Externalist argument against medical assistance in dying for psychiatric illness
Bibliographic record
Abstract
Medical assistance in dying, which includes voluntary euthanasia and assisted suicide, is legally permissible in a number of jurisdictions, including the Netherlands, Belgium, Switzerland and Canada. Although medical assistance in dying is most commonly provided for suffering associated with terminal somatic illness, some jurisdictions have also offered it for severe and irremediable psychiatric illness. Meanwhile, recent work in the philosophy of psychiatry has led to a renewed understanding of psychiatric illness that emphasises the role of the relation between the person and the external environment in the constitution of mental disorder. In this paper, I argue that this externalist approach to mental disorder highlights an ethical challenge to the practice of medical assistance in dying for psychiatric illness. At the level of the clinical assessment, externalism draws attention to potential social and environmental interventions that might have otherwise been overlooked by the standard approach to mental disorder, which may confound the judgement that there is no further reasonable alternative that could alleviate the person's suffering. At the level of the wider society, externalism underscores how social prejudices and structural barriers that contribute to psychiatric illness constrain the affordances available to people and result in them seeking medical assistance in dying when they otherwise might not have had under better social conditions.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.053 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.015 | 0.017 |
| 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".