“Nothing Hurts Less Than Being Dead”: Psychological Pain in Case Descriptions of Psychiatric Euthanasia and Assisted Suicide from the Netherlands: « Rien ne fait moins mal qu’être mort »: La douleur psychologique dans les descriptions de cas d’euthanasie et de suicide assisté psychiatrique aux Pays-Bas
Bibliographic record
Abstract
Objectives: Euthanasia and assisted suicide (EAS) of individuals with mental disorders is a growing practice in several countries, including the Netherlands. Here, we aimed to identify the most frequent dimensions of and associated factors to psychological pain, which has been associated with suicidality, in individuals undergoing psychiatric EAS. Methods: An exploratory retrospective content analysis of the English translation of 66 digital case records of individuals who died by EAS in the Netherlands between 2011 and 2014 was performed. Nine standard psychological pain dimensions (irreversibility, loss of control, emptiness, emotional flooding, freezing, social distancing, narcissistic wounds, confusion, and self-estrangement), illness, and sociodemographic variables were evaluated by 2 independent raters using a premade data abstraction form (Kohen κ > 0.8 in all cases). Results: The mean number of dimensions was 4.64 ± 1.20 (median = 5), out of 9. The most frequent dimensions were irreversibility, loss of control, emptiness, and emotional flooding, in decreasing order. Past treatment refusal and the mention of social connections in case descriptions were related to the higher number of psychological pain dimensions (4.89 ± 1.24 vs. 4.31 ± 1.07, P = 0.03 and 5.05 ± 1.17 vs. 4.43 ± 1.17, P = 0.03, respectively). Emotional flooding was the only dimension specifically associated with specific psychiatric conditions, namely posttraumatic phenomena and personality disorders. Conclusions: Numerous psychological pain dimensions were detected in case descriptions of individuals who underwent EAS before the procedure. Subjective nature of the study precludes definite conclusions but suggest that future studies should explore psychological pain and the role of interventions targeting it in patients requesting EAS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".