A qualitative study of physicians’ conscientious objections to medical aid in dying
Bibliographic record
Abstract
BACKGROUND: Under Quebec's Act respecting end-of-life care, physicians may refuse to provide medical aid in dying because of personal convictions, also called conscientious objections. Before legalisation, the results of our survey showed that the majority of physicians were in favour of medical aid in dying (76%), but one-third (28%) were not prepared to perform it. After 18 months of legalisation, physicians were refusing far more frequently than the pre-Act survey had anticipated. AIM: To explore the conscientious objections stated by physicians so as to understand why some of them refuse to get involved in their patients' medical aid in dying requests. DESIGN/PARTICIPANTS: An exploratory qualitative study based on semi-structured interviews with 22 physicians who expressed a refusal after they received a request for medical aid in dying. Thematic descriptive analysis was used to analyse physicians' motives for their conscientious objections and the reasons behind it. RESULTS: The majority of physicians who refused to participate did not oppose medical aid in dying. The reason most often cited is not based on moral and religious grounds. Rather, the emotional burden related to this act and the fear of psychological repercussions were the most expressed motivations for not participating in medical aid in dying. CONCLUSION: The originality of this research is based on what the actual perception is of doing medical aid in dying as opposed to merely a conceptual assent. Further explorations are required in order to support policy decisions such as access to better emotional supports for providers and interdisciplinary support.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".