Exploring assisted dying policies for mature minors: A cross jurisdiction comparison of the Netherlands, Belgium & Canada
Bibliographic record
Abstract
• Different policy approaches are associated with assisted dying (including/excluding mature minors). • The Dutch and Belgian approach was parliamentary; Canada was primarily judiciary. • Patient suffering emphasized for Dutch/Belgian context, contrasting human rights in Canada. • Assisted dying eligibility is consistently competence-focused—Canada only applies to adults. • Canada lacks strong social consensus on MAID for mature minors. Medical Assistance in Dying (MAID) was decriminalized in Canada in 2016 for individuals 18 years or older who met eligibility criteria. Currently, individuals younger than 18 years are legally permitted to access an assisted death in the Netherlands and Belgium, but not in Canada. To-date, no work has compared factors shaping the policy processes and outcomes in these three countries. Therefore, our objective was to explore the legalities of assisted dying for minors in the Netherlands and Belgium, along with how each jurisdiction arrived at their respective policies and why the trajectory differed in Canada. After screening and compiling peer-reviewed and grey literature, we used Yanow's interpretive method for comparative work to review included materials. We framed findings using Hajer's discourse coalition theory. The Dutch and Belgian contexts relied upon a parliamentary approach in legalizing assisted dying for mature minors that emphasized suffering, whereas Canada's approach was initiated by a Supreme Court of Canada decision and emphasized human rights. While the Netherlands and Belgium viewed mature minors as capable to make decisions about assisted dying, the Canadian position on mature minors’ decisional capacity with respect to assisted dying remains unsettled. This work contributes to understanding how context and sociopolitical values shape assisted dying legislations and treatment of mature minors, while highlighting areas requiring further study amid ongoing debate in Canada.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".