Disruptive behavior in the operating room
Bibliographic record
Abstract
PURPOSE OF REVIEW: Disruptive workplace behavior can have serious consequences to clinicians, institutions, and patients. There is a range of disruptive behaviors, and the consequences are often underappreciated. The purpose of this manuscript is to review the definition, prevalence, consequences, prevention, and management of disruptive behavior in the operating room. RECENT FINDINGS: Although a small minority of operating room clinicians act disruptively, 98% of clinicians report having recently been exposed to disruptive behavior, with the average being 64 events per clinician per year. The causes include intrapersonal factors, workplace relationships, workplace logistics, and broader contextual factors. Disruptive behavior undermines patient care by decreasing individual and team clinical performance. It decreases clinician well being, sets a poor example for medical students who are susceptible to negative role models, and decreases hospital efficiency. The way that clinicians respond to disruptive behavior may either exacerbate or reduce the consequences of the behavior. In order to prevent disruptive behavior, the causes must be addressed. Institutions must have robust policies to deal with disruptive behavior and have preventive measures that include regular staff education. Whenever disruptive behavior does occur, it must be expeditiously addressed, which may include graded discipline. SUMMARY: Disruptive intraoperative behavior is prevalent and harms multiple parties in the operating room. Institutions require comprehensive measures to prevent the behavior and to mitigate consequences.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".