Institutional Resistance to Medical Assistance in Dying in Canada: Arguments and Realities Emerging in the Public Domain
Bibliographic record
Abstract
Since the legalization of medical assistance in dying (MAiD) in Canada in 2016, volitional non-participation in MAiD on the part of some healthcare institutions has revealed ethical uncertainties, potential access problems, and policy gaps. The problem has remained much neglected in the literature base, with no comprehensive studies on the subject so far. We analyzed print media articles and grey literature on institutional objections to and non-participation in MAiD. Thematic analyses were performed on all data to better understand the diverse stakeholder arguments and positions that characterize this important public health debate. Our search yielded 89 relevant media articles and 22 legislative, policy, and other relevant documents published since 2016 in the English language. We identified four main themes about institutional refusals to participate in MAiD, articulated as the following questions: (1) Who has the right to conscience? (2) Can MAiD be considered a palliative practice? (3) Are there imbalances across diverse stakeholder rights and burdens? and (4) Where are the gaps being felt in MAiD service implementation? Stakeholder views about institutional conscience with respect to MAiD are varied, complex, and evolving. In the absence of substantial systematic evidence, public domain materials constitute a key resource for understanding the implications for service access and determining the relevance of this contentious issue for future MAiD research and policy.
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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 | 0.046 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.027 | 0.043 |
| Scholarly communication | 0.024 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".