The moral web of accessibility to medical assistance in dying: Reflections from the Canadian context
Bibliographic record
Abstract
In this paper, we reflect on factors that seem to have influenced the accessibility of medical assistance in dying (MAID) in the Canadian context. Since legalization in 2016, the uptake of MAID has increased rapidly to equal or exceed rates in other countries. In that MAID implementation involves numerous ethical/moral complexities, we consider four factors that appear to have influenced this growth. First, we reflect on the vague language contained within the legislation that has been interpreted by a community of practice in which making MAID accessible is an important priority. Second, we consider policies of effective referral and self-referral that have been strategies for enhancing accessibility in relation to a wider context that contains conscientious objection. Third, we examine the apparent impact of centralized clinical teams and coordination services that have enhanced accessibility for persons residing in rural and remote areas. Fourth, we reflect on ways in which public awareness of MAID has been enhanced through policies that enable healthcare providers to introduce the topic of MAID as an option within advance care planning. We conclude with a consideration of how these intersecting factors may be shaping the moral complexity inherent in the idea of making MAID accessible.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.072 | 0.060 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".