Does Legalization of Medical Assistance in Dying Affect Rates of Non-Assisted Suicide?
Bibliographic record
Abstract
In 2015, the Southern Medical Journal published a paper by Jones and Paton that explored the effects of legalizing physician-assisted suicide (PAS) on state-level suicide rates using empirical data from Oregon, Washington, Montana, and Vermont. It is essential to assess this paper critically because: a) there is little literature on this topic and so any available papers may be given significant weight in public and policy debates on medical assistance in dying (MAiD) in Canada; b) the paper has been used in attempts to support particular policy positions in relation to MAiD in Canada (e.g., Sonier, 2016); and c) the paper may be referenced as an academic foundation for claims about the effects of legalization that will be made in the Charter challenge to the new Canadian MAiD legislation (Lamb v. Canada (Attorney General), 2016) and ongoing debates in Canada (including the statutorily mandated independent reviews commissioned by the government and being conducted by an independent expert panel appointed by the Council of Canadian Academies on three outstanding issues regarding access to MAiD in Canada). We present a description of the Jones and Paton paper’s objectives, methods, and results. We then explore its strengths and weaknesses, and illustrate the problems with interpreting the data as the authors have done. We conclude that the interpretations of the authors are not supported by the data presented and we caution against using the authors’ conclusions for the purposes of informing public opinion, litigation, and law reform. A quick comment about terminology is required here. “Medical assistance in dying” is the umbrella term used in the new Canadian legislation to capture both euthanasia (“the administering by a medical practitioner or nurse practitioner of a substance to a person, at their request, that causes their death”) and assisted suicide (“the prescribing or providing by a medical practitioner or nurse practitioner of a substance to a person, at their request, so that they may self-administer the substance and in doing so cause their own death”) (An Act to amend the Criminal Code and to make related amendments to other Acts (medical assistance in dying), S.C. 2016, c.3, s.3). Jones and Paton refer to “physician-assisted suicide” because, under the American model for assisted dying, only assisted suicide is permitted and only physicians are permitted to provide the assistance.
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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 | 0.006 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".