Medical Assistance in Dying: A Review of Canadian Regulatory Documents
Bibliographic record
Abstract
After years of heated debate about the issue, medical assistance in dying (MAiD) was legalized in Canada in 2016. Canada became the first jurisdiction where MAiD may be delivered by nurse practitioners as well as physicians. Experience has revealed significant public demand for the service, and Canadians expect nurses to advocate for safe, high-quality, ethical practice in this new area of care. Pesut et al. offer a superb analysis of the related Canadian nursing regulatory documents and the challenges in creating a harmonized approach that arise in a federation where the Criminal Code is a federal entity and the regulation of health care providers and delivery of care fall under provincial and territorial legislation. Organizations like the Canadian Nurses Association contribute to the development of good legislation by working with partners to present evidence to help legislators consider impacts on public health, health care, and providers. Nursing regulators across Canada responded quickly to the unfolding policy landscape as the federal legislation evolved and will face that task again: In February 2020, the federal government tabled legislation to relax conditions related to MAiD requests that will force regulators and professional associations back to public advocacy and legislative tables. The success of the cautious approach exercised by nursing bodies throughout this journey should continue to reassure Canadians that their high trust in the profession is well placed.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".