Canadian Undergraduate Perspectives on Medical Assistance in Dying for Mental Illness: Does Psychiatric Illness Type, Age, and Exposure to Information Influence Acceptance of MAiD?
Bibliographic record
Abstract
Background and ObjectivesIn 2027, Canadians whose only medical condition is an untreatable mental illness and who otherwise meet all eligibility criteria will be able to request Medical Assistance in Dying (MAiD). This study investigates the attitudes of undergraduate students towards widening the scope of MAiD for physical illness for certain psychiatric conditions. We were interested in understanding if age, information, and type of mental illness influenced undergraduates' acceptance or rejection of MAiD for mental illness (MAiD-MI).Method413 undergraduate students participated in this study which examined the factors that correlate with the acceptance or rejection of MAiD-MI. Four scenarios were presented in which age (older or younger) and illness type (depression or schizophrenia) were manipulated. Demographic questions and measures assessing personality, religion, and attitudes towards euthanasia were administered. Questions assessing participants' general understanding of MAiD and their life experiences with death and suicide were also asked.ResultsMost of the participants accepted MAiD-MI for both depression and schizophrenia. As hypothesized, support for MAiD-MI was higher for patients with schizophrenia than for depression. Also as hypothesized, support was higher for older patients than for younger patients. Variables such as religion, personality and political affiliation were also associated with acceptance or rejection of MAiD-MI. Finally, consistent with our hypotheses, participants' understanding of MAiD and experiences with death and suicide was predictive of support for MAiD-MI.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 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".