216.8: Organ donation following medical assistance in dying: A Canadian environmental scan.
Bibliographic record
Abstract
Introduction: The practice of organ donation following Medical Assistance in Dying (MAiD) presents complex ethical, legal, and procedural challenges. The MAiD-Donor Environmental Scan in Canada is designed to investigate the policies, procedures, and guidelines governing this practice. Given the evolving nature of MAiD, this study aims to provide clarity and insight into the processes surrounding organ donation post-MAiD, ensuring ethical integrity, patient autonomy, and procedural transparency within the Canadian healthcare system. Method: This environmental scan involves four phases. In Phase 1, a scoping review will be updated using the JBI approach and PRISMA guidelines to capture literature published between December 2021 and the present. Phase 2 employs a cross-sectional survey to gather insights from Organ Donation Organizations (ODOs) and healthcare professionals across Canada, identifying gaps in current practices. Phase 3 utilizes in-depth qualitative interviews with key stakeholders including ODO managers, Organ and Tissue Donation Coordinators (OTDCs), and MAiD providers to explore existing protocols. Phase 4 will involve a retrospective data review, analyzing organ donation statistics post-MAiD to uncover regional patterns and trends. Results: The updated scoping review (completed) provides an overview of current literature on organ donation after MAiD, highlighting key gaps in policy and practice. The survey and interviews (ongoing) are collecting data on the specific challenges faced by healthcare professionals involved in organ donation following MAiD, offering insights into existing protocols, medication combinations, and regional differences in practices. The retrospective data (to be started) review will provide further evidence on the success rates of organ donation post-MAiD and the characteristics of high-performing regions. Conclusion: The MAiD-Donor Environmental Scan aims to address the gaps in the current understanding of organ donation following MAiD. By providing evidence-based insights into current practices, the study will contribute to the development of improved policies and procedures that ensure ethical and effective practices in organ donation. The results of this study will support healthcare professionals by fostering best practices, improving resilience, and enhancing the overall integrity of the organ donation process in Canada. Ultimately, this research will promote patient autonomy and dignity, while improving transplantation outcomes across the country. This work has been funded by Canadian Blood Services and Canadian Association of MAiD Assessors and Providers.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.034 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".