To Lose a Loved One by Medical Assistance in Dying or by Natural Death with Palliative Care: A Mixed Methods Comparison of Grief Experiences
Bibliographic record
Abstract
The integration of assisted dying into end-of-life care is raising reflections on bereavement. Patients and families may be faced with a choice between this option and natural death assisted by palliative care; a choice that may affect grief. Therefore, this study describes and compares grief experiences of individuals who have lost a loved one by medical assistance in dying or natural death with palliative care. A mixed design was used. Sixty bereaved individuals completed two grief questionnaires. The qualitative component consisted of 16 individual semi-structured interviews. We found no statistically significant differences between medically assisted and natural deaths, and scores did not suggest grief complications. Qualitative results are nuanced: positive and negative imprints may influence grief in both contexts. Hastened and natural deaths are death circumstances that seem to generally help ease mourning. However, they can still, in interaction with other risk factors, produce difficult experiences for some family caregivers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".