Addressing the Slippery Slope Argument: Trends in Euthanasia Among Patients with Psychiatric Disorders and Dementia in Belgium, 2002–2023
Bibliographic record
Abstract
Abstract Background Assisted suicide of patients with psychiatric disorders or dementia have increased in countries like Belgium, the Netherlands, and Canada and are often mentioned to argue against euthanasia regulations, suggesting they lead to a slippery slope. We examine changes in euthanasia cases for patients with these conditions in Belgium. Data and methos We use data on all cases of euthanasia reported to the Federal Commission for the Control and Evaluation of Euthanasia (FCCEE) from 2002 to 2023. Psychiatric disorders (N=427) and dementia (N=310) represent 1.27% and 0.92% of all cases, respectively. Using time-series Poisson regression, we model trends by first examining interactions between euthanasia reasons and year, then extending to three-way interactions with gender and region. We replicate the analyses with an offset to account for demographic changes. Results Euthanasia for psychiatric disorders and dementia showed distinct trends over time. Euthanasia for psychiatric disorders followed trends similar to the other types of euthanasia, while euthanasia for dementia showed a slight over-increase. Demographic change explains part of the increase in euthanasia for dementia and other reasons but not psychiatric disorders. While euthanasia rates for psychiatric disorders were initially higher for women, the trend for men is catching up over time. Regional trends indicate higher overall euthanasia rates in the Dutch-speaking population but with faster increases in the French-speaking population. Discussion The Belgian experience demonstrates that assisted dying laws can include non-terminal psychiatric conditions with appropriate safeguards and without evidence of significant misuse, addressing concerns about the slippery slope argument.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".