Refusing Care as a Legal Pathway to Medical Assistance in Dying
Bibliographic record
Abstract
Can a competent individual refuse care in order to make their natural death reasonably foreseeable in order to qualify for medical assistance in dying (MAiD)? Consider a competent patient with left-side paralysis following a right-brain stroke who is not expected to die for many years; normally his cause of death would not be predictable. However, he refuses regular turning, so his physician can predict that pressure ulcers will develop, leading to infection for which he will refuse treatment and consequently die. Is he now eligible for MAiD? Consider a competent patient with spinal stenosis (a non-fatal condition) who refuses food (but not liquids in order not to lose capacity from dehydration). Consequently, her physician can predict death from starvation. Is she now eligible for MAiD? Answering these questions requires that we answer three sub-questions: 1) do competent patients have the right to refuse care?; 2) do healthcare providers have a duty to respect such refusals?; and 3) are deaths resulting from refusals of care natural for the purposes of determining whether a patient is eligible for MAiD? If a competent patient has the right to refuse some particular care, and healthcare providers have a duty to respect that refusal, and if the death that would result from the refusal of that care is natural, then that refusal of care is a legal pathway to MAiD. However, if the competent patient does not have the right to refuse some particular care, or if healthcare providers do not have a duty to respect that refusal, or if the death that would result from the refusal of that care is not natural, then that refusal of care is not a legal pathway to MAiD. In this paper, we explore this complex legal terrain with the most profound of ethical implications – access to MAiD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".