Interpreting and operationalizing the incurability requirement in Canada’s assisted dying legislation
Bibliographic record
Abstract
To access medical assistance in dying (MAiD) in Canada, a person must have a “grievous and irremediable medical condition” defined in part as “a serious and incurable illness, disease, or disability”. Thus, the clinical assessment of the incurability of a person’s condition is central to determining MAiD eligibility. However, the clinical interpretation and operationalization of the term have been uncertain due to the absence of a clear legal definition and evolving legislation. This has led to confusion and controversy in the public and professional discussion of MAiD eligibility. In this paper, we examine various attempts to interpret and operationalize the term “incurable”, identifying the limitations of each approach. We aim to overcome these limitations by proposing a method for operationalizing the term. We argue that our approach: (1) is consistent with the current legal framework, (2) is consistent with the interpretations of the terminology used in the Criminal Code, and (3) reflects the clinical knowledge and reasoning about the full range of medical conditions that can lead to a request for MAiD. In our analysis, we show that incurability cannot be understood only as a feature of a person’s medical condition but resides in the interplay between the nature of the pathology and the person’s treatment decision-making. Our analysis should help with the ongoing operationalization of the incurability requirement in Canada. It may also be helpful to clinicians in other jurisdictions that either invoke or are considering invoking similar terms/concepts.
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 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 | 0.028 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.022 | 0.024 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".