Professional experiences of formal healthcare providers in the provision of medical assistance in dying (MAiD): A scoping review
Bibliographic record
Abstract
OBJECTIVE: This scoping review describes the existing literature which examines the breadth of healthcare providers' (HCP's) experiences with the provision of medical assistance in dying (MAiD). METHOD: This study employed a scoping review methodology: (1) identify research articles, (2) identify relevant studies, (3) select studies based on inclusion/exclusion criteria, (4) chart the data, and (5) summarize the results. RESULTS: = 12). This scoping review found that HCPs experienced a variety of emotional responses to providing or providing support to MAiD. Some HCPs experienced positive emotions through helping patients at the end of the patient's life. Still other HCPs experienced very intense and negative emotions such as immense internal moral conflict. HCPs from various professions were involved in various aspects of MAiD provision such as responding to initial requests for MAiD, supporting patients and families, nursing support during MAiD, and the administration of medications to end of life. SIGNIFICANCE OF RESULTS: This review consolidates many of the experiences of HCPs in relation to the provision of MAiD. Specifically, this review elucidates many of the emotions that HCPs experience through participation in MAiD. In addition to describing the emotional experiences, this review highlights some of the roles that HCPs participate in with relation to MAiD. Finally, this review accentuates the importance of team supports and self-care for all team members in the provision of MAiD regardless of their degree of involvement.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".