Exploring How Internal Medicine Residents Approach Requests for Medical Assistance in Dying: An Exploratory Qualitative Study at the University of Calgary
Bibliographic record
Abstract
To develop Medical Assistance in Dying (MAiD) competencies and curricula, we aimed to create a framework outlining how internal medicine (IM) residents approach MAiD requests and identify subsequent learning gaps. We used qualitative descriptive methodology to explore individual participant responses to three clinical vignettes centered on aspects of MAiD, followed by a group discussion. Responses were recorded and transcribed. Codes were reviewed and iteratively organized into themes, highlighting the steps taken for each MAiD scenario. Themes were compared and classified into broader categories within a framework to describe the participants’ overall approach to MAiD requests. Three overarching categories illustrated the approach taken by participants when faced with MAiD requests: 1) Action: the pragmatic steps participants took to respond to requests 2) Decision: the rationale behind how participants decided if MAiD was an option; and 3) Reaction: the emotional reactions that arise from requests; each highlighted significant learning gaps. Participants lacked understanding in concurrent medical and symptom management, MAiD eligibility and referral criteria, roles and responsibilities, and were uncomfortable discussing MAiD. IM residents not only require education on MAiD as a topic but on developing an approach to responding to requests and ways of addressing subsequent personal reactions.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".