Knowledge and Attitudes of Hospital Pharmacy Staff in Canada Regarding Medical Assistance in Dying (MAiD)
Bibliographic record
Abstract
ABSTRACTBackground: In February 2015, the Supreme Court of Canada ruled that it was unconstitutional to prohibit physicians from assisting in a patient’s consensual death, thereby setting the groundwork for the legalization of medical assistance in dying (MAiD). Much of the research on this topic has focused on physicians, although other health care professionals will be involved in the process, including pharmacists, pharmacy technicians, and pharmacy assistants. In many provinces, the medications required for MAiD will be dispensed from hospital pharmacies, which will result in direct involvement of hospital pharmacy staff.Objectives: The primary objective was to investigate the knowledge and attitudes of hospital pharmacy staff in Canada regarding MAiD. The secondary objective was to determine the factors that might influence those opinions.Methods: A 34-question web-based survey was available for 6 weeks during early 2017 to hospital pharmacy staff throughout Canada. For most questions, responses were based on a 5-point Likert scale, ranging from “strongly agree” to “strongly disagree”. Descriptive and inferential statistics were used to analyze the data.Results: A total of 1040 valid survey responses were received: 607 from pharmacists, 273 from pharmacy technicians, and 160 from pharmacy assistants. Most respondents were supportive of MAiD; however, nearly all respondents (99% [601/607] of pharmacists, 73% [315/431] of technicians and assistants]) reported lacking comprehensive education on the topic. Despite high levels of overall support, pharmacists tended to be less supportive of MAiD than pharmacy technicians or assistants. Factors that influenced opinions included strong religious beliefs, region, and knowledge of provincial and federal legislation. Conclusions: The majority of respondents, particularly technicians and assistants, were supportive of MAiD, but most respondents lacked education about the topic.RÉSUMÉContexte : En février 2015, la Cour suprême du Canada a statué qu’il était inconstitutionnel d’interdire aux médecins d’aider les patients à mourir par consentement, ce qui a jeté les bases de la légalisation de l’aide médicale à mourir (AMAM). Une grande partie de la recherche sur le sujet était axée sur les médecins, malgré le fait que d’autres professionnels de la santé seront appelés à participer au processus, notamment les pharmaciens, les techniciens en pharmacie et les aides-pharmaciens. Dans bien des provinces, les médicaments nécessaires à l’AMAM proviendront des pharmacies hospitalières, ce qui résultera en la participation directe du personnel de pharmacie hospitalière.Objectifs : L’objectif principal visait à examiner les connaissances et l’attitude du personnel de pharmacie hospitalière au Canada relativement à l’AMAM. L’objectif secondaire était de découvrir les facteurs pouvant influencer les avis du personnel sur le sujet.Méthodes : Pendant six semaines, au début de 2017, un sondage en ligne de 34 questions était à la disposition du personnel de pharmacie hospitalière de partout au Canada. Inspirés de l’échelle de Likert à cinq points, les choix de réponse à la plupart des questions s’étendaient de « fortement d’accord » à « fortement en désaccord ». Des statistiques descriptives et par inférence ont servi à analyser les données.Résultats : Des 1040 réponses valables, 607 provenaient de pharmaciens, 273 de techniciens en pharmacie et 160 d’aides-pharmaciens. La plupart des répondants étaient en faveur de l’AMAM. Cependant, près de l’ensemble des répondants (99 % [601/607] des pharmaciens et 73 %[315/431] des techniciens et des aides) ont signalé ne pas posséder une connaissance suffisante du sujet. Malgré le degré élevé de soutien apporté par l’ensemble des personnes interrogées, l’appui des pharmaciens à l’AMAM tendait à être plus faible que celui des techniciens en pharmacie ou des aides-pharmaciens. Parmi les facteurs propres à influencer les avis des répondants, on trouvait les croyances religieuses fortes, la provenance géographique et la connaissance des lois provinciales et fédérales. Conclusions : La majorité des répondants, particulièrement les techniciens et les aides, était en faveur de l’AMAM, mais la plupart des répondants ne possédaient pas une connaissance suffisante du sujet.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".