Assessing Surgical Teamwork Competencies During Moments of Uncertainty Using OR Black Box
Bibliographic record
Abstract
Teamwork is an essential aspect to maintaining high-quality healthcare. This is especially true during times of uncertainty, when collaborative problem solving is necessary for clinical teams to adapt and deliver safe and effective care. We conducted a prospective observational study using audio/visual analysis captured by OR Black Box. Human factors experts transcribed and coded the videos using an evidence-based teamwork framework, specific to healthcare. We identified teamwork competencies that were either present or absent during moments of uncertainty in the operating room. Four main team roles (nurses, anesthesiologists, surgeons and trainees) were studied. We identified 3539 instances of teamwork, during 180 hours of surgical observation, and categorized them into 7 competencies. Team leadership was expressed significantly more often by surgeons compared to other team members whereas backup behaviour was expressed significantly more by nurses. Understanding how each team role uniquely contributes to teamwork can help develop specific and actionable teamwork interventions, which could ultimately lead to increased safety in the OR.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".