When medical assistance in dying is not a last resort option: survey of the Canadian public
Bibliographic record
Abstract
OBJECTIVES: What are the Canadian public's understanding of and views toward medical assistance in dying (MAID) in persons refusing recommended treatment or lacking access to standard treatment or resources? DESIGN/SETTING: An online survey assessed knowledge of and support for Canadian MAID law, and views about four specific scenarios in a two (medical or psychiatric) by two (treatment refusal or lack of access) design. PARTICIPANTS: A quota sample (N=2140) matched to the 2021 Canadian census by age, gender, income, education and province. MAIN OUTCOMES: Participants' level of support for MAID in general and in the four specific scenarios. RESULTS: Only 12.1% correctly answered ≥4 of 5 knowledge questions about the MAID law; only 19.2% knew terminal illness is not required and 20.2% knew treatment refusal is compatible with eligibility. 73.3% of participants expressed support for the MAID law in general, matching a nationally representative poll that used the same question. 40.4% of respondents supported MAID for mental illnesses. Support for MAID in the scenarios depicting refusal or lack of access to treatment ranged from 23.2% (lack of access in medical condition) to 32.0% (treatment refusal in medical illness). Older age, more education, higher income, lower religious attendance or being white was associated with greater support for MAID in general but was either negatively associated or not associated with support for MAID in the four refusal or lack of access scenarios. CONCLUSIONS: Most Canadians support the current MAID law but appear unaware that MAID cases they do not support are compatible with that law. The lower support for MAID in the four scenarios cuts across sociodemographics. The gap between current policy and public opinion warrants further study. For jurisdictions debating MAID, opinion surveys may need to go beyond assessing general attitudes, and target knowledge and views regarding implications of legalisation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| 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".