Exploring Canadian Physicians' Experiences Providing Medical Assistance in Dying: A Qualitative Study
Bibliographic record
Abstract
CONTEXT: Medical assistance in dying (MAiD) allows a practitioner to administer or prescribe medication for the purpose of ending a patient's life. In 2016, Canada was the latest country, following several European countries and American states, to legalize physician-assisted death. Although some studies report on physicians' attitudes toward MAiD or describe patient characteristics, there are few studies that explore the professional challenges faced by physicians who provide MAiD. OBJECTIVES: The objective of the study was to explore the professional challenges faced by Canadian physicians who provide MAiD. METHODS: Sixteen physicians from across Canada who provide MAiD completed in-depth, semistructured telephone interviews. An inductive thematic analysis approach guided data collection and the iterative, interpretive analysis of interview transcripts. Three members of the research team systematically co-coded interview transcripts, and the emerging themes were developed with the broader research team. NVivo was used to manage the coded data. RESULTS: Participants described three challenges associated with providing MAiD: 1) their relationships with other MAiD providers were enhanced and relationships with objecting colleagues were sometimes strained; 2) they received inadequate financial compensation for time, and 3) they experienced increased workload, resulting in sacrifices to personal time. Although these providers did not intend to stop providing MAiD at the time of the interview, they indicated their concerns about whether they would be able to sustain this service over time. CONCLUSION: Physicians described relationship, financial, and workload challenges to providing MAiD. We provide several recommendations to address these challenges and help ensure the sustainability of MAiD in countries that provide this service.
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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 | 0.000 |
| 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) | 0.000 | 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".