Canadian Undergraduates’ Perspectives on Medical Assistance in Dying (MAiD): A Quantitative Study
Bibliographic record
Abstract
Background and Objectives: In 2016, Medical Assistance in Dying (MAiD) became legal in Canada for those suffering a grievous and untreatable medical condition. Currently, it is not available to minors or to those with an untreatable mental illness, although it is likely the scope of MAiD will be widened to include persons with severe and untreatable mental illnesses. However, little is known about the factors predicting acceptance or rejection of MAiD for persons with either a grievous medical condition or an untreatable mental illness. Methods: A survey was administered to 438 undergraduate students to examine factors associated with their acceptance or rejection of MAiD. The survey included four different scenarios: a young or old person with an untreatable medical condition, and a young or old person with an untreatable mental illness. Demographic questions (age, sex, religion, etc), personality measures, and an attitude towards euthanasia scale were also administered, as well as questions assessing participants’ general understanding of MAiD and their life experiences with death and suicide. Results/Conclusion: Overall, most of the Canadian undergraduate participants accepted MAiD for both terminally ill and mentally ill patients; however, different variables, such as age, religion, and ethnicity, predicted the acceptance or rejection of MAiD for each scenario.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".