Putting a Bow on Death and Dying\nHealth Care Professionals’ Experiences with Medical Assistance in Dying (MAiD)\nA Foucauldian Discourse Analysis with Agambian Insights
Bibliographic record
Abstract
This paper employs a Foucauldian Discourse Analysis perspective to enrich the\nunderstanding of the experiences that health care professionals in Ontario, Canada have with medical assistance in dying. Interview data is analyzed by situating the health care professional as an effect, as a producer, and as a challenger of power-knowledge systems. Philosophical theories of Giorgio Agamben are applied to the data to challenge Foucauldian principles, and to\nbolster the discussion of defining of the body that deserves to live, and the body that deserves to die.\n\nMajor findings that emerged include the dominant discourse of aligning right and good within confines of the law, and the absolution of quantification and generalizability in relation to definitions surrounding dying. In terms of next steps for social work practice, this paper concludes by asking social workers to interrogate why we feel the need to ‘put a bow on death and dying’, so that we may engage in critical conversations with our colleagues.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.045 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".