Reflections on dying patients, hospices, assisted suicide, and euthanasia
Bibliographic record
Abstract
My parents’ death struggle, my clinical work with dying patients, the euthanasia of Freud, and a fear of dementia form the background to my reflections on dying patients, hospices, assisted suicide, and euthanasia. The change in public opinion has resulted in a displacement from Nazi crimes to the present focus on the right to self-determination. Consequently, a law allowing assisted suicide or euthanasia has been adopted in several locations, such as Oregon in the USA, the Benelux countries, Switzerland, and Canada. The fear of suffering, hopelessness, and inability are strong arguments to allow euthanasia and aided suicide. A compelling case against it is its negative social consequences, the infringement into the private sphere when the sick person and their family must decide if they are willing to accept assisted suicide or euthanasia. Although the “right to death” provides freedom to some, for others it is a forced choice that interferes with the dying process. I conclude by highlighting the palliative model, wherein death is perceived as a part of an individual’s life and as a normal process, although this task is hard for the family to contain, especially when the dying person is in pain and agony. Dying is not merely an individual process. It affects the whole family as well as the future generations’ views on reciprocity and responsibility.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.041 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.018 | 0.042 |
| Insufficient payload (model declined to judge) | 0.003 | 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".