Optimizing Teamwork in the Operating Room: A Scoping Review of Actionable Teamwork Strategies
Bibliographic record
Abstract
Suboptimal teamwork in the operating room (OR) is a contributing factor in a significant proportion of preventable complications for surgical patients. Specifying behaviour is fundamental to closing evidence-practice gaps in healthcare. Current teamwork interventions, however, have yet to be synthesized in this way. This scoping review aimed to identify actionable strategies for use during surgery by mapping the existing literature according to the Action, Actor, Context, Target, Time (AACTT) framework. The databases MEDLINE (Medical Literature Analysis and Retrieval System Online), Embase, Cumulated Index to Nursing and Allied Health Literature (CINAHL), Education Resources Information Center (ERIC), Cochrane, Scopus, and PsycINFO were searched from inception to April 5, 2022. Screening and data extraction were conducted in duplicate by pairs of independent reviewers. The search identified 9,289 references after the removal of duplicates. Across 249 studies deemed eligible for inclusion, eight types of teamwork interventions could be mapped according to the AACTT framework: bundle/checklists, protocols, audit and feedback, clinical practice guidelines, environmental change, cognitive aid, education, and other), yet many were ambiguous regarding the actors and actions involved. The 101 included protocol interventions appeared to be among the most actionable for the OR based on the clear specification of ACCTT elements, and their effectiveness should be evaluated and compared in future work.
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 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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".