Examining assisted suicide and euthanasia through the lens of healthcare quality
Bibliographic record
Abstract
Many people on both sides of the debate to legalise physician-hastened death are motivated by compassion and a desire to provide better end of life care for others. Assisted dying may include euthanasia and/or assisted suicide (EAS). It is legal in some jurisdictions and under debate in others including Ireland. EAS is a complex, sensitive and can be an emotive issue; detailed and nuanced examination of the subject is needed. To enhance this discussion, we examine EAS through the lens of quality. In examining EAS from this stance, we consider the action, along with the outcomes, the impact of the outcomes from other jurisdictions with legalised EAS, alongside the risks and the balancing measures used, in addition to considering the intervention itself. Progressive expansion of eligibility for EAS has occurred over time in the Netherlands, Belgium and Canada. Given the complexity of assessing coercion, the risks to persons in vulnerable groups (including older persons, persons with mental health conditions and persons with disabilities), the progressive expansion of eligibility for EAS, the lack of safety and the undermining of suicide prevention strategies, the current law is most protective of persons in vulnerable groups in the interest of social justice. Person-centred and compassionate care needs be prioritised with greater access and equitable access to primary and specialist palliative care and mental health care for persons with incurable and terminal illnesses and support for caregivers allowing patients to die naturally with optimised symptom control.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".