Reflections on the relational ontology of medical assistance in dying
Bibliographic record
Abstract
Canadian nursing practice has been profoundly influenced by the legalization of medical assistance in dying in 2016, requiring that nurses navigate new and sometimes highly challenging experiences. Findings from our longitudinal studies of nurses' experiences suggest that these include deep emotional responses to medical assistance in dying, an urgency in orchestrating the perfect death, and a high degree of relational impact, both professionally and personally. Here we propose a theoretical explanation for these experiences based upon a relational ontology. Drawing upon the work of Wildman, we understand a relational ontology to be one in which relationships are more fundamentally central than the conceptual entities that provide the context to practice. It is in a relationship that conceptual entities, and their affiliated values, are created and recreated. Seen as causal, relationships have ontological status, with important implications for how we consider the concepts of death, suffering, and time in this context. From a conceptual perspective, suffering is primarily self-defined based upon personal histories, time reflects the potential remaining until death, and death is primarily biological and amoral, although social discourses of a good and bad death surround the death trajectory. However, within a relational ontology of medical assistance in dying, these understandings shift. Death becomes primarily social rather than biological, suffering is shared, and time until death is now clearly delimited. Accordingly, nurses assume a profound responsibility for influencing outcomes that are authentically person-centered. These understandings provide important insights into nurses' experiences, enabling us to recognize the causal effects, both intended and unintended, of nurses' relational practices amidst the complexities of assisted death. Drawing on such a perspective, we find implications for how we provide spaces for nurses to reflect on, and have conversations about, their experiences with some of the greatest mysteries of life-death, suffering, and time.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.108 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| 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".