Facing our own dying: exploring conflicts between our individual professional stance and our own personal views on MAiD
Bibliographic record
Abstract
Physician-administered euthanasia (Medical-Aid-in-Dying or MAiD) has been legally available in Canada since 2016, with ever-widening indications. Most palliative care physicians in Canada do not provide MAiD themselves but will refer to colleagues who provide this procedure. The author was involved in a qualitative research project on MAiD, looking at the views of Montreal-based palliative care physicians regarding their role. One interesting finding from that project is that our own individual personal views (i.e., what I would want for myself when I will inevitably face my own death) versus my professional views as a palliative care physician (i.e., the kind of end-of-life care that I am ready to provide, or what I think patients should receive) may radically differ. We teach our trainees (and the community beyond) that dying can have meaning up to the end of one’s natural life. Patients facing terminal illness commonly express a fear of becoming a burden to others. Yet we teach that this sentiment is often not well-founded, based on the expressed views of the patient’s loved ones. And yet dying can be difficult, even when patients receive the best available palliative care. Our professional view of what constitutes a dignified end-of-life and what patients and families (and I, eventually) will experience may be different. This presentation will spark reflection regarding this dichotomy. What feelings might this inner split provoke, when our professional and personal views conflict with each other? Am I being a dishonest physician? And yet…
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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 | 0.063 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.029 | 0.049 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.005 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".