Do technical skills correlate with non-technical skills in crisis resource management: a simulation study
Bibliographic record
Abstract
BACKGROUND: Both technical skills (TS) and non-technical skills (NTS) are key to ensuring patient safety in acute care practice and effective crisis management. These skills are often taught and assessed separately. We hypothesized that TS and NTS are not independent of each other, and we aimed to evaluate the relationship between TS and NTS during a simulated intraoperative crisis scenario. METHODS: This study was a retrospective analysis of performances from a previously published work. After institutional ethics approval, 50 anaesthesiology residents managed a simulated crisis scenario of an intraoperative cardiac arrest secondary to a malignant arrhythmia. We used a modified Delphi approach to design a TS checklist, specific for the management of a malignant arrhythmia requiring defibrillation. All scenarios were recorded. Each performance was analysed by four independent experts. For each performance, two experts independently rated the technical performance using the TS checklist, and two other experts independently rated NTS using the Anaesthetists' Non-Technical Skills score. RESULTS: TS and NTS were significantly correlated to each other (r=0.45, P<0.05). CONCLUSIONS: During a simulated 5 min resuscitation requiring crisis resource management, our results indicate that TS and NTS are related to one another. This research provides the basis for future studies evaluating the nature of this relationship, the influence of NTS training on the performance of TS, and to determine whether NTS are generic and transferrable between crises that require different TS.
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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 | 0.005 | 0.039 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".