Understanding the Factors Explaining the Growing Use of Medical Assistance in Dying in Québec: Protocol for an Interdisciplinary Mixed Methods and Multimethods Study (Preprint)
Bibliographic record
Abstract
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
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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 | 0.047 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.007 |
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".