Cultivating Compassion: The Practice Experience of a Medical Assistance in Dying Coordinator in Canada
Bibliographic record
Abstract
Accessing medical assistance in dying (MAiD) became legal in Canada in June, 2016. This marks a unique time in our history, as eligible persons can now opt for an assisted death and health care professionals can be involved without criminal repercussion. I used an autoethnographic approach to explore and describe my experience of implementing and coordinating a new MAiD program in a local health authority. Part I is a self-reflexive narrative based on journal entries about my immersion in this practice role over a 6 month period. In Part II, I share five emergent storylines: coming to the role (the calling), embodiment (becoming the face of), immersion in clinical practice, interactions with those seeking MAiD, and self survival (sense making). The created story and storylines shine a light on new ethical practice realities, enhance understanding about MAiD as it continues to unfold, and hopefully inspire human centered, compassionate care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.047 | 0.024 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| 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".