Charter Scrutiny of Canada's Medical Assistance in Dying Law and the Shifting Landscape of Belgian and Dutch Euthanasia Practice
Bibliographic record
Abstract
In 2017, Canada's Parliament partially decriminalized Medical Assistance in Dying [MAID] (the term the law uses for euthanasia and physician assisted suicide) and allowed the practice in specific circumstances at the end of life. Canadian advocates of broad access to MAID have criticized the new legislation for being too restrictive, and particularly rejected the law's end-of-life access criteria of ‘reasonably foreseeable death’ and ‘irreversible decline of capabilities’. Two legal challenges have already been launched that aim at broadening the category of people who can access MAID. In these lawsuits, an analysis of what happens in jurisdictions that allow broad access to MAID, and a comparison with other jurisdictions that restrict access to end-of-life situations, could play an important role as it reveals why a restriction to the end-of-life context is important. This paper provides such a comparison, and discusses in detail the most recent developments of the euthanasia practices in Belgium and the Netherlands, two jurisdictions with broad access to euthanasia without end-of-life restriction. This discussion, informed by many sources that are not available in English and that are quoted in translation, reveals why giving physicians discretionary power to decide who has access to MAID, on the basis of broad access criteria without restriction to the end-of-life context, risks undermining important legislative goals that are enumerated in Canada's MAID law. These include: the protection against errors and abuse; the protection of vulnerable people from being induced, in moments of weakness, to end their lives; the equal value of every person’s life and the avoidance of negative perceptions of the quality of life of persons who are elderly, ill, or disabled; and the prevention of suicide. The paper shows the most recent trends in Belgium and the Netherlands and discusses in particular growing controversies related to the expansion of euthanasia practices outside the end-of-life context and in the context of mental health. It discusses further the problems with the various safeguards in the Belgian and Dutch regimes, which are also increasingly becoming apparent. This analysis shows how a restriction of access to the end-of-life context is essential to ensure a reasonable balance between providing access and protecting the important human rights related goals embedded in the legislation.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".