Health leaders’ perspectives and attitudes on medical assistance in dying and its legalization: a qualitative study
Notice bibliographique
Résumé
BACKGROUND: Medical Assistance in Dying (MAiD) has transformed health policy and practice on death and dying. However, there has been limited research on what shaped its emergence in Canada and the beliefs and views of health leaders who hold positions of influence in the healthcare system and can guide policy and practice. The objective of this study was to examine health leaders' perspectives on the factors that led to the emergence of MAiD and explore their attitudes about the legalization of MAiD. METHODS: In this qualitative study, we conducted online semi-structured interviews with health leaders from April 2021 to January 2022. Purposive and snowball sampling techniques were used to recruit health leaders who have expertise and engagement with the delivery of MAiD or palliative and end-of-life care, and who hold positions of leadership relevant to MAiD in their respective organisations. Inductive thematic analysis was used to analyze the transcribed interviews. RESULTS: Thirty-six health leaders were interviewed. Participants identified six factors that they believed to have led to the introduction of MAiD in Canada: public advocacy and influence; judicial system and notable MAiD legal cases; political ideology and landscape; policy diffusion; healthcare system emphasis on a patient-centred care approach; and changes in societal and cultural values. Participants expressed wide-ranging attitudes on the legalization of MAiD. Some described overall agreement with the introduction of MAiD, while still raising concerns regarding vulnerability. Others held neutral attitudes and indicated that their attitudes changed on a case-by-case basis. Participants described four factors that they considered to have had influence on their attitudes: personal illness experiences; professional experiences and identity; moral and religious beliefs; and, the valence of patient autonomy and quality of life. CONCLUSIONS: This study highlights the wide-ranging and complex attitudes health leaders may hold towards MAiD and identifies the convergence of multiple factors that may have contributed to the legalization of MAiD in Canada. Understanding health leaders' attitudes and perspectives on the legalization of MAiD may inform stakeholders in other countries who are considering the legalization of assisted dying.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».