Shaping Experiences: Exploring the Impact of Legislation, Policy, and Programs on Family Members of MAID Recipients
Bibliographic record
Abstract
Published literature on family members’ experiences with assisted dying is minimal, with only a limited number of studies exploring the perspectives of bereaved family members. Studies have shown family members can play a significant role in assisted dying. My study aimed to understand the experiences of bereaved family members who have had a loved one receive medical assistance in dying (MAID) and describe how MAID implementation, policy, and processes in two different settings in Canada influenced these experiences. The study used interpretive description, a qualitative research methodology framed by the theoretical lens of relational ethics. A total of 31 family members and 15 key informant participants took part in the study. The analysis of the data identified three descriptive themes: (a) they want MAID, now what, (b) prepared but maybe not ready, and (c) evolving understanding of this type of death, with associated subthemes that revealed the complex and layered experience of family members whose loved one received MAID. Study findings also revealed the experience of family members was influenced not only by individual-level factors but also by meso-level factors, including programs, processes, policy, and macro-level elements, including MAID legislation. These elements did not operate in isolation; instead, they interconnected to influence family members. Based on study findings, recommendations focus on policy, practice, and education, as well as future research and propose options to address the elements that affect the experiences of bereaved family members of MAID recipients.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".