An Alternative to Medical Assistance in Dying? The Legal Status of Voluntary Stopping Eating and Drinking (VSED)
Bibliographic record
Abstract
Medical assistance in dying (MAiD) has received considerable attention from many in the field of bioethics. Philosophers, theologians, lawyers, and clinicians of all sorts have engaged with many challenging aspects of this issue. Public debate, public policy, and the law have been enhanced by the varied disciplinary analyses. With the legalization of MAiD in Canada, some attention is now being turned to issues that have historically been overshadowed by the debate about whether to permit MAiD. One such issue is voluntary stopping eating and drinking (VSED) as an alternative to MAiD. In this paper, I will apply a legal lens to the issue. An understanding of whether VSED is legal provides a foundation for ethical reflection on whether it ought to be permitted. Is it permitted for those who prefer VSED to MAiD? Is it permitted for those who do not qualify for MAiD under our current legislation – for those who do not have a grievous and irremediable medical condition, for mature minors, for individuals whose sole underlying medical condition is a mental disorder and who do not otherwise meet the eligibility criteria, and for individuals who have lost capacity but had completed an advance directive?
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".