Medical assistance in dying: Examining Canadian pharmacy perspectives using a mixed-methods approach
Bibliographic record
Abstract
BACKGROUND: Canada legalized assisted dying with the passing of Bill C-14, Medical Assistance in Dying (MAiD), in June 2016. This legislation has implications for health care professionals participating in MAiD. This research aims to understand the effect that MAiD has on pharmacists and pharmacy technicians in Canada. METHODS: We conducted a thematic document analysis of pharmacy guidelines, position statements and standards of practice from pharmacy regulatory authorities across Canada. In addition, the Ontario Pharmacists Association surveyed its members (including pharmacists, pharmacy technicians and pharmacy students) about their perceptions of MAiD. RESULTS: Our thematic analysis of the reviewed documents resulted in 3 major themes: pharmacists' role in quality assurance, practice considerations when implementing MAiD and resources for pharmacy staff involved in MAiD. Survey responses illustrated that most (68%) pharmacy staff would dispense MAiD medications. Nonetheless, many respondents perceived that they lacked knowledge or comfort with different aspects of the MAiD process. Overall, 80% of participants reported a desire for professional development about MAiD. CONCLUSION: Despite the rapidly changing landscape surrounding medical assistance in dying within the past year, most pharmacy regulatory authorities have provided direction and resources to their pharmacists. Ontario pharmacists and pharmacy technicians are willing to dispense MAiD medications; however, additional support in the form of professional development may be necessary based on participants' desire for education coupled with their perceived lack of knowledge. Future research may focus on the efficacy of provincial guidelines in supporting pharmacists' participation 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 | 0.041 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".