Causes for Conscientious Objection in Medical Aid in Dying: A Scoping Review
Bibliographic record
Abstract
In the light of current legislation on Medical Aid in Dying (MAiD; also known as euthanasia and assisted suicide) in different countries worldwide, there have been some arguments devoted to the right to conscientious objection for healthcare professionals in these specific practices. The goals of this scoping review are to provide an overview of the motivations and causes that lie behind conscientious objection identified by previous literature according to professionals’ experiences and to verify if these motives match with theoretical debates on conscientious objection. As the results show, there is a dissonance between the motivations included in the traditional and mainstream definition of conscientious objection used in theoretical and speculative frameworks and the actual factors that empirical studies note as reported motivations to object to MAiD. Hence, either we consider new factors to include as causes of “conscience”, or we accept that there are motivations that are not actually applicable to conscientious objection and should be addressed by other means. As conscientious objection to MAiD is multifaceted, there can be different kinds of motivations acting at the same time. It is thus pertinent to rebalance theoretical and empirical considerations to fully understand the complexity of the phenomenon and so provide insights on how to best deal with conscientious objection.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".