Canadian family members’ experiences with guilt, judgment and secrecy during medical assistance in dying: a qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: Although research briefly mentions that family members have encountered unexpected experiences during the medical assistance in dying (MAiD) process, from keeping MAiD a secret, to being judged and feeling guilty, the potential implications of these are less understood. This study's aim was to examine guilt, judgment and secrecy as part of the MAiD experiences of family members in Canada. METHODS: We conducted a qualitative descriptive study with 1-hour semistructured interviews by telephone or video from December 2020 to December 2021. Through local and national organizations, we recruited Canadian family members with MAiD experience. A subset analysis of unexpected experiences was conducted, which identified 3 categories: guilt, judgment and secrecy. Similar codes were grouped together within each category into themes. Participants were sent the draft manuscript and their suggestions were integrated. RESULTS: A total of 45 family members from 6 provinces who experienced MAiD from 2016 to 2021 participated. Many people who had MAiD were diagnosed with cancer, comorbidities or neurologic disease. Some participants unexpectedly found themselves managing guilt, judgment and/or secrecy, which may complicate their grieving and bereavement. Numerous participants experienced judgment from relatives, friends, religious people and/or health care professionals. Many kept MAiD secret because they were not allowed to tell or for religious reasons, and/or selectively told others. INTERPRETATION: Family members said they were ill-prepared to manage their experiences of guilt, judgment and secrecy during the MAiD process. MAiD programs and assessors/providers could provide family-specific information to help lessen these burdens and better prepare relatives for common, yet unexpected, experiences they may encounter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".