Institutional Conscientious Objection to Medical Assistance in Dying in Canada: A Critical Analysis of the Personnel-Based Arguments
Bibliographic record
Abstract
Debate rages over whether Canadian provincial and territorial governments should allow healthcare institutions to conscientiously object to providing medical assistance in dying (MAiD). This issue is likely to end up in court soon through challenges from patients, clinicians, or advocacy groups such as Dying With Dignity Canada. When it does, one key question for the courts will be whether allowing institutional conscientious objection (ICO) to MAiD respects (i.e., shows due regard for) the consciences of the objecting healthcare institutions, understood as unitary entities. This question has been thoroughly explored elsewhere in the academic literature. However, another key question has been underexplored. Specifically, precedent set by the Supreme Court of Canada’s decision in Loyola High School v. Quebec (Attorney General) suggests that the courts will consider whether allowing ICO to MAiD respects the consciences of the personnel within objecting healthcare institutions. My answer to this question is no, by which I mean that allowing ICO to MAiD shows undue disregard for some consciences and undue regard for others. To justify this answer, I analyze the arguments that hold that allowing ICO in healthcare respects the consciences of the personnel within objecting healthcare institutions. My conclusion is that none of these personnel-based arguments succeed in the case of ICO to MAiD. Some fail because they are wrong about the nature of conscience and complicity. Others fail because they contradict the arguments’ proponents’ positions on conscientious objection by individual healthcare providers. Still others fail because they are internally inconsistent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.042 | 0.046 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".