Exploring the interface between ‘physician‐assisted death’ and palliative care: cross‐sectional data from Australasian palliative care specialists
Bibliographic record
Abstract
BACKGROUND: Legalisation of physician-assisted dying (PAD) remains a highly contested issue. In the Australasian context, the opinion and perspective of palliative care specialists have not been captured empirically, and are required to inform better the debate around this issue, moving forward. AIM: To identify current attitudes and experiences of palliative care specialists in Australasia regarding requests for physician-assisted suicide and voluntary euthanasia, and to capture the opinion of palliative care specialists on the legalisation of these practices in the Australasian context. METHOD: An anonymous, cross-sectional, online survey of Australasian specialists in palliative care, addressing the following six areas: (i) demographics; (ii) frequency of requests, and response given; (iii) understanding of the term 'voluntary euthanasia'; (iv) opinion regarding legalisation of physician-assisted suicide and voluntary euthanasia in Australasia, and willingness to participate if legal; (v) identification of the most important values guiding this opinion; and (vi) anticipated impact that legalisation of assisted death would have on palliative care practice. RESULTS: Important findings include: (i) palliative care specialists are largely opposed to the legalisation of PAD; (ii) the proportional titration of opioids is not understood by any palliative care specialist studied to be 'voluntary euthanasia'; and (iii) there is a wide variation in frequency of requests, and one-third of palliative care specialists express discomfort in dealing with requests for assisted suicide or euthanasia. CONCLUSION: Key areas for future research at the interface between PAD and best practice end-of-life care are identified, including exploration into why palliative care specialists are largely opposed to PAD, and consideration of the impact 'the opioid misconception' may have on the literature informing this debate.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".