Nontechnical Skills Assessment After Simulation-Based Continuing Medical Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Human factors have been identified as root causes of human error in medicine. The "Anesthetists' Non-Technical Skills (ANTS) system" evaluates the effect of simulation training and debriefing on nontechnical skills (NTS). Studies suggest that residents' NTS may improve after simulation training but the effect on NTS of practicing anesthesiologists is unclear. The purpose of this study was to determine whether high-fidelity simulation training and debriefing improved the NTS of practicing anesthesiologists using the ANTS tool. METHODS: In a previous study, 67 practicing anesthesiologists managed a 45-minute standardized anesthetic case using high-fidelity simulation and returned 5 to 9 months later to manage a second case. After Research Ethics Board approval, two blinded video reviewers, trained in the use of the ANTS system, evaluated archived videotapes of the 59 subjects who completed both sessions. Results were analyzed with a mixed-design analysis of variance. Interrater reliability was calculated using the intraclass correlation coefficient. RESULTS: Interrater reliability for the ANTS scoring was 0.436, P < 0.05. Overall, ANTS scores improved approximately 5% from session 1 to 2 (P < 0.01), but there was no effect due to debriefing. The situational awareness ANTS category showed a statistically significant effect of debriefing (P < 0.05). CONCLUSIONS: The relatively short simulation intervention, the length of time until the posttest was completed, well-developed NTS in practicing physicians, and a tool that might not be the optimal method of measurement may all account for the lack of improvement in NTS of practicing anesthesiologists as demonstrated in this study.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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