Access Isn’t Enough: Evaluating the Quality of a Hospital Medical Assistance in Dying Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it