Grief and bereavement of family and friends around medical assistance in dying: scoping review
Bibliographic record
Abstract
OBJECTIVES: The increase in the number of jurisdictions legalising medical assistance in dying (MAiD) has contributed to a growth in the number of family and friends who may face unique elements of grief and bereavement. The aim of this study was to review the literature of grief and bereavement of family and friends following MAiD, and to summarise findings for the development of community resources and programming. METHODS: We performed a scoping review with workshop consultation of stakeholders. Six electronic databases and the grey literature were searched for qualitative, quantitative and review articles. Content-analytical techniques and multidisciplinary discussions led to the development of concepts and a conceptual framework. RESULTS: Twenty-eight articles met the inclusion criteria. We identified five concepts that impact the grief and bereavement of family/friends: relationships between family/friends and the patient as well as healthcare providers; aspects of MAiD grief which can include secrecy and/or anticipatory grief; preparations which may include family/friends and should be centralised and harmonised; end of life as an opportunity for ceremony; and the aftereffects during which mental health outcomes are studied. CONCLUSION: This multidisciplinary scoping review incorporates stakeholder consultation to find that support is needed to address the complicated and changing emotions of family/friends before, during and after a MAiD death. Furthermore, additional societal normalisation of MAiD is necessary to reduce secrecy and stigma and improve the accessibility of resources for family/friends.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 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".