Shades of gray: Conscientious objection in medical assistance in dying
Bibliographic record
Abstract
With the advent of legalized medical assistance in dying [MAiD] in Canada in 2016, nursing is facing intriguing new ethical and theoretical challenges. Among them is the concept of conscientious objection, which was built into the legislation as a safeguard to protect the rights of healthcare workers who feel they cannot participate in something that feels morally or ethically wrong. In this paper, we consider the ethical complexity that characterizes nurses' participation in MAiD and propose strategies to support nurses' moral reflection and imagination as they seek to make sense of their decision to participate or not. Deconstructing the multiple and sometimes conflicting ethical and professional obligations inherent in nursing in such a context, we consider ways in which nurses can sustain their role as critically reflective moral agents within a context of a relational practice, serving the diverse needs of patients, families, and communities, as Canadian society continues to evolve within this new way of engaging with matters of living and dying.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".