Improving the Medical Assistance in Dying (MAID) process: A qualitative study of family caregiver perspectives
Bibliographic record
Abstract
OBJECTIVE: The road to legalization of Medical Assistance in Dying (MAID) across Canada has largely focused on legislative details such as eligibility and establishment of regulatory clinical practice standards. Details on how to implement high-quality, person-centered MAID programs at the institutional level are lacking. This study seeks to understand what improvement opportunities exist in the delivery of the MAID process from the family caregiver perspective. METHOD: This multi-methods study design used structured surveys, focus groups, and unstructured e-mail/phone conversations to gather experiential feedback from family caregivers of patients who underwent MAID between July 2016 and June 2017 at a large academic hospital in Toronto, Canada. Data were combined and a qualitative, descriptive approach used to derive themes within family perspectives. RESULT: Improvement themes identified through the narrative data (48% response rate) were grouped in two categories: operational and experiential aspects of MAID. Operational themes included: process clarity, scheduling challenges and the 10-day period of reflection. Experiential themes included clinician objection/judgment, patient and family privacy, and bereavement resources. SIGNIFICANCE OF RESULTS: To our knowledge, this is the first time that family caregivers' perspectives on the quality of the MAID process have been explored. Although practice standards have been made available to ensure all legislated components of the MAID process are completed, detailed guidance for how to best implement patient and family centered MAID programs at the institutional level remain limited. This study provides guidance for ways in which we can enhance the quality of MAID from the perspective of family caregivers.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".