Disparities in public awareness, practitioner availability, and institutional support contribute to differential rates of MAiD utilization: a natural experiment comparing California and Canada
Bibliographic record
Abstract
Even though California and Canada both legalised medical aid in dying (MAiD) in 2016 and have similarly sized populations, only 853 medically assisted deaths occurred in California versus 13,241 in Canada in 2022, the most recently reported year.Ten testable hypotheses were proposed to explain this 15-fold differential in MAiD utilisation.A demographically representative online survey of adults 60 and older in both jurisdictions (N = 556) revealed no differences in moral acceptance of MAiD or willingness to use it.However, only 25% of Californian participants were aware that MAiD was legally available versus 67% of Canadian participants.Evidence in the public domain revealed that there were 6.0 times more MAiD practitioners per capita in Canada than in California, and there was far greater support for MAiD by Canadian healthcare institutions.The evidence did not support hypotheses presuming more restrictive laws in California or greater access to palliative care/hospice.While other reasons may contribute to the difference in MAiD utilisation, limited public awareness, fewer MAiD practitioners per capita, and sparse support by healthcare institutions may significantly reduce California residents' ability to exercise their autonomy when making end of life choices.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".