Nurses and Voluntary Assisted Dying: How the Australian Capital Territory’s Law Could Change the Australian Regulatory Landscape
Bibliographic record
Abstract
On June 5, 2024, the Australian Capital Territory passed a law to permit voluntary assisted dying ("VAD"). The Australian Capital Territory became the first Australian jurisdiction to permit nurse practitioners to assess eligibility for VAD. Given evidence of access barriers to VAD in Australia, including difficulty finding a doctor willing to assist, the Australian Capital Territory's approach should prompt consideration of whether the role of nurses in VAD should be expanded in other Australian jurisdictions. Drawing on lessons from Canada, which currently permits nurse practitioners to assess patient eligibility, we argue that the time has come for Australian jurisdictions to expand the role of nurses in VAD systems. This would be an important step in ensuring access to VAD for patients in practice. Attention, however, must also be paid to ensuring adequate remuneration of nurses (and doctors) if this goal of promoting access is to be achieved in practice.
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 | 0.051 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.025 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".