Development of Learning Objectives for a Medical Assistance in Dying Curriculum for Family Medicine Residency
Bibliographic record
Abstract
Abstract Background Medical assistance in dying (MAID) became legal across Canada when Bill C-14 was passed in 2016. Currently, little is known about the most effective strategies for providing MAID education, and the importance of integrating MAID into existing curricula. In this study, a set of learning objectives (LOs) was developed to inform a foundational MAID curriculum in Canadian Family Medicine (FM) residency training programs. Methods Mixed-methods were used to develop LOs based on a previously-published needs assessment from a large, four-site family medicine residency program in southeastern Ontario. Draft LOs were evaluated and modified using a modified Delphi process and focus group which included faculty and resident leaders. LOs were mapped to the existing family medicine residency curriculum, as well as the College of Family Physicians of Canada’s Priority Topics and CanMEDS-Family Medicine roles. Results Nine LOs were developed to provide a foundational education regarding MAID. While all LOs could be mapped to the Domains of Clinical Care with the departmental curriculum, they mapped inconsistently to departmental Entrustable Professional Activities, the Priority Topics, and CanMEDS-FM roles. LOs focused on patient education and identification of patient goals were most readily mapped to existing curricular framework, while LOs with MAID-exclusive content revealed gaps in the current curriculum. Conclusions The developed LOs provide a guide to ensure family medicine residents obtain generalist-level knowledge to counsel their patients about MAID. These LOs can serve as a model for developing learning objectives for both family medicine and specialist residency programs in Canada, as well as globally in countries where assisted dying is legal.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".