Contemplating the Impacts of Canadian Healthcare Institutions That Refuse to Provide Medical Assistance in Dying: A Framework-Based Discussion of Potential Health Access Implications
Bibliographic record
Abstract
Introduction Following the historic Canadian legislation on medical assistance in dying (MAiD) in 2016, many implementation challenges and ethical quandaries have formed the focus of further scholarly investigation and policy revisions. Of these, conscientious objections held by some healthcare institutions have involved relatively less scrutiny, despite indicating possible hurdles to the universal availability of MAiD services in Canada. Methods In this paper, we contemplate potential accessibility concerns that pertain specifically to service access, with the hope to trigger further systematic research and policy analysis on this frequently overlooked aspect of MAiD implementation. We organize our discussion using two important health access frameworks: Levesque and colleagues’ Conceptual Framework for Access to Health and the Provisional Framework for MAiD System Information Needs (Canadian Institute for Health Information). Results Our discussion is organized along five framework dimensions through which institutional non-participation may generate or exacerbate inequities in MAiD utilization. Considerable overlaps are revealed across framework domains, indicating the complexity of the problem and the need for further investigation. Conclusion Conscientious dissensions on the part of healthcare institutions form a likely barrier to ethical, equitable, and patient-oriented MAiD service provision. Comprehensive, systematic evidence is urgently needed to understand the nature and scope of resulting impacts. We urge Canadian healthcare professionals, policymakers, ethicists, and legislators to attend to this crucial issue in future research and in policy discussions.
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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 | 0.040 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.033 | 0.058 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.008 | 0.006 |
| 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".