Quality of Bereavement for Caregivers of Patients Who Died by Medical Assistance in Dying at Home and the Factors Impacting Their Experience: A Qualitative Study
Bibliographic record
Abstract
Background: Medical Assistance in Dying (MAiD) was legalized in Canada in June 2016. MAiD is available to those who are at least 18 years of age with an irremediable medical condition and an irreversible state of decline causing unbearable suffering. Between June 2016 and December 2019, 13,946 MAiD cases were reported in Canada.3 Although 35.2% have taken place in the home, very little is known about the experience of caregivers in this setting. Objectives: This study explored caregivers' experience with MAiD in the home-setting and their bereavement process. Setting/Subjects: Caregivers of patients of the Temmy Latner Centre for Palliative Care in Toronto, Canada, who underwent MAiD by a physician at home. This study was approved by the Sinai Health Research Ethics Board. Design: This study used a semistructured interview guide and standardized questionnaires. Thirteen caregivers were contacted at least six months post-MAiD to participate in a one-on-one interview. The interviews were transcribed, coded, and evaluated using a thematic analysis approach. Results: The main themes that emerged from the interviews were the caregivers' experience with MAiD, their interaction with the MAiD team, disclosure about MAiD, their bereavement experience, and comparison of experiencing a MAiD death to a natural death. Conclusion: We hypothesize that caregivers in our study were better prepared for the upcoming death due to more certainty as to how and when their loved one would die. Having closure and being able to say goodbye may also have positively influenced the bereavement experience. Finally, MAiD may have spared the caregivers the trauma of witnessing their loved one deteriorate in their final days of life.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".