Pediatric Crisis Resource Management Training Improves Emergency Medicine Trainees’ Perceived Ability to Manage Emergencies and Ability to Identify Teamwork Errors
Bibliographic record
Abstract
OBJECTIVES: Improved pediatric crisis resource management (CRM) training is needed in emergency medicine residencies because of the variable nature of exposure to critically ill pediatric patients during training. We created a short, needs-based pediatric CRM simulation workshop with postactivity follow-up to determine retention of CRM knowledge. Our aims were to provide a realistic learning experience for residents and to help the learners recognize common errors in teamwork and improve their perceived abilities to manage ill pediatric patients. METHODS: Residents participated in a 4-hour objectives-based workshop derived from a formal needs assessment. To quantify their subjective abilities to manage pediatric cases, the residents completed a postworkshop survey (with a retrospective precomponent to assess perceived change). Ability to identify CRM errors was determined via a written assessment of scripted errors in a prerecorded video observed before and 1 month after completion of the workshop. RESULTS: Fifteen of the 16 eligible emergency medicine residents (postgraduate year 1-5) attended the workshop and completed the surveys. There were significant differences in 15 of 16 retrospective pre to post survey items using the Wilcoxon rank sum test for non-parametric data. These included ability to be an effective team leader in general (P < 0.008), delegating tasks appropriately (P < 0.009), and ability to ensure closed-loop communication (P < 0.008). There was a significant improvement in identification of CRM errors through the use of the video assessment from 3 of the 12 CRM errors to 7 of the 12 CRM errors (P < 0.006). CONCLUSIONS: The pediatric CRM simulation-based workshop improved the residents' self-perceptions of their pediatric CRM abilities and improved their performance on a video assessment task.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".