Increasing Interest and Demand? Is Our System Well-Enough Prepared for Policy Change?
Bibliographic record
Abstract
The article co-authored by Maureen Taylor and Sandra Martin raises important issues that are resulting in new debate and attention in our thinking concerning physician-assisted death. It is likely that a change in policy is forthcoming, especially with the emerging force of a growing demographic who value personal choice and autonomy and are well-versed in the range of medical technologies and practices available. The issue of physician-assisted death cannot be understood apart from considering current models of healthcare and the role of adequate supportive care and psychosocial support. Despite having access to research and frameworks to inform quality palliative care, as well as communication competencies and guidelines to assist practitioners in the management of debilitating symptoms, our current healthcare system consists of healthcare professionals who continue to be challenged in their abilities to alleviate complex and challenging symptoms and distress. We will need to carefully assess our systems and plan well ahead for changes in policy to provide optimal, ethical and safe approaches to the offering of services around assisted death as an option for end-of-life care.
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 | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.022 | 0.017 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".