Organ Donation and Medical Assistance in Dying in Canada
Bibliographic record
Abstract
Introduction In June 2016, medical assistance in dying (MAiD) became legal in Canada. This has prompted many to ask whether organ donation following MAiD should be permitted and under what circumstances. Indeed, since the MAiD legislation came into effect, reports indicate that a few individuals have donated organs following medical assistance in dying. This study considers the legal and ethical questions that arising regarding organ donation following MAiD in Canada. Methods The authors have engaged in a review of the relevant federal and provincial laws and policies governing MAiD and organ donation. These include the law establishing the conditions under which an individual may undergo MAiD, provincial laws setting out the consent requirements for organ donation and pre-mortem interventions to determine eligibility for organ donation and conscientious objection policies for health care providers. The authors have also reviewed the relevant national and international literature on organ donation following MAiD. Results Our study indicates that there are several important legal and ethical questions that must be considered in developing a framework for organ donation following MAiD in Canada. What legal protections are necessary to ensure free and informed consent to organ donation following MAiD? Several concerns arise such as the risk that patients may feel pressured or coerced to consent to organ donation. Potential safeguards to ensure consent is voluntary include precluding physicians and organ procurement organizations from approaching patients to seek their consent for organ donation, separating the decisions concerning MAiD and organ donation, precluding directed donation and ensuring that patients are aware that they can withdraw consent to MAiD and/or organ donation at any time. Other pressing questions include the extent to which health care professionals may exercise a conscientious objection to this practice and whether the recipient of an organ should be informed that an organ was obtained from an individual who underwent MAiD. In addition to domestic legislation, the experience of countries like Belgium and the Netherlands offer important guidance in addressing some of these difficult questions. Conclusion Organ donation following MAiD has the potential to benefit patients and increase organs available for donation in Canada. This study considers the difficult legal and ethical questions that arise in this context and proposes a framework for organ donation following MAiD in Canada. Reference: 1. Criminal Code - R.S.C., 1985, c. C-46 (Section 241).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".