From Courtroom to Bedside - A Discussion with Dr. Jeff Blackmer on the Implications of Carter v. Canada and Physician-Assisted Death
Bibliographic record
Abstract
Introduction:On February 6th, 2015, the Supreme Court of Canada (SCC) concluded that “the prohibition on physician-assisted dying is void insofar as it deprives a competent adult of such assistance where (1) the person affected clearly consents to the termination of life; and (2) the person has a grievous and irremediable medical condition (including an illness, disease or disability) that causes enduring suffering that is intolerable to the individual in the circumstances of his or her condition.”[1]. The Court added, “The declaration of invalidity is suspended for 12 months,”[1] to allow the government to respond with appropriate legislation to guide and regulate the practice of Physician-Assisted Death (PAD).Dr. Jeff Blackmer is the Vice President of Medical Professionalism at the Canadian Medical Association (CMA). He holds a Masters in Medical Ethics from the University of Toronto. He served as the Executive Director of the CMA’s Office of Ethics, Professionalism and International Affairs and has been the interim Director of Ethics for the World Medical Association in Geneva. In an interview on February 11th, Dr. Blackmer kindly agreed to help us navigate through an array of ethical and practical ramifications stemming from the decision on Carter v. 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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.056 | 0.021 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.060 | 0.083 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".