Words matter: ‘enduring intolerable suffering’ and the provider-side peril of Medical Assistance in Dying in Canada
Bibliographic record
Abstract
Enduring intolerable suffering, an essential eligibility criterion in Medical Assistance in Dying (MAiD) in Canada and elsewhere, is a contradiction in terms, in that suffering must be tolerable to be endured. Cases of people who were approved for MAiD but who elected to die naturally, thus tolerating their suffering, bear out the unreliability of this central safeguard. The clinical assessment of intolerable suffering may be strengthened by adopting a definition of intolerable suffering centred on clinically evidenced physical and psychological decompensation. This argument also raises important questions about the risks of MAiD clinicians subjectively defining, approving and providing MAiD in ways that deviate from accepted legal and clinical concepts and ethics. Examples show some prolific clinicians describe MAiD in terminology that differs from such norms, as a personal mission, as personally pleasurable, and as a rights-based service. These alternative views are explored for their risks in assessing and providing MAiD for intolerable suffering. This further demonstrates the need for conceptual clarity in legislation, improved vetting and monitoring of clinicians, and a different assessment process to protect patients and clinicians.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".