Therapeutic Conversations with Seriously Ill People and Their Families
Bibliographic record
Abstract
Seriously ill people and those they love encounter health care professionals regularly. Published studies, representing how seriously ill people prefer to be communicated with suggest they would like open, honest and thoughtful communication. Additionally, these studies emphasize that seriously ill people prefer to talk about their illness when they are ready. Medical professionals are often on the frontline of communication with these people. There is a paucity of education on communicating with seriously ill people in professional education spanning many health care professions.Our workshop will empower participants in all capacities to better communicate with seriously ill people. We will teach not how to communicate information but rather how to have a therapeutic interaction that is consistent with what we know to be true about what seriously ill people value in their communication with their health care team.We will use patient narratives both oral and video, role play and reflection to convey an easy to implement framework to therapeutic communication.Session attendees will be able to1. Understand foundational communication desires of seriously ill people and their families.2. Describe a framework to approach difficult conversations with a therapeutic intention.3. Implement practical approaches to enhance their communication with seriously ill patients and families they encounter daily.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".