An entrustable professional activity descriptor for medical aid in dying: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: In jurisdictions where medical aid in dying (MAiD) is legal, there is an obligation to ensure the competence of those who assess eligibility and provide MAiD to patients. Entrustable professional activities (EPAs) are one framework for incorporating competency-based training and assessment into the workplace, so we convened a group of experienced MAiD providers to develop an EPA descriptor for MAiD. METHODS: We performed a mixed-methods sequential qualitative (focus group via 2 teleconferences) and quantitative (survey) study to generate and refine a consensus descriptor using open coding followed by a modified Delphi approach. Participants were experienced MAiD assessors and providers identified purposively from a national community of practice in Canada. RESULTS: Of the 22 MAiD assessors and providers invited to participate in the focus group, 13 (59%) agreed. The focus group divided MAiD into 3 components: assessment, preparation and provision of MAiD. Participants identified key knowledge, skills and attitudes for each component. They also suggested teaching approaches, potential sources of information to evaluate progress and a potential basis for evaluating progress and entrustment. Key points from this descriptor were sent via survey to 88 assessors and providers, of whom 64 (73%) responded. Respondents agreed on all key points except for the conditions of entrustment; these were modified based on feedback and sent back to the respondents for a second Delphi round, where agreement was achieved. INTERPRETATION: We achieved a high degree of agreement on a competency-based descriptor of MAiD in the form of an EPA. This can be used to inform practice standards, curriculum development and/or assessment of competence among learners and practising providers alike.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".