The Impact of Institutional Abstinence from Medical Assistance in Dying (MAiD): A Qualitative Study Protocol For Understanding Patient Transfer Journeys (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Many healthcare institutions in Canada currently decline to provide medical assistance in dying (MAID) on-site for religious or moral reasons. If patients receiving medical care at such institutions request MAiD, they need to be transferred elsewhere in order to access this important medical service and Charter-protected legal right. The process places additional burdens—in terms of seeking information and support, navigating the health system, and communicating with health professionals—on vulnerable patients who are already battling grievous illness and psychological distress. While anecdotes relating to the issue occasionally appear in the press, there is very little systematic evidence on the experiences of patients and families who make this critical transition in care. This study aims to fill this vital knowledge gap by mapping the MAiD access trajectories of patients and families at MAiD-abstaining institutions. Through a combination of textual analysis (media sources and institutional grey literature) and in-depth interviews (with patients, families, policymakers, and health professionals), this project will generate evidence on how patients and families interact with healthcare institutions and professionals; where they seek information; who they turn to for support and decision-making; what alternative routes they choose when faced with barriers; and where key resources to support their transfer journeys are located. Utilizing the Patient Engagement Framework suggested by the Strategy for Patient-Oriented Research (SPOR), this study will involve patients and families—across all stages of its research design—for: (i) informing the socio-ethical debate on institutional MAiD-abstinence; (ii) identifying gaps in MAiD service delivery, and (iii) making policy recommendations for equitable access to 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 | 0.074 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.035 | 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".