Dignity Narratives in Complex MAiD Bereavement Stories: A Critical Qualitative Analysis
Bibliographic record
Abstract
A death by medical assistance in dying (MAiD) is often equated with a good death or a death with dignity, yet how MAiD-bereaved family members in Canada conceptualize the relationship between dignity and MAiD is currently unknown. Using a critical narrative inquiry approach, this article explores how family members with complex MAiD experiences constructed the concept of dignity in their bereavement stories. Dignity is conceived of as a thick, culturally relative concept with descriptive and evaluative meanings. Twelve family members from three of Canada's provinces (Alberta, British Columbia, and Ontario) completed narrative interviews about their experiences with complex MAiD bereavement.The interview transcripts are presented as short stories that portray how participants talk about dignity in relation to MAiD. These stories were analyzed from a critical narrative analysis approach that examined how institutional discourses are weaved into everyday stories about personal experience. The analysis identified three dignity narratives in participants' stories: the Dignified MAiD Narrative, the Traumatic MAiD Narrative, and the Unjust MAiD Narrative. The Dignified MAiD Narrative may provide solace to family members who agreed with their loved one's decision to choose MAiD; however, this narrative may simultaneously create moral tensions by setting unrealistic expectations for family members. The Traumatic and Unjust MAiD Narratives provide counter perspectives that challenge the notion that MAiD unequivocally leaves a legacy of a dignified, good death.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".