Documenting the process of developing the Victorian voluntary assisted dying legislation
Bibliographic record
Abstract
Many countries across the world have legislated for their constituents to have control over their death. Commonalities and differences can be found in the regulations surrounding the shape and practices of voluntary assisted dying (VAD) and euthanasia, including an individual’s eligibility and access, role of health professions and the reporting. In Australia there have been perennial debates across the country to attempt legislative change in assisting a terminally ill person to control the ending of their life. In 2017, Victoria became the first state to successfully legislate for VAD. In describing the Victorian process that led to the passage of legislation for VAD, this paper examines the social change process. The particular focus of the paper is on the vital role played by a multidisciplinary ministerial advisory panel to develop recommendations for the successful legislation, and is written from their perspective. What is known about the topic? VAD has not been legal in an Australian state until legislation passed in Victoria in 2017. What does this paper add? This paper describes how the legislation was developed, as well as the significant consultative and democratic processes required to get the bill to parliament. What are the implications for practitioners? In documenting this process, policy makers and others will have an understanding of the complexities in developing legislation. This information will be useful for other Australian jurisdictions considering similar legislative changes.
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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 | 0.126 | 0.150 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".