Simulation-Based Learning: Is it Time for a Paradigm Shift in Training Postgraduates for Crisis Resource Management? Prospective Interventional Study
Bibliographic record
Abstract
A BSTRACT Background: Crisis resource management (CRM) skills are nontechnical skills that are often neglected during the training of residents in the management of clinical emergencies. This study was conducted to assess the utility of high-fidelity simulation to enhance the learning of CRM among internal medicine postgraduates (PGs). Materials and Methods: After obtaining IEC approval, a total of ten (five 1 st year and five 3 rd year) internal medicine PG students were included by convenience sampling. Each student participated in four simulated emergencies in the simulation laboratory of a tertiary care hospital, one before and three after CRM training. They participated in groups of 3–5 at intervals of 15–30 days. All the scenarios were video graphed and viewed by the investigators and each student was assessed in each session for CRM using Ottawa global rating score (GRS). The outcome variable was an overall score (OS-ordinal from 1to 7). After the last session, a questionnaire was administered to assess the perceptions of the participants about the course. After 1 year, the participants self-assessed their CRM in a real emergency using GRS and also identified barriers for the application of CRM in real life. The statistical tests used were paired t -test, Student’s t -test, and repeated measures ANOVA. Results: The mean OS at baseline was 3.9 ± 1.5 and after training, it improved to 4.6 ± 1.26 ( P = 0.024). In the three posttraining sessions, the OS did not decline and this indicated good retention of CRM ( P = 0.056). PGs had a consistently favorable opinion of this course. The self-assessed OS in a real emergency was 5.7 ± 0.82. The barriers to CRM application in real emergencies were lack of practice and lack of team training. Conclusions: High fidelity simulation is an effective and acceptable method of teaching CRM to internal medicine PGs and should be incorporated into the PG curriculum.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".