Understanding the Factors Explaining the Growing Use of Medical Assistance in Dying in Québec: Protocol for an Interdisciplinary Mixed Methods and Multimethods Study (Preprint)
Notice bibliographique
Résumé
BACKGROUND Medical assistance in dying (MAiD) became a legal end-of-life option on December 10, 2015, in Québec, and on June 17, 2016, in the rest of Canada. Since its legalization, there has been a steady increase in the number of MAiD requests and provisions. Across permissive jurisdictions, Québec now has the highest rate of assisted death. Despite the growing use of MAiD, research examining the factors driving this increase remains limited and fragmented. Existing studies offer partial and sometimes contradictory explanations, with little integration of legal, institutional, societal, and individual dimensions. Further research is needed to better understand the determinants of MAiD requests and practices, particularly in the Canadian and Québec contexts. OBJECTIVE This research aims to understand the factors influencing changes in MAiD requests and administrations in Québec by examining laws, practices, societal perspectives, organization of care and services, and individual characteristics of those requesting MAiD, as well as their interrelationships. We present the protocol developed by the Consortium interdisciplinaire de recherche sur l'aide médicale à mourir, an interdisciplinary research consortium, including an international advisory committee, set up for this research. METHODS The design of this protocol is multimethods and convergent mixed methods, including (1) an international cross-thematical approach with 4 main research methods (a scoping review, key informant interviews, focus groups with health care professionals, and a population-based survey) chosen to partially answer research questions across the entire study and to compare with other jurisdictions and (2) 11 theme-specific methods (including community forums, media coverage analysis, comparative legal analyses, case studies of triads, individual interviews, and system mapping) to enrich and complement findings from the cross-thematical approach. RESULTS When this 3-year funded study started in July 2024, several research methods not requiring ethics committee approval (because no human participants were involved) were initiated, including scoping and systematic reviews, media coverage analysis, and comparative legal analyses. By August 2025, interviews with key informants were completed, and analyses took place in September. Concurrently, other subteams started data collection (focus groups December 2025) or are getting ready to seek ethics approval for their protocols and data collection processes involving human participants: case studies of triads, individual interviews, and community forums. CONCLUSIONS Findings from the international cross-thematical approach and theme-specific methods will provide a comprehensive understanding of the factors influencing the use of MAiD in Québec. This study has strengths, including the use of a specific theoretical framework, a variety of complementary methods, and an integrated knowledge mobilization strategy. As for its limitations, we foresee challenges with the comparison of jurisdictions in terms of language, culture, and legal systems, as well as access to data about MAiD cases, since reporting systems may differ between jurisdictions. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/83549
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,047 | 0,059 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,066 | 0,007 |
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 source (Gemma direct ou Codex distillé), 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 ».