Implementation of an Effective Strategy for Teaching Nontechnical Skills in the Operating Room
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of debriefing and feedback on intraoperative nontechnical performance as an instructional strategy in surgical training. BACKGROUND: Regulatory authorities for accreditation in North America have included nontechnical skills such as communication and teamwork in the competencies to be acquired by surgical residents before graduation. Concrete recommendations regarding the training and assessment of these competencies, however, are lacking. METHODS: Nonrandomized, single-blinded study using an interrupted time-series design. Eleven senior surgical residents were observed during routine cases in the operating room (OR) at baseline and post-training. The Non-Technical Skills for Surgeons (NOTSS) rating system was used. Observers were trained in NOTSS and blinded to the study purpose. Independent of the blinded observations, a surgeon educator conducted intraoperative observations, which served as the basis for the structured debriefing and feedback intervention. The intervention was administered to participants after a set of (blinded) baseline observations had been completed. Primary outcome was nontechnical performance in the OR as measured by the NOTSS system. Secondary outcome was perceived utility as assessed by a post-training questionnaire. RESULTS: Twelve senior trainees were recruited, 11 completed the study. Average NOTSS scores improved significantly from 3.2 (SD 0.37) at baseline to 3.5 (SD 0.43) post-training [t(10) = -2.55, P = 0.29]. All participants felt the intervention was useful, and the majority thought that debriefing and feedback on nontechnical skills should be integrated in surgical training. CONCLUSIONS: Debriefing and feedback in the OR may represent an effective strategy to ensure development of nontechnical skills in competency-based education.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".