This moral coil: a cross-sectional survey of Canadian medical student attitudes toward medical assistance in dying
Bibliographic record
Abstract
BACKGROUND: In February, 2015, the Supreme Court of Canada struck down the ban on medical assistance in dying (MAiD). In June, 2016, the federal government passed Bill C-14, permitting MAiD. Current medical students will be the first physician cohort to enter a system permissive of MAiD, and may help to ensure equitable access to care. This study assessed medical student views on MAiD, factors influencing these views, and opportunities for medical education. METHODS: An exploratory cross-sectional survey was developed and distributed to medical students across all years of a three-year Canadian undergraduate medical program. The investigators administered the survey to participants during academic sessions from November to December, 2015. Analysis of the results included summary descriptive statistics, Pearson's chi-square test of independence to identify differences between participants by year of study, logistic regression to identify factors that influence students' stances on MAiD, and Wilcoxon signed rank test to measure changes in student support for MAiD and comfort discussing MAiD. RESULTS: There were 405 participants for a response rate of 87%. The majority of students (88%) supported the Supreme Court's decision, 61% would provide the means for a patient to end their life, and 38% would personally administer a lethal medication. Students who were more willing to provide the means for MAiD found medical education/clinical experience and patient autonomy to be important contributors to their stances on MAiD. Those students who were less willing to provide the means for MAiD found religious/spiritual beliefs and teachings, as well as concern about potential negative consequences, to be important contributors to their stances on MAiD. Educational training desired by participants included medicolegal (91%), communication skills (80%), technical skills (75%), and religious (49%). CONCLUSIONS: Medical students generally supported and would provide the means for MAiD to patients. They also indicated a desire for directed medical education on MAiD.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".