What’s suffering got to do with it? A qualitative study of suffering in the context of Medical Assistance in Dying (MAID)
Bibliographic record
Abstract
Abstract Background Intolerable suffering is a common eligibility requirement for persons requesting assisted death, and although suffering has received philosophic attention for millennia, only recently has it been the focus of empirical inquiry. Robust theoretical knowledge about suffering is critically important as modern healthcare provides persons with different options at end-of-life to relieve suffering. The purpose of this paper is to present findings specific to the understanding and application of suffering in the context of MAID from nurses’ perspectives. Methods A longitudinal qualitative descriptive study using semi-structured telephone interviews. Inductive analysis was used to construct a thematic account. The study received ethical approval and all participants provided written consent. Results Fifty nurses and nurse practitioners from across Canada were interviewed. Participants described the suffering of dying and provided insights into the difficulties of treating existential suffering and the iatrogenic suffering patients experienced from long contact with the healthcare system. They shared perceptions of the suffering that leads to a request for MAID that included the unknown of dying, a desire for predictability, and the loss of dignity. Eliciting the suffering story was an essential part of nursing practice. Knowledge of the story allowed participants to find the balance between believing that suffering is whatever the persons says it is, while making sure that the MAID procedure was for the right person, for the right reason, at the right time. Participants perceived that the MAID process itself caused suffering that resulted from the complexity of decision-making, the chances of being deemed ineligible, and the heighted work of the tasks of dying. Conclusions Healthcare providers involved in MAID must be critically reflective about the suffering histories they bring to the clinical encounter, particularly iatrogenic suffering. Further, eliciting the suffering stories of persons requesting MAID requires a high degree of skill; those involved in the assessment process must have the time and competency to do this important role well. The nature of suffering that patients and family encounter as they enter the contemplation, assessment, and provision of MAID requires further research to understand it better and develop best practices.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".