Operating from the Other Side of the Table: Control Dynamics and the Surgeon Educator
Bibliographic record
Abstract
BACKGROUND: Critical moments in operations cause the surgeon to transition from a relatively "automatic" mode of operating to a more attentive mode-previously referred to as "slowing down when you should." Using this framework, this study explored how academic surgeons manage and balance the often competing responsibilities of patient safety and education during the slowing-down moments. STUDY DESIGN: This study used a constructivist approach to grounded theory methodology to explore an emergent theme of control among academic surgeons. Twenty-eight surgeons were interviewed across 4 academic teaching hospitals, and 5 general (hepato-pancreatico-biliary) surgeons were observed. Thematic analysis of the transcripts and field notes was conducted and iteratively elaborated and refined as data collection progressed with all team members. A reflexive approach was adopted throughout. RESULTS: An interesting control dynamic emerged as surgeons discussed the need to maintain a sense of control of an operation regardless of how much manual control they had. A dual responsibility to education and patient safety was apparent, with surgeons describing and demonstrating numerous strategies for negotiating manual control with the trainee during the critical slowing-down moments. An assessment of the trainee was implicit in the negotiation process. Numerous complications of control were identified ("bargaining," "skidding") as a product of this control dynamic. CONCLUSIONS: Operating from the "other side of the table" sets up a control dynamic that requires manipulation and negotiation on the part of the academic surgeon. Understanding these issues informs surgeons in their supervisory role, offering avenues for optimizing surgical training.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".