question of equal access, but this extension involves considerable complexities not present in other cases. This paper examines psychiatric MAID in the Canadian context, engaging directly with the most pertinent arguments of the practice’s advocates. The paper argues that independent of any prior commitments vis-à-vis the permissibility of MAID per se, there is a clear ethical and legal necessity to oppose extending MAID on the grounds of mental illness if we follow the parameters set up within the Canadian regime. The paper advances three arguments: first, that mental illnesses cannot be deemed irremediable, as required by the Canadian law; second, that we cannot establish with adequate certainty that a mentally ill patient has the decision-making capacity to choose MAID; and third, that allowing psychiatric MAID will have a devastatingimpact on care and support of the mentally ill.
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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.135 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.018 |
| 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".