Transforming from victim to survivor—Part 1: Strategies for clinicians to safeguard themselves, colleagues, and patients from disruptive intraoperative behaviour
Bibliographic record
Abstract
Disruptive intraoperative behaviour is pervasive and harms clinicians, patients, and institutions. Clinicians exposed to disruptive behaviour inadvertently become parties to an interpersonal conflict. While previous reviews focused on antecedents and consequences of disruptive behaviour, we adopt a conflict resolution perspective to (1) equip clinicians to maintain their well-being when faced with disruptive behaviour; and (2) outline how clinicians can respond to prevent escalation while not reinforcing the behaviour. Clinician responses start with cognitive appraisals, which determine the psychological impact of disruptive behaviour. Clinicians can improve their appraisals using situational awareness, cognitive reappraisal, and grounding techniques. Over the long term, clinicians can use adaptive coping mechanisms, characterized by a “survivor” mindset, and avoid maladaptive strategies, characterized by a psychologically harmful “victim” mindset. Clinicians must be mindful of the roles they assume in conflicts. Manipulative and malicious responses turn clinicians into accomplices or retaliatory offenders, while overusing passive responses risks relegating them to being enabling bystanders. Instead, clinicians should respond assertively, which transforms them into upstanders. Successful assertive efforts involve refocusing attention, avoiding flash escalation, using structured communication tools, setting clear boundaries, and practising these skills via simulation. By adopting these micro-level interventions, clinicians can help cultivate a respectful OR culture and safeguard their well-being.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".