Identifying positive and negative use of non-technical skills by anesthesiologists in the clinical operating room: An exploratory descriptive study
Bibliographic record
Abstract
Background: Teamwork is a critical competency in high-risk settings like the operating room (OR). While conventional approaches focus on describing and learning from negative performance, there may be value in learning from high-performing behaviour, particularly in specialties where serious safety events are relatively rare. This study aimed to explore both the positive and negative use of non-technical skills by anesthesia practitioners in the OR and situate them within the clinical OR context. Methods: This study employed a prospective observational design. Following research ethics approval, a sample of surgical cases in a tertiary hospital were recorded using the OR Black Box®. Data related to surgical phase timing, non-technical skills, team factors, and environmental factors were identified by analysts according to a modified Systems Engineering Initiative for Patient Safety model. We performed descriptive statistics and qualitative description of these observations. Results: We observed 25 surgical cases capturing 242 instances of positive non-technical skills among anesthesiologists in the operating room and 9 instances of negative demonstrations. Situational awareness was most frequently (n = 160) observed, followed by communication and teamwork skills (n = 82), and were most often demonstrated in the context of potential environmental distractions (e.g., doors opening, unnecessary interruptions). The least common category of positive non-technical skills observed was leadership (n = 3). Conclusions: Our findings show anesthesiologists are doing a lot "right" and there may be many opportunities for learning from positive practice in the clinical setting. These findings can inform future work to better understand and standardize best practices for non-technical performance in anesthesia.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".