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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".