P.137 A qualitative study of families’ experiences with medical assistance in dying (MAiD) in Nova Scotia
Bibliographic record
Abstract
Background: MAiD became legal in Canada in 2015, with Bill C-14 delineating eligibility criteria and access. Previous research found families are intimately involved with decision-making, with conflicting perspectives on how they cope. Our study sought to learn about the experiences of family members, and determine what supports might be beneficial to improve MAiD delivery and aftercare. Methods: We conducted hour-long semi-structured interviews with 20 family members of individuals who had MAiD. Interviews took place by telephone or virtually via MS Teams, and transcripts were analyzed using an iterative coding process and thematic analysis. Results: Prominent themes emphasized the importance of respecting autonomy, decision-making, and allowing people to regain a sense of control, particularly with so much taken away. The death itself was described as peaceful. Interviewees were overwhelmingly filled with relief and gratitude for being able to respect the individual’s wishes. Interviewees spoke of importance of support for themselves, and the desire to build a network of individuals with similar experiences; to share their stories, grieve together, and support the next generation. Conclusions: These results will help improve MAiD delivery and aftercare in Nova Scotia, by informing, developing and enabling access to resources for individuals who accompany a family member on their end-of-life journey.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".