A framework for finding common ground with substitute decision-makers to achieve a patient's good death
Bibliographic record
Abstract
Conflict with substitute decision-makers of dying patients is not uncommon in end-of-life care. Understanding how to best manage these conflicts may improve relationships between physicians and substitute decision-makers, reduce stress, and ultimately improve the care of dying patients. To gain insight, we explored the experiences of Canadian Family Physicians who encountered conflict with a substitute decision-maker of a dying patient. We employed a Grounded Theory methodology with in-depth, semi-structured, audiotaped interviews about recent experiences of conflict. Purposeful sampling sought a maximum sample variation for physician participants. The verbatim transcripts, field notes and project memos were analysed using an iterative process involving the constant-comparative method to identify emerging key themes and concepts. Our study found that the physicians' main concern was for the patient to have a death free from avoidable distress, and in accord with the patient's wishes. A framework for achieving a “good death” through Finding Common Ground is described. This process involves: 1) building trust through clarifying roles, bringing key players together and delivering small bits of information at a time; 2) understanding through active listening, and finally; 3) informed shared decision-making. Preliminary findings also describe barriers to achieving Common Ground and what to do when an impasse occurs. This presentation will describe a framework for developing Common Ground between Family Physicians and substitute decision-makers to assist in achieving a “good death”. Discussion of these primary results may help physicians, allied healthcare professionals, learners, and the public, improve end-of-life decision-making.
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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 | 0.058 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.024 | 0.086 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".