Response to Medical Assistance in Dying, Palliative Care, Safety, and Structural Vulnerability
Bibliographic record
Abstract
This report, signed by >170 scholars, clinicians, and researchers in palliative care and related fields, refutes the claims made by the previously published Medical Assistance in Dying, Palliative Care, Safety, and Structural Vulnerability . That report attempted to argue that structural vulnerability was not a concern in the provision of assisted dying (AD) by a selective review of evidence in medical literature and population studies. It claimed that palliative care has its own safety concerns, and that “misuse” of palliative care led to reports of wrongful death. We and our signatories do not feel that the conclusions reached are supported by the evidence provided in the contested report. The latter concluded that the logical policy response would be to address the root causes of structural vulnerability rather than restrict access to AD. Our report, endorsed by an international community of palliative care professionals, believes that public policy should aim to reduce structural vulnerability and, at the same time, respond to evidence-based cautions about AD given the potential harm.
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 | 0.064 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.032 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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".