E-Survey of Stressors and Protective Factors in Practicing Medical Assistance in Dying
Bibliographic record
Abstract
Objective: To better identify, quantify, and understand the current stressors and protective factors reported by Canadian medical assistance in dying (MAiD) assessors and providers to inform policy, education, and supports. Methods: E-survey of MAiD stressors ( n = 33) and protective factors ( n = 27); resilience measurement and comments relating to practice involving physicians and nurse practitioners who provide MAiD services and belong to the Canadian Association of MAiD Assessors and Providers or a francophone equivalent. The survey was conducted, while Parliament was considering changes to MAiD eligibility criteria, which occurred during COVID-19 pandemic restrictions. Results: In total, there were 131 respondents (response rate 35.8%). Two possible changes to future eligibility (mental disorders as the sole reason for MAiD and mature minors) were highly scored as were extra clinical load and patients' family conflict over MAiD. Twenty percent of respondents considered stopping MAiD work. The CD Resilience Scale-2 mean score was 6.90. Highly scored protective factors included compassionate care, relief of suffering, patient autonomy, patient gratitude, feelings of honor, privilege, and professionally satisfying work. Discussion: The identified stressors and reasons for considering stopping MAiD work indicate needs for policy, education, and supports to be optimized or developed. Respondents showed high resilience and highly scored protective factors, which should be optimized. This survey should be repeated in countries where MAiD is legal to determine stressors and protective factors in MAiD practice, stressors addressed, and protective factors enhanced where feasible in the local context for optimal care.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".