Bibliographic record
Abstract
Canada’s new medical assistance in dying (MAiD) law is ethically superior to the previous version. I agree with Udo Shuklenk and Jocelyn Downie1 that both social determinants of health and slippery slope objections to the recent amendments are unsuccessful.[1] Despite this broad agreement, I worry that the authors’ argument against the slippery slope objection is too focused on the current amendments at the expense of future changes. Before I address that argument, I have one point about the social determinants of health. Any treatment that goes against a patient’s values is wrong. Critics of expanding MAiD criteria are concerned that expanded access will mean that persons with disabilities, among others, will be more likely to be pressured into getting an assisted death. Were there is evidence for this worry, it ought to be taken seriously. However, in my work as a clinical ethicist, it is overwhelmingly more common for me to be consulted about clinical situations where patient values are disregarded for the sake of invasive life-sustaining treatment, rather than concerns about hastened death. This takes different forms. In some cases, a healthcare provider or a family member will pressure a patient who has decision-making capacity. This is why patients occasionally ask that their request for MAiD be kept private from their family: the patient is worried about being pressured out of it (or of dealing with the other effects of MAiD’s stigma). In non-MAiD cases, a patient’s substitute decision-maker will request treatment with the aim of maximising length of life, despite knowledge that the patient did not want life-sustaining treatment …
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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 | 0.054 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.075 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.066 | 0.120 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".