Development of a Canadian Medical Assistance in Dying Curriculum for Healthcare Providers
Bibliographic record
Abstract
Objectives: Medical Assistance in Dying (MAiD) was legalized in Canada in 2016, necessitating greater education and training in MAiD for physicians and nurse practitioners. To meet this need, the Canadian MAiD Curriculum (CMC) was developed to offer a nationally accredited, comprehensive, bilingual, hybrid (synchronous and asynchronous) educational program to support and enhance the practice of MAiD in Canada. Methods: This work describes the process of developing the CMC, including its guiding principles and framework. The CMC was guided by constructivism and adult learning theory, preliminary literature review, 5 key principles based on a needs assessment survey, as well as consultation with diverse partners. Results: Seven modules were developed: (1) foundations of MAiD in Canada, (2) clinical conversations that includes MAiD, (3) how to do an MAiD assessment, (4) capacity and vulnerability, (5) providing MAiD, (6) navigating complex cases with confidence, and (7) MAiD and mental disorders. An eighth topic on clinician resilience and reflection was woven into each of the 7 modules. Conclusion: This curriculum ensures that consistent information is available to healthcare providers concerning the practice of MAiD in Canada. To ensure sustainability, the CMC will continue to be updated alongside the evolution of MAiD policy and services in Canada.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".