Perception of roles across the interprofessional team for delivery of medical assistance in dying
Bibliographic record
Abstract
In 2016, Canada joined many jurisdictions worldwide in legalizing Medical Assistance in Dying (MAiD). Given the paucity of qualitative research regarding the involvement of interprofessional health care providers (HCPs) in MAiD, the goal of this study was to better understand how HCPs viewed their role(s). Semi-structured interviews were conducted with 3 pharmacists, 10 nurses, and 8 social workers at an academic hospital in Toronto. Thematic analysis generated six broad themes: 1) Practical/Technical Component, 2) Education, 3) Support, 4) "Part of the Job," 5) "All of the Job," and 6) Lack of Published Information. While nurses and social workers espoused many commonalities, nursing roles were more "in the moment," whereas social workers viewed their roles as beginning earlier and extending after provision of MAiD. There was a spectrum of how participants perceived their role: pharmacists minimized the task of dispensing medications as an insignificant experience, nurses viewed involvement as consistent with their other professional duties (specifically non-MAiD deaths), and social workers described MAiD as a unique opportunity to employ the full gamut of their skills. The study highlights the importance of supporting HCPs through education and information at both regulatory and research levels, recognizing the key roles they play in MAiD.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".