Mapping MAiD Discordance: A Qualitative Analysis of the Factors Complicating MAiD Bereavement in Canada
Bibliographic record
Abstract
Medical assistance in dying (MAiD) is an evolving practice in Canada, with requests and outcomes increasing each year, and yet controversy is present-with a vast spectrum of ethical positions on its permissibility. International research indicates that family members who experience disagreement over their loved one's decision to have MAiD are less likely to be actively involved in supporting patients through the practical aspects of the dying process. Family members with passive involvement in the assisted dying process may also experience more significant moral dilemmas and challenging grief experiences than those who supported the decision. Given these previous findings, we designed this study to explore the factors complicating family members' experiences with MAiD in Canada and to understand how these complicating factors impact family members' bereavement in the months and years following MAiD. We conducted narrative interviews with 12 MAiD-bereaved family members who experienced disagreements, family conflicts, or differences in understanding about MAiD. Documenting and analyzing participants' experiences through storytelling allowed us to appreciate the complexity of family members' experiences and understand their values. The analysis generated five factors that can complicate the MAiD process and bereavement for family members: family discordance, internal conflict, legislative and eligibility concerns, logistical challenges, and managing disclosure and negative reactions. To our knowledge, this is the first Canadian study that explores how family discordance can impact bereavement following MAiD. Future bereavement services and resources should consider how these complicating factors may impact bereavement and ensure that Canadians with diverse MAiD experiences can access appropriate support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".