Public interest in medical assistance in dying and palliative care
Bibliographic record
Abstract
OBJECTIVES: Medical assistance in dying (MAiD) is legal in an increasing number of countries, but there are concerns that its availability may compromise access to palliative care. We assessed public interest in MAiD, palliative care, both, or neither, and examined characteristics associated with this interest. METHODS: We surveyed a representative sample of the adult Canadian public, accessed through a panel from May to June 2019. Weighted generalised multinomial logistic regression analyses were used to determine characteristics associated with interest in referral to palliative care, MAiD, or both, in the event of diagnosis with a serious illness. RESULTS: Of 1362 participants who had heard of palliative care, 611 (44.8% weighted (95% CI 42.1% to 47.5%)) would be interested in both MAiD and palliative care, 322 (23.9% (95% CI 21.5% to 26.2%)) palliative care alone, 171 (12.3% (95% CI 10.5% to 14.1%)) MAiD alone and 258 (19.0% (95% CI 16.9% to 21.2%)) neither. In weighted multinomial logistic regression analyses, interest in both MAiD and palliative care (compared with neither) was associated with better knowledge of the definition of palliative care, older age, female gender, higher education and less religiosity; interest in palliative care alone was associated with better knowledge of the definition of palliative care, older age, female gender and being married/common law; interest in MAiD alone was associated with less religiosity (all p<0.05). CONCLUSIONS: There is substantial public interest in potential referral to both MAiD and palliative care. Simultaneous availability of palliative care should be ensured in jurisdictions where MAiD is legal, and education about palliative care should be a public health priority.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".