Medical Assistance in Dying (MAiD): the opinions of medical trainees in Newfoundland and Labrador. A cross-sectional study.
Bibliographic record
Abstract
Background: Medical Assistance in Dying (MAiD) was legalized in Canada in 2016. As future physicians, medical trainees will face decisions regarding MAiD. Although many publications exist internationally, Canadian data is limited in the peer-reviewed literature. The purpose of this study is to determine the opinions of medical trainees in Newfoundland and Labrador regarding MAiD, and the factors that impact these views. Methods: A survey was distributed to all medical trainees at Memorial University (N=570). The survey collected demographic information and opinions regarding MAiD. Respondents were divided into groups based on demographic characteristics, and their responses analyzed using non-parametric statistics. Results: The survey was completed by 124 trainees. Ninety percent of respondents agreed with the legalization of MAiD in Canada and nearly 60% stated they would perform the procedure for their patients. Several factors influenced the opinions of medical trainees, including level of training and religious affiliation. Trainees also favored detachment from the MAiD process. Interpretation: Canadian medical trainees are largely in favor of MAiD, which will likely be requested more frequently in the future. This highlights the importance of emphasizing MAiD within medical curricula, so that trainees are adequately informed and prepared to handle this new aspect of medical care upon joining independent practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".