Complex MAiD Bereavement Stories: A Critical Narrative Analysis of Family Members’ Discordance, Pain, Dignity, and Wisdom
Bibliographic record
Abstract
Medical assistance in dying (MAiD) is becoming a legal option in an increasing number of jurisdictions across the world. Scholarship from Canada, where MAiD was legalized in 2016, and elsewhere suggests that family members are often actively involved in helping patients through the medical and legal processes required to access this life-ending intervention. However, there is a dearth of research investigating the factors that may complicate family members’ bereavement experiences following MAiD. The current study was designed to investigate this gap in the literature and was constructed using a critical narrative inquiry approach grounded in poststructural epistemology. Narrative interviews were conducted with 12 MAiD-bereaved family members and close friends in three provinces (Alberta, British Columbia, and Ontario) who had some disagreement, family conflict, or difference in understanding about MAiD. The Dissertation includes three research articles and a knowledge translation project. The first chapter introduces the project and reviews the relevant literature. Chapter Two explores the factors complicating family members’ experiences with MAiD in Canada. Chapter Three investigates the role of physical and emotional pain in family members’ MAiD bereavement stories, the role of disenfranchised grief, and how care can be improved for those with complex MAiD experiences. Chapter Four features three short stories that deconstruct how family members draw upon three distinct dignity narratives in their MAiD bereavement stories. Chapter Five details a knowledge translation project that shares participants’ wisdom gained through their experiences. This wisdom is represented through the construction of a quilt. The dissertation closes with a discussion of the project’s substantive, methodological, and theoretical contributions.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.022 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".