Impact of medical assistance in dying (MAiD) on family caregivers
Bibliographic record
Abstract
Medical assistance in dying (MAiD) is a globally polarising topic which often sparks debate surrounding the ethical and moral dilemmas that arise with a life-ending intervention. To gain a better understanding of this intervention, it is important to explore the experience of those most intimately affected by MAiD. Family caregivers of those with a terminal illness are the backbone of the healthcare and support team, often providing a substantial amount of informal care while at the same time coping with their own distress and anticipatory grief. However, we know the least about how MAiD impacts the psychosocial well-being of these same individuals. The aim of this article is to explore the experience of MAiD from the family caregiver perspective, namely their beliefs and opinions about the intervention, how the process of MAiD impacts them, how the intervention shapes their view of their loved one's quality of death, and the psychosocial outcomes after the passing of their loved one. Beyond the literature, challenges within both the clinical and research realms will be discussed and future directions will be offered. While MAiD is currently legal in only a small number of countries, a better understanding of the impact of MAiD will help inform policy and legislation as they are developed in other jurisdictions. Further, this article aims to inform future research and clinical interventions in order to better understand and support those seeking MAiD and their families.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".