Medical Assistance in Dying in health sciences curricula: A qualitative exploratory study
Bibliographic record
Abstract
BACKGROUND: This paper offers insight into (1) the driving and restraining forces impacting the inclusion of medical assistance in dying (MAID) in health sciences curricula, (2) the required resources for teaching MAID, and (3) the current placement of MAID in health sciences curricula in relation to end-of-life care concepts. METHOD: We conducted a qualitative exploratory study in a Canadian province using Interpretive Description, Force Field Analysis, and Change as Three Steps. We interviewed ten key informants (KI), representing the provincial health sciences programs of medicine, nursing, pharmacy, and social work. KIs held various roles, including curriculum coordinator, associate dean, or lecturing faculty. Data were analyzed via the comparative method using NVivo12. RESULTS: Curriculum delivery structures, resources, faculty comfort and practice context, and uncertainty of the student scope of practice influenced MAID inclusion. Medical and pharmacy students were consistently exposed to MAID, whereas MAID inclusion in nursing and social work was determined by faculty in consideration with the pre-existing course objectives. The theoretical and legal aspects of MAID were more consistently taught than clinical care when faculty did not have a current practice context. Care pathways, accreditation standards, practice experts, peer-reviewed evidence, and local statistics were identified as the required resources to support student learning. MAID was delivered in conjunction with palliative care and ethics, legalities, and professional regulation courses. CONCLUSION: The addition of MAID in health sciences curricula is crucial to support students in this new practice context. Identifying the drivers and restrainers influencing the inclusion of MAID in health sciences curricula is critical to support the comprehensiveness of end-of-life education for all students.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".