Double-edged MAiD death family legacy: a qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: Individuals who accompany a loved one through medical assistance in dying (MAiD) have to live with the experience and the psychological, moral and social consequences of their involvement in the process long after the death occurs. AIM: To explore the legacy of a MAiD death for individuals who accompanied a loved one through the process. DESIGN: Using a qualitative descriptive approach we conducted semi-structured interviews to collect data from family members who had accompanied a loved one through MAiD. Data were analysed using conventional content analysis. SETTING/PARTICIPANTS: 16 family members of 14 patients who received MAiD at a Canadian hospital with an interdisciplinary MAiD programme. RESULTS: The main theme in the analysis is the opposing tensions experienced by individuals who accompany a loved one through a MAiD death, which we conceptualise as a double-edge experience. This double-edge experience is illustrated through four thematic opposing tensions: (1) support for patient autonomy and ambivalence about the MAiD choice, (2) gratitude for suffering relieved for loved one and grief for lost time with loved one, (3) time as a gift and time as a burden and (4) positive legacy and challenging bereavement experience. CONCLUSION: The nature of the MAiD experience for involved families is rooted in complexity, ambiguity and ambivalence and thus resists easy categorisation. Families would benefit from structured psychosocial and spiritual supports that acknowledge this complexity, along with MAiD-specific bereavement support following the 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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".