Who is Eligible for Voluntary Assisted Dying: Nine Medical Conditions Assessed against Five Legal Frameworks
Bibliographic record
Abstract
Eligibility criteria in voluntary assisted dying legislation determine access to assistance to die. This article undertakes the practical exercise of analysing whether each of the following nine medical conditions can provide an individual with access to voluntary assisted dying: cancer, motor neurone disease, chronic obstructive pulmonary disease, chronic kidney disease, Alzheimer’s disease, anorexia, frailty, spinal cord injury and Huntington’s disease. This analysis occurs across five legal frameworks: Victoria, Western Australia, a model Bill in Australia, Oregon and Canada. The article argues that it is critical to evaluate voluntary assisted dying legislation in relation to key medical conditions to determine the law’s boundaries and operation. A key finding is that some frameworks tended to grant the same access to voluntary assisted dying, despite having different eligibility criteria. The article concludes with broader regulatory insights for designing voluntary assisted dying frameworks both for jurisdictions considering reform and those reviewing existing legislation.
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 | 0.044 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| 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".