Transforming from victim to survivor—Part 2: Fixing the systems that enable disruptive intraoperative behaviour
Bibliographic record
Abstract
Disruptive intraoperative behaviour is prevalent and consequential. It undermines patient care, sets a poor example for medical students, and erodes clinician wellbeing. Part 1 of this article series emphasized the importance of micro-level solutions, including proper appraisals and behavioural responses by victims and witnesses. However, focusing exclusively on clinician-level strategies puts undue responsibility on those individuals already affected. Part 2 focuses on the broader systems that allow disruptive behaviour to persist and, more importantly, how they can be changed. Specifically, this article explores how systems of hiring, education, mentorship, and cultural reinforcement shape the clinical environment and can either enable or prevent unprofessional conduct. Hiring practices should include candid discussions about professional expectations and anticipated challenges, while selecting candidates aligned with organizational values. Educational programs should explicitly teach clinicians the values and soft skills needed to avoid and mitigate disruptive behaviour and then engrain these skills using simulation. Mentorship systems should match new clinicians with good models of professionalism and should leverage advancements in professionalism education to hasten cultural change. Finally, organizations should create clear policies, enforce behavioural expectations consistently and fairly, create confidential reporting mechanisms, adjust working conditions to reduce stress and burnout, and supply supports to clinicians in need. Complex social issues like disruptive behaviour require both individual action and systems reform. Ultimately, combining these micro and macro-level solutions can mitigate the negative impacts of disruptive behaviour and shift organizational culture toward professionalism and safety.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".