Implementation of Medical Assistance in Dying: An evaluation of clinician knowledge and perceptions at a large urban multi-site rehabilitation centre in Toronto
Bibliographic record
Abstract
Objective: Evaluate clinician knowledge and perceptions to ensure the newly implemented Medical Assistance in Dying (MAID) policies and guidelines are understood correctly to help facilitate the best possible experience for patients and their families.Methods: One hundred and twenty-five clinicians completed a survey, developed for this study, to determine their perceptions and knowledge base related to MAID.Results: The average grade on the knowledge-based portion of the survey was 69%. On average, respondents displayed a good understanding (84%) of the legislated eligibility criteria while room for improvement was noted for facility specific policy questions (65%) and general principle questions (71%). Analysis of perception-based questions indicated most respondents were in support of MAID, however, they expressed mixed feelings towards the ease of having MAID related conversations. Respondents expressed mixed opinions in relation to whether the facility was providing adequate training to staff. Sixty-four percent of respondents expressed interest in receiving further training relating to MAID.Conclusions: Education for healthcare providers to ensure they understand the relevant hospital policy and guidelines is critical to improve compliance with the implementation of MAID. It is important to continue to understand and support the perceptions of clinicians to ensure that MAID is administered correctly and as effectively as possible.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".