The who, what, and how of teamwork research in medical operating rooms: A scoping review
Bibliographic record
Abstract
Despite the importance of teamwork in the operating room (OR), teamwork can often be conflated with teamwork components (e.g., communication, cooperation). We reviewed the existing literature pertaining to OR teamwork to understand which teamwork components have been assessed. Following PRISMA guidelines for scoping reviews, 4,233 peer-reviewed studies were identified using MEDLINE and Embase. Eighty-seven studies were included for synthesis and analysis. Using the episodic model of teamwork as an organizing framework, studies were grouped into the following teamwork categories: (a) transition processes (e.g., goal specification), (b) action processes (e.g., coordination), (c) interpersonal processes (e.g., conflict management), (d) emergent states (e.g., psychological safety), or (e) omnibus topics (a combination of higher-order teamwork processes). Results demonstrated that action processes were most frequently explored, followed by transition processes, omnibus topics, emergent states, and interpersonal processes. Although all studies were framed as investigations of teamwork, it is important to highlight that most explored only one or a few constructs under the overarching umbrella of teamwork. We advocate for enhanced specificity with descriptions of OR teamwork, reporting practices pertaining to interprofessional demographics and outcomes, and increased diversity in study design and surgery type to advance understanding of teamwork and its implications.
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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 | 0.083 | 0.242 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.042 | 0.038 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".