Preparing the Next Generation of Code Blue Leaders Through Simulation: What's Missing?
Bibliographic record
Abstract
INTRODUCTION: Despite the increasing reliance on simulation to train residents as code blue leaders, the perceived role and effectiveness of code blue simulations from the learners' perspective have not been explored. A code blue Simulation Program (CBSP), developed based on evidence-based simulation principles, was implemented at our institution. We explored the role of simulation in code blue training and the differences between real and simulated code blues from the learner perspective. METHODS: Using a thematic analysis approach and a purposeful sampling strategy, residents who participated in the CBSP were invited to participate in one of the three focus groups. Data were collected through small group discussions guided by semistructured interviews. The interviews were audio-recorded and transcribed. Interview transcripts were coded to assess underlying themes. RESULTS: Thematic analysis revealed that participants believed that the CBSP enhanced preparedness by capturing aspects of real codes (eg, inclusion of precode scenarios with awake patients, lack of readily available information) and facilitating automatization of code blue processes. Despite efforts to develop a high-fidelity simulation, participants noted that they experienced more anxiety, observed more chaos in the environment, and encountered different communication challenges in real codes. CONCLUSIONS: The CBSP enhanced resident preparedness to serve as code blue leaders. Learners highlighted that they valued the CBSP; however, differences remain between simulated and real codes that could be addressed to enhance the fidelity of future simulations.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".