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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".