A Defence of Conscientious Objection in Medicine: A Reply to Schuklenk and Savulescu
Bibliographic record
Abstract
In a recent (2015) Bioethics editorial, Udo Schuklenk argues against allowing Canadian doctors to conscientiously object to any new euthanasia procedures approved by Parliament. In this he follows Julian Savulescu's 2006 BMJ paper which argued for the removal of the conscientious objection clause in the 1967 UK Abortion Act. Both authors advance powerful arguments based on the need for uniformity of service and on analogies with reprehensible kinds of personal exemption. In this article I want to defend the practice of conscientious objection in publicly-funded healthcare systems (such as those of Canada and the UK), at least in the area of abortion and end-of-life care, without entering either of the substantive moral debates about the permissibility of either. My main claim is that Schuklenk and Savulescu have misunderstood the special nature of medicine, and have misunderstood the motivations of the conscientious objectors. However, I acknowledge Schuklenk's point about differential access to lawful services in remote rural areas, and I argue that the health service should expend more to protect conscientious objection while ensuring universal access.
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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 | 0.029 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.035 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.126 | 0.115 |
| Insufficient payload (model declined to judge) | 0.003 | 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".