Navigating crossroads: exploring organ donation following medical assistance in dying
Bibliographic record
Abstract
The emergence of medical assistance in dying (MAiD) as an accepted practice in numerous countries has promoted the possibility of organ donation following circulatory determination of death after MAiD.1,2 In the past few years, patients who requested MAiD in Canada, The Netherlands, Belgium, and, most recently, Spain, have been permitted to make an informed decision about organ donation.3–5 On one hand, this practice is legally permissible and seen by many as a way to respect patients’ autonomy and help fulfill their last wishes of organ donation.6 On the other hand, few junctures are as ethically intricate and emotionally intense in health care delivery as the convergence of MAiD and organ donation; therefore, many experts have also raised concerns around the ethics of such a procedure considering the potential emotional vulnerability of patients choosing MAiD.7,8 Any novel health care practice, such as organ donation after MAiD, requires precise regulatory frameworks and evidence-based practice guidelines to not only uphold patient safety and quality processes, but also to ensure socially acceptable and equitable and ethical access to such procedures. Due to changes in the Canadian legislation for MAiD, updates to the policy guidance for practice of organ donation following MAiD were necessary.9 As a funded initiative by Canadian Blood Services, our research team conducted a comprehensive synthesis using the JBI approach for scoping reviews10 to establish a foundation to substantiate and guide the discussion of organ donation after MAiD. This review aimed to investigate the various aspects of organ donation following MAiD practice worldwide, including ethical considerations, legal requirements, clinical pathways, and provider viewpoints. It also aimed to identify research gaps, inviting readers to explore this unique topic and potentially fill in the gaps in the available research and literature that have been identified. This project resulted in a 2-part review published in this issue of JBI Evidence Synthesis.11,12 In part I,11 we synthesized the literature on the legal and ethical dimensions of the practice of organ donation following MAiD. Readers are able to review the existing legal definitions and legislation from jurisdictions where this novel procedure is permitted, and further explore the main points of debate and experts’ opinion on ethical dilemmas and consensus, including patient consent and objection, as well as public perceptions and social acceptance of this practice.11 In part II,12 readers embark on a deeper exploration of the topic, particularly of the processes involved in organ donation following MAiD, and the roles and perceptions of the health care providers involved. We have included visual aids and detailed explanations of the different pathways used worldwide for organ donation processes following MAiD: the steps for organ donation following MAiD in the hospital setting; MAiD at the patient’s home with organ retrieval in the hospital; and MAiD starting at home with completion in the hospital setting, followed by organ retrieval.12 We have also examined the processes of organ donation following MAiD from the viewpoint of the health care professionals involved in the processes, and procedures around the potential benefits and challenges of the practice. Next, we explored insights into the roles and perceptions of health care providers during organ donation following MAiD, acknowledging the emotional weight carried by these professionals. Finally, the crucial role of clinical practice tools, and the need for education and support for health care providers involved in this juncture are explored.12 This 2-part review offers a comprehensive foundation to guide further exploration of the existing evidence and, as mentioned, provides the basis for future initiatives in this field that require navigation of a complex blend of ethics, legality, and health care practices of organ donation following MAiD in our society. In a world where medical ethics are in constant flux and where compassion for others propels change, the boundaries of what is possible and acceptable are continuously pushed. Reviews such as this offer an essential point of reference to foster discussions among various stakeholders, prompt policymakers to reflect, and cultivate empathy among health care providers. By investigating the legal and ethical aspects of this practice and thoroughly examining its current processes and procedures, this 2-part review will assuredly spark informed discussions, introspection, and purposeful scientific advancement.11,12
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".