The good death between consensus and paradoxes: perspectives of various stakeholders including persons nearing death
Bibliographic record
Abstract
The idea of what constitutes a good death has changed throughout history. Modern death is defined by professional, medical or technological interventions. No study has investigated the idea of a ‘good death’ from the perspective of those facing it, such as those towards the end of their life and those caring for them, since a law permitting euthanasia in Quebec, Canada, was passed. The purpose of this study was to investigate how the idea of a ‘good death’ is perceived by those who are nearing death, their supportive decision-makers, geriatricians, and other healthcare allies. In three geriatric medical care units, we conducted a study that included 16 focused interviews. The results demonstrate that support, comfort, a proper environment and lack of discomfort and distress were prevalent emerging themes. The findings revealed paradoxes surrounding two themes – being conscious during and a speed of dying. Participants’ opinions on what constitutes a ‘good death’ varied depending on whether they were talking about their own deaths or the deaths of others. We draw the conclusion that euthanasia enables the fulfilment of many of the characteristics that can characterise a ‘good death’ in our postmodern culture and urge the establishment of high-quality palliative and end-of-life care provided at home.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.027 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".