Nontechnical Skills Assessment After Simulation-Based Continuing Medical Education
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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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".