Exploring the Role of 'Slowing Down When You Should' in Operative Surgical Judgment
Bibliographic record
Abstract
Context: The study of expertise in medical education has tended to follow the traditions of describing either the analytic processes or the non-analytic resources that experts acquire with experience. We argue that a critical function of expertise is the ability to transition from the automatic mode to the more effortful mode when required – a transition referred to as ‘slowing down when you should’. Objectives: To explore the phenomenon of ‘slowing down when you should’ in operative surgical practice and its role in intra-operative surgical judgment, and to develop conceptual models of the factors involved in the display of this transition in surgical operative practice. Design: In Phase 1A, 28 surgeons were interviewed about their views of surgical judgment in general and their perceptions of the role of this phenomenon in operative judgment. In Phase 1B, a subset of surgeons from Phase 1A was re-interviewed to explore their perceptions of automaticity in operative practice. In Phase 2, observational sessions (and brief interviews) were conducted of surgeons in the operating room to explore the nature of this phenomenon in its natural environment. Results: The surgeons in this study recognized the phenomenon of ‘slowing down’ in their operative practice and acknowledged its link to surgical judgment. Two main initiators were described and observed: proactively planned ‘slowing down’ moments occurring intra-operatively initiated by critical events anticipated pre-operatively and situationally responsive ‘slowing down’ moments initiated by emergent cues intra-operatively. Numerous influences of this transition were uncovered. A control dynamic emerged as surgeon’s negotiated ‘slowing down’ moments through trainees in their supervisory academic practice. Numerous manifestations of this phenomenon were observed in the operating room and considered using a cognitive psychology attention capacity model. Conclusions: This study offers a conceptual framework for understanding the role of ‘slowing down when you should’ in operative surgical practice, providing a vocabulary that will allow more explicit consideration of what contributes to surgical expertise. Consideration of this framework with its consequent ability to make surgical practices more explicit has implications for self-regulation in practice, surgical error, and 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 | 0.010 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".