Simulated Learning Experience in Advance Care Planning Conversations
Bibliographic record
Abstract
The Centers for Disease Control and Prevention reports that one-third of all Americans have not done any advance care planning, which is essential for maintaining autonomy when people are no longer able to communicate wishes directly at the end of life. The Institute of Medicine reports that providers have limited training in communication techniques and low confidence in holding advance care planning conversations. Forty-four family nurse practitioner students received classroom communication training using evidence-based end-of-life nursing education consortium materials and role-play opportunities. One month later, students participated in a palliative care simulation experience in a high-fidelity simulated medical office environment with standardized patient. The family practice nurse practitioner students were required to communicate bad news of a poor diagnosis to a family member and begin the advance care planning conversation using the SPIKES protocol. Findings include a significant increase in postintervention advance care planning communication knowledge scores and self-confidence in holding these conversations (mean, 30.55 [SD, 2.992]; 95% confidence interval, 29.64-31.46; median, 31; range, 11-35) and satisfaction with the learning experience (mean, 18.14 [SD, 2.11]; 95% confidence interval, 17.50-18.78; median, 19; with range between 13 and 20).
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".