A good death: non-negotiable personal conditions for clinicians, healthcare administrators and support staff
Bibliographic record
Abstract
OBJECTIVES: To ask all clinical, administrative and support staff affiliated with a large network of healthcare facilities to identify the conditions that they consider as non-negotiable for their own deaths to be regarded as good. METHODS: All 3495 staff of a healthcare network were asked to rank 10 conditions according to how non-negotiable they would be for themselves during their final 3 months or few hours for their own deaths to be considered as good. They were also asked about whether they had thought about their own death in the last 3 months, if they had a will, believed in God, and in the possibility of a good death, and the intensity of their fear of death. RESULTS: 2971 (85%) completed the survey. Most were female (79%) and clinical staff (65%). 93% believed in God, 60% had thought about their death recently, 33% had an intense fear of death, and 4% had a will. 64% considered a good death possible. Participants ranked dying at a preferred place, emotional support from family and friends and relief from physical symptoms as their top priorities. The lowest ranked conditions were (from the bottom) relief from psychological distress, performance of rituals and the right to terminate life. There were no statistically significant differences across genders or individual occupational groups. CONCLUSION: Most of conditions for a good death of interest to healthcare professionals could be provided without sophisticated medical infrastructure or specialised knowledge, opening the door for new support services to make it possible for everyone, anywhere.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".