Analyzing interprofessional teamwork in the operating room: An exploratory observational study using conventional and alternative approaches
Bibliographic record
Abstract
Intraoperative teamwork is vital for patient safety. Conventional tools for studying intraoperative teamwork typically rely on behaviorally anchored rating scales applied at the individual or team level, while others capture narrative information across several units of analysis. This prospective observational study characterizes teamwork using two conventional tools (Operating Theatre Team Non-Technical Skills Assessment Tool [NOTECHS]; Team Emergency Assessment Measure [TEAM]), and one alternative approach (modified-Systems Engineering Initiative for Patient Safety [SEIPS] model). We aimed to explore the advantages and disadvantages of each for providing feedback to improve teamwork practice. Fifty consecutive surgical cases at a Canadian academic hospital were recorded with the OR Black Box®, analyzed by trained raters, and summarized descriptively. Teamwork performance was consistently high within and across cases rated with NOTECHS and TEAMS. For cases analyzed with the modified-SEIPS tool, both optimal and suboptimal teamwork behaviors were identified, and team resilience was frequently observed. NOTECHS and TEAM provided summative assessments and overall pattern descriptions, while SEIPS facilitated a deeper understanding of teamwork processes. As healthcare organizations continue to prioritize teamwork improvement, SEIPS may provide valuable insights regarding teamwork behavior and the broader context influencing performance. This may ultimately enhance the development and effectiveness of multi-level teamwork interventions.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".