An Educator’s Reflection on the exploration of the ethical issues of Medical Assistance in Dying (MAiD) with Nursing and Healthcare Students
Bibliographic record
Abstract
In 2016, the Canadian federal government passed Bill C-14, a statute to enable those with irremediable conditions to obtain medical assistance in dying. This paper is my reflection from the dialogues I continue to have with students in lectures where we explore the various issues that MAiD presents for the health care system and for physicians and nurses who are called upon to assist in dying, with specific reference to the Canadian context from the perspectives of human rights and Kantian and utilitarian ethics. Specific attention is paid to conscientious objection within the Canadian context to fully protect nurses and physicians from negative consequences for whom participation in medically assisted dying is ethically objectionable. A relational approach is proposed as a framework that acknowledges the vulnerability of both nurses and those who are suffering as an ethical approach to avoid Kant’s concern with using persons as means to an end.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.034 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.019 | 0.058 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".