405.6: Organ procurement after medical assistance in dying in Canada.
Bibliographic record
Abstract
In Canada since the implementation of Medical Assistance in Dying (MAiD) in 2016, eligible patients have the option to donate their organs and tissues after MAiD. Since then, MAiD legislation has gone through major changes. Accordingly, in 2021, the new eligibility criteria, safeguards, waiver of final consent and monitoring regime have been implemented. Between 2016 and 2021, a total number of 155 patients donated their organs and tissues after MAiD in Canada. While Canada’s rate of deceased organ donations lags behind that of other countries, an international review in 2021 found that Canada is performing the most organ transplants from MAiD patients among the four countries studied that offer this practice. By reviewing the organ donation program after MAiD, this paper discusses some ethical challenges. It argues that while, MAiD itself poses procedurally complex challenges that can be a major source of ethical concerns; more attention should be given to the approval process for organ donation after MAiD and to ensure that appropriate measures are in place to protect patients in those circumstances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".