Interview - Justin Sanders on Medical Assistance in Dying
Bibliographic record
Abstract
Jonathan and Chris speak to Dr. Justin Sanders, the director of palliative care at McGill, on the topic of medical assistance in dying (MAiD). What are the arguments for and against MAiD? And what do we make of the thornier aspects of euthanasia, like its intersection with disability rights and the conflict of interest at the heart of a government that funds both assisted suicide and palliative care? (2:30) What is palliative care?(10:21) The history of MAiD in Canada(15:34) Sedation vs. MAiD(19:30) The need for control at the end of life(21:15) The arguments for MAiD(23:34) Feeling like a burden(27:33) MAiD vs. disability rights(36:20) The social safety net and the government's conflict of interest(41:55) Most people don't want to die(44:22) MAiD for mental illness(49:25) Tenfold increase in Canadians receiving MAiD * Theme music: \\"Fall of the Ocean Queen\\" by Joseph Hackl. To contribute to The Body of Evidence, go to our Patreon page at: http://www.patreon.com/thebodyofevidence/. Patrons get a bonus show on Patreon called \\"Digressions\\"! Check it out! Links:1) Carter v. Canada: https://scc-csc.lexum.com/scc-csc/scc-csc/en/item/14637/index.do2) Information on medical assistance in dying in Canada: https://www.canada.ca/en/health-canada/services/medical-assistance-dying.html3) CTV's coverage of the expansion into mental illness: https://www.ctvnews.ca/politics/the-issue-of-expanding-assisted-dying-eligibility-has-already-been-decided-senator-1.63137404) The 2021 report on MAiD services in Canada: https://www.canada.ca/en/health-canada/services/medical-assistance-dying/annual-report-2021.html
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.161 | 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; both teacher heads agree on what is shown here.
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".