Impact of Medical Assistance in Dying on palliative care: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Medical Assistance in Dying comprises interventions that can be provided by medical practitioners to cause death of a person at their request if they meet predefined criteria. In June 2016, Medical Assistance in Dying became legal in Canada, sparking intense debate in the palliative care community. AIM: This study aims to explore the experience of frontline palliative care providers about the impact of Medical Assistance in Dying on palliative care practice. DESIGN: Qualitative descriptive design using semi-structured interviews and thematic analysis. SETTINGS/PARTICIPANTS: We interviewed palliative care physicians and nurses who practiced in settings where patients could access Medical Assistance in Dying for at least 6 months before and after its legalization. Purposeful sampling was used to recruit participants with diverse personal views and experiences with assisted death. Conceptual saturation was achieved after interviewing 23 palliative care providers (13 physicians and 10 nurses) in Southern Ontario. RESULTS: Themes identified included a new dying experience with assisted death; challenges with symptom control; challenges with communication; impact on palliative care providers personally and on their relationships with patients; and consumption of palliative care resources to support assisted death. CONCLUSION: Medical Assistance in Dying has had a profound impact on palliative care providers and their practice. Communication training with access to resources for ethical decision-making and a review of legislation may help address new challenges. Further research is needed to understand palliative care provider distress around Medical Assistance in Dying, and additional resources are necessary to support palliative care delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".