Medically assisted dying in Canada and unjust social conditions: a response to Wiebe and Mullin
Bibliographic record
Abstract
In the paper, titled 'Choosing death in unjust conditions: hope, autonomy and harm reduction,' Wiebe and Mullin argue that people living in unjust social conditions are sufficiently autonomous to request medical assistance in dying (MAiD). The ethical issue is that some people may request MAiD primarily because of unjust social conditions, not their illness, disease, disability or decline in capability. It is easily agreed that people living in unjust social conditions can be autonomous. Nevertheless, Wiebe and Mullin fail to appreciate that autonomy is only a necessary condition for MAiD. In addition to autonomy, one must decide that providing assisted dying to a patient because they are living in unjust social conditions is ethical. Central to making this ethical decision is the principle of non-maleficence, famously articulated as 'do no harm.' The authors admit that performing MAiD in response to unjust social circumstances is harmful, but they justify this harmful action by appealing to the principle of harm reduction. A fundamental flaw of their approach is that it relies on the legislative definition of intolerable suffering, which is based on circular reasoning and given that 99.2% of patients that have applied for MAiD satisfied this criterion, it is essentially equivalent to no standard/criterion. Canadian society is struggling with the ethical implications of its permissive MAiD programme, and, fundamental to this debate, will be determining the proper balance between autonomy and non-maleficence for people living in unjust social conditions.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.049 | 0.031 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.068 | 0.081 |
| 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".