Evaluation of the Impact of a Simulation-enhanced Breaking Bad News Workshop in Pediatrics
Bibliographic record
Abstract
INTRODUCTION: Our goal was to develop and evaluate the effectiveness of a simulation-based workshop for teaching pediatric trainees' communication skills in breaking bad news. METHODS: A simulation-based workshop was developed to teach skills in breaking bad news. After a classroom-based introduction, small groups of residents participated in 3 scenarios, each starting with a simulated resuscitation, followed by 2 conversations with the patient's parent, played by actors. Each conversation was observed through a 1-way mirror and was followed by a debriefing. After the workshop, the residents completed workshop evaluations and a self-assessment. Before and after the workshop, residents were evaluated in Objective Structured Clinical Examination stations where they were required to give bad news. Two physician experts and 2 parents who personally experienced receiving bad news about their child evaluated resident performance using a previously validated communication evaluation tool. RESULTS: Residents' ratings of the workshop were very high for all items, and 100% of the residents reported improvement in their ability to deliver bad news after the workshop. Statistically significant improvement was found in 14 of 17 items of the evaluation tool used by experts and parents, with the parents reporting greater improvement than the experts. CONCLUSIONS: This reflective, simulation-based workshop successfully improved pediatric trainees' skills in having difficult conversations with families, as evaluated by the participants, by physician experts, and, most importantly, by parents who have experienced these conversations in real life.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".