Bibliographic record
Abstract
Background: Legally practiced assisted dying is an ethically complex area in need of empirical and conceptual work. International research suggests that providing assisted dying may be experienced as rewarding and meaningful but also emotionally and psychologically taxing, associated with feelings of loss and loneliness. Yet little research has been published to date, which attends to the long-term effects of providing assisted dying. In this article, I contribute to filling this gap in the literature using the Canadian province Quebec as an illustrative case. Medical aid in dying (MAiD) in the form of physician provided euthanasia has been a lawful end of life healthcare option in Quebec since December 2015 and significant research is currently emerging from this jurisdiction. Methods: In this article, I draw on nine in-depth interviews with Quebec physicians, all of whom engaged with end of life care in different ways. Results: Four of the interviewed physicians provided medical aid in dying (MAiD) and five did not. The major themes of MAiD in relation to aggressive treatment, conscientious objection and uneven distribution of work emerge, and it appeared clearly that MAiD was experienced and thought of as qualitatively different to other end of life procedures. Conclusions: Our findings expose a complexity and contentiousness within the practice, which remains under researched and underreported and indicate avenues where more research is needed.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".