S3-2 Missing goldilocks and killing kant: the price of canada’s headlong assisted death expansion
Bibliographic record
Abstract
At the conclusion of this session, participants will be able to: (i) Be aware of the scope of physician assisted death (PAD) frameworks, ranging from PAD for terminal illness to PAD outside near end-of-life conditions (ii) Appreciate challenges assessing irremediability, intolerable suffering, capacity and decision making when considering potential requests for assisted dying in the context of mental illnesses (iii) Understand proposed safeguards, and their limitations (iv) Appreciate the tension between ‘overinclusion’ and ‘underinclusion’ in the context of potential assisted dying requests Physician Assisted Death (PAD) has been legalized or decriminalized in well over a dozen jurisdictions around the world, and assisted dying policies continue to evolve rapidly. Many jurisdictions are exploring whether to introduce assisted dying laws, or expand existing laws. There is wide variation in how policies address potential applications for assisted dying for mental illness. PAD for sole criterion mental illness is available in the Netherlands, Belgium, Luxembourg and Switzerland, and recent Canadian legislation will permit psychiatric euthanasia by 2023. This session will explore medicolegal, scientific, ethical and public policy issues related to PAD, focusing on the particular challenges posed with mental illnesses in the context of PAD, including challenges determining irremediability and the overlap between suicidality and psychiatric PAD. Dr. K. Sonu Gaind, a University of Toronto professor and psychiatrist, a past president of the Canadian Psychiatric Association and panelist from the Council of Canadian Academies Expert Panel reviewing psychiatric euthanasia, will review the Canadian experience with PAD, including recent policy developments expanding PAD to non-dying disabled. Issues that have driven Canada’s PAD expansion will be discussed. This session will also explore differences between groups who seek PAD for different reasons, and discuss potential impacts of expanding PAD laws on marginalized populations suffering from life distress.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.006 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.104 | 0.014 |
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".