Surgeons’ Leadership Style and Team Behavior in the Hybrid Operating Room
Bibliographic record
Abstract
OBJECTIVE: This study aimed to assess the relationship between surgeons' leadership style and team behavior in the hybrid operating room through video coding. Secondly, possible fluctuations possible fluctuations in leadership styles and team behavior during operative phases were studied. BACKGROUND: Leadership is recognized as a key component to successful team functioning in high-risk industries. The 'full range of leadership' theory is commonly used to evaluate leadership, marking transformational, transactional, and passive. Few studies have examined the effects of these leadership styles on team behavior in surgery and/or their fluctuations during surgery. METHODS: A single-center study included patients planned for routine endovascular procedures. A medical data capture system was used to allow post hoc video coding through Behavior Anchored Rating Scales. Multilevel statistical analysis was performed to assess possible correlations between leadership style and 3 team behavior indicators (speaking up, knowledge sharing, and collaboration) on an operative phase level. RESULTS: Twenty-two cases were analyzed (47 hours recording). Transformational leadership is positively related to the extent to which team members work together (γ=0.20, P <0.001), share knowledge (γ=0.45, P <0.001), and speak up (γ=0.64, P <0.001). Passive leadership is significantly positively correlated with speaking up (γ=0.29, P =0.004). Leadership style and team behavior clearly fluctuate during a procedure, with similar patterns across different types of endovascular procedures. CONCLUSIONS: Consistent with other professional fields, surgeons' transformational leadership enhances team behavior, especially during the most complex operative phases. This suggests that encouraging surgeons to learn and actively implement a transformational leadership style is meaningful to enhance patient safety and team performance.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".