Peer-assisted learning in critical care: a simulation-based approach for postgraduate medical training
Why this work is in the frame
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Bibliographic record
Abstract
Enhancing clinical competence in postgraduate year (PGY) trainees is crucial for effective patient care, especially in emergency medicine. This study investigated the impact of a well-designed, group-developed, peer-assessed learning approach combined with high-fidelity simulations on clinical skills and teamwork of PGY trainees. PGY trainees participated in a one-month program featuring team development, clinical training, scenario design, simulation, peer-assisted debriefing, and post-course evaluations at one week and three months. Trainees were divided into two groups, engaged in clinical practice, group discussions, and developed critical scenarios under mentor guidance to challenge the other group. Teamwork performance was assessed using the TEAM scale, Ottawa Global Rating Scale, and reflective essays. Follow-up evaluations employed the PGY Clinical Proficiency Evaluation scale. Trainees identified deficiencies in situation monitoring and maintaining composure, noting difficulties in effectively monitoring and reassessing situations. Despite having passed ACLS training, participants recognized their lack of clinical experience in managing critically ill patients, handling dynamic situations, low self-confidence, and limited leadership opportunities in resuscitation teams. However, team morale was high, and performance in communication and leadership was relatively strong due to the similar hierarchical levels of the trainees and initial team dynamics established during their training. Follow-up questionnaires indicated significant improvements in clinical confidence, reasoning abilities, familiarity with ACLS resuscitation guidelines, and team dynamics across various subspecialty training areas. The integration of peer-assisted learning with high-fidelity simulation significantly enhanced clinical competence, teamwork, and confidence in PGY trainees. This innovative approach provides a structured, supportive learning environment that effectively prepares trainees for real-world clinical challenges. Future research should explore long-term outcomes and broader applications of this method.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it