Mental illness segregation and truncated autonomy within medical assistance in dying legislative frameworks in Colombia and Canada
Bibliographic record
Abstract
Colombia and Canada are the only jurisdictions in the Americas that have adopted countrywide frameworks on medical assistance in dying (MAiD), also known as physician-assisted death, for terminal and non-terminal illnesses. Both countries have excluded mental illness as a sole condition from eligibility to date. In this forum article, we adopt the notion of truncated autonomy to critically analyse how individuals with mental illness have been impacted by specific instances of legislative inaction and misdirected action, identified throughout the development of the current legislative frameworks on MAiD in each country. To develop our argument, we will introduce an overview of definitions and relevant debates on MAiD. We then present snapshots of Colombia’s and Canada’s healthcare systems along with some pressing public mental health concerns in each country. Finally, we will examine the development history of the legislative frameworks on MAiD adopted by each jurisdiction, highlighting instances of legislative inaction and misdirected actions that have contributed to the current paradoxical portrayal of mental illness as a condition that cannot be treated as equal to other illnesses considered non-mental. We conclude by arguing that an artificial divide between illnesses places individuals with mental illness into a situation of legal uncertainty that truncates their autonomy by undermining their equitable access to healthcare and protection of their human rights. We also reflect on the relevance of systematic participatory practices, including systematic exploration and incorporation of the views and care preferences of people living with mental illness, for the development and refinement of legislative and regulatory frameworks on MAiD in jurisdictions that might actively discuss or consider its adoption in the future.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.020 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".