The Legal Status of Deep and Continuous Palliative Sedation Without Artificial Nutrition and Hydration
Bibliographic record
Abstract
Deep and continuous palliative sedation combined with the withholding or withdrawal of artificial nutrition and hydration (collectively termed “PSs̄ANH”) is an important aspect of high-quality end-of-life care. It is one means of alleviating suffering. Unfortunately, the legality of this practice has been under-researched and PSs̄ANH is not yet appropriately regulated in Canada. In this paper, we explore the legal status of PSs̄ANH where it (1) will not hasten death (Type 1 PSs̄ANH); (2) might, but is not certain to, hasten death (Type 2 PSs̄ANH); or (3) is certain to hasten death (Type 3 PSs̄ANH). It is clear that Type 1 is lawful. While it could be argued that Types 2 and 3 are also lawful, their legal status is ultimately unclear. We argue that the current lack of clarity and robust regulation with respect to Types 2 and 3 is a profound disservice to suffering individuals and health care providers and should be remedied by the federal government through amendments to the Criminal Code. We then propose amendments that would bring clarity, coherence, and comprehensiveness to end-of-life law, policy, and practice and thus enable better care for the dying.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.013 | 0.010 |
| 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".