Access Isn’t Enough: Evaluating the Quality of a Hospital Medical Assistance in Dying Program
Bibliographic record
Abstract
Following an initial study of the needs of healthcare providers (HCP) regarding the introduction of Medical Assistance in Dying (MAiD), and the subsequent development of an assisted dying program, this study sought to determine the efficacy and impact of MAiD services following the first two years of implementation. The first of three aims of this research was to understand if the needs, concerns and hopes of stakeholders related to patient requests for MAiD were addressed appropriately. Assessing how HCPs and families perceived the quality of MAiD services, and determining if the program successfully accommodated the diverse needs and perspectives of HCPs, rounded out this quality evaluation. This research implemented a mixed-methods design incorporative of an online survey with Likert scale and open-ended questions, as well as focus groups and interviews with staff and physicians, and interviews with MAiD-involved family members. There were 356 online surveys, as well as 39 participants in six focus groups with HCP, as well as fourteen interviews with MAiD-involved family members. Participants indicated that high-quality MAiD care could only be provided with enabling resources such as policies and guidelines to ensure safe, evidence-based, standardized care, as well as a specialized, trained MAiD team. Both focus group and survey data from HCPs suggest the infrastructure developed by the hospital was effective in delivering high-quality MAiD care that supports the diverse needs of various stakeholders. This study may serve as a model for evaluating the impact and quality of services when novel and ethically-contentious clinical practices are introduced to healthcare organizations.
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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 | 0.070 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 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".