Teaching operating room conflict management to surgeons: clarifying the optimal approach
Bibliographic record
Abstract
CONTEXT: Conflict management has been identified as an essential competence for surgeons as they work in operating room (OR) teams; however, the optimal approach is unclear. Social science research offers two alternatives, the first of which recommends that task-related conflict be managed using problem-solving techniques while avoiding relationship conflict. The other approach advocates for the active management of relationship conflict as it almost always accompanies task-related conflict. Clarity about the optimal management strategy can be gained through a better understanding of conflict transformation, or the inter-relationship between conflict types, in this specific setting. The purpose of this study was to evaluate conflict transformation in OR teams in order to clarify the approach most appropriate for an educational conflict management programme for surgeons. METHODS: A constructivist grounded theory approach was adopted to explore the phenomenon of OR team conflict. Narratives were collected from focus groups of OR nurses and surgeons at five participating centres. A subset of these narratives involved transformation between and within conflict types. This dataset was analysed. RESULTS: The results confirm that misattribution and the use of harsh language cause conflict transformation in OR teams just as they do in stable work teams. Negative emotionality was found to make a substantial contribution to responses to and consequences of conflict, notably in the swiftness with which individuals terminated their working relationships. These findings contribute to a theory of conflict transformation in the OR team. CONCLUSIONS: There are a number of behaviours that activate conflict transformation in the OR team and a conflict management education programme should include a description of and alternatives to these behaviours. The types of conflict are tightly interwoven in this setting and thus the most appropriate management strategy is one that assumes that both types of conflict will exist and should be managed actively.
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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 | 0.085 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".