Designing a Curriculum Outline for MAiD Based on Canada for Medical Students
Bibliographic record
Abstract
Medical Assistance in Dying (MAiD) remains to be a global debate. However, within Canada, MAiD has made several breakthroughs and has progressed exponentially. This paper explores the ethical, educational, and legal challenges surrounding MAiD, while also shedding light on the future mental health policies surrounding MAiD. Through synthesis of existing literature, multiple concerns were identified: the tensions surrounding patient autonomy, the necessity to address Voluntary Stopping Eating and Drinking (VSED), gaps in practitioner education, and the emotional impacts of MAiD. The findings below place an emphasis on the necessity to improve education surrounding MAiD, emphasizing the need to prepare practitioners and citizens in future progression. This paper explains the significance of intertwining ethics and education for MAiD. Through the identified key concerns, an outline for an educational curriculum of an educational curriculum on MAiD is proposed—one that is sensitive to the ethical, legal, and emotional complexities. The paper underscores the importance of addressing the gaps in public knowledge of MAiD through structured education. By developing an outline for a curriculum, in the future, society will be able to keep up with the advancements in MAiD.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".