Challenging Conversations in Healthcare: Simulation-Based Interprofessional Learning
Bibliographic record
Abstract
Introduction: Interprofessional education is central to the mission of the Institute for Professionalism and Ethical Practice, based at Boston Children's Hospital and affiliated with Harvard Medical School. The Institute’s Program to Enhance Relational and Communication Skills (PERCS) offers simulation-based interprofessional workshops designed to help trainees and practitioners engage in challenging healthcare conversations across situations such as critical care, primary care, parent presence during resuscitation, spiritual distress, adverse medical outcomes, informed consent, organ donation, and others.Objective: To describe the pedagogy, recruitment statistics and sustained participant outcomes of the Program to Enhance Relational and Communication Skills (PERCS).Methods: The pedagogical framework is based on creating safety for learning, emphasizing moral and relational aspects of care, suspending hierarchy to support interprofessional learning, honoring multiple perspectives, and valuing reflection and self-discovery. Programs bring together physicians, nurses, social workers, psychologists, chaplains and other healthcare professionals for a wide range of innovative educational offerings. Core learning occurs through live enactments of challenging conversations with professional actors portraying patients and family members, followed by guided debriefings that support individual and group reflection.Results: Approximately 3000 local, national and international professionals have participated since the program’s inception in 2002. PERCS workshop participants have reported a greater sense of preparation, confidence, improved communication and relational skills, and decreased anxiety when holding challenging healthcare conversations immediately following training and up to 12 months later. Benefits of the training were not related to discipline, level of experience or previous educational opportunities.Conclusions: Participants reported enhanced communication and relational skills. The program strives to develop relational competence in the healthcare world, including qualities of compassion, trust, and respect between clinicians and patients, and increased attention to interprofessional collaboration and knowledge sharing.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".