When a Theoretical Commitment to Broad Physician-Aid-In-Dying Faces the Reality of Its Implementation
Bibliographic record
Abstract
Brent Kious and Margaret Battin argue that since relief of intolerable suffering constitutes the dominant justification for physician-assisted-dying (PAD) laws, we should give access to patients who suffer intolerably from mental illness. As others, they reject ‘terminal illness’ as a legitimate access restriction. They recognize, though, that a key challenge is to determine “what degree of suffering is enough to justify death”. Yet they fail to see how the experience in the very few jurisdictions that rely on unbearable suffering as a key threshold for access reveals the dangers of not treating PAD as a truly exceptional procedure that should not move beyond the context of end-of-life care. They fail to see this because (1) they gloss over and fail to give due weight to a longstanding human rights tradition respecting the intrinsic value of human life; and (2) do not appreciate how fraught the application of the concept of ‘unbearable suffering’ really is as a matter of policy and practice.In this paper, I discuss these two issues, briefly expanding on what we can learn from the very few jurisdictions that have implemented broad access to PAD. Rather than promoting broader access to PAD on the basis of a theoretical commitment, we should take the compounding impact of organizing a PAD regime around the concept of unbearable suffering much more seriously.
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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 | 0.048 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.080 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.022 | 0.051 |
| Insufficient payload (model declined to judge) | 0.004 | 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".