Assisted or Hastened Death: The Healthcare Practitioner’s Dilemma
Bibliographic record
Abstract
Assisting or hastening death is a dilemma with many ethical as well as practical issues facing healthcare practitioners in most countries worldwide now. Various arguments for and against assisted dying have been made over time but the call from the public for the legalisation of euthanasia and assisted suicide has never been stronger. While some studies have documented the reluctance of medical and other healthcare professionals to be involved in the practice of assisted dying or euthanasia, there is still much open debate in the public domain. Those who have the most experience of palliative care are strongest in their opposition to hastening death. This paper explores salient practical and ethical considerations for healthcare practitioners associated with assisting death, including a focus on examining the concepts of autonomy for patients and healthcare practitioners. The role of the healthcare practitioner has clearly and undoubtedly changed over time with advances in healthcare practices but the duty of care has not changed. The dilemmas for healthcare practitioners thus who have competent patients requesting hastened death extends far beyond acting within a country’s laws as they go to the very heart of the relationship between the practitioner and patient.
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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 | 0.033 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.035 | 0.023 |
| 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".