Conflict resolution processes in end-of-life care disputes between families and healthcare providers in Canada.
Bibliographic record
Abstract
Conflict at the end-of-life, particularly between families and health-care providers, involves many complex factors; differing opinions surrounding a patient’s prognosis, cultural differences, moral values, and religious beliefs, associated costs, internal family dynamics, and of course, legal ramifications. Legislative reform at both the provincial and federal levels with respect to assisted dying has had far-reaching implications for healthcare decisionmaking for families, healthcare providers, religious groups, and others. These reforms provide the backdrop for this paper, which examines the conflict resolution processes that can provide a solution amidst an often stressful, costly, and time-consuming ordeal. This paper reviews several processes, but focuses on the Ontario Consent and Capacity Board. In addition, this paper discusses the importance of empathy and cultural understanding in the face of cross-cultural conflict in end-of-life decision-making processes.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.046 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".