Slowing Down to Stay Out of Trouble in the Operating Room: Remaining Attentive in Automaticity
Bibliographic record
Abstract
PURPOSE: Automaticity is integral to expert performance, but experts must be able to transition from an automatic mode into a more effortful state when required. In this study, the authors identified and characterized the manifestations of the phenomenon of "slowing down when you should" to stay out of trouble in operative practice. METHOD: The authors interviewed 28 surgeons (60-minute, semistructured format) from various specialties at four academic medical centers and observed 5 hepatopancreatobiliary surgeons in the operating room (29 cases, 147 hours) during 2007-2009. Using a grounded theory qualitative methodology, they conducted a thematic analysis of transcripts and field notes in an iterative manner. Data collection continued until saturation. They adopted a reflexive approach throughout. RESULTS: Surgeons described and the authors observed four phenomenological manifestations of the transition to a more effortful state. In the most extreme manifestation, "stopping," surgeons actually stopped the procedure, whereas in the most subtle manifestation, "fine-tuning," surgeons were able to continue the procedure and focus on minor events simultaneously. A separate phenomenon of "drifting" represented surgeons' failure to transition out of the automatic mode when appropriate, resulting in surgical errors or near misses. CONCLUSIONS: The manifestations of the slowing down phenomenon represent acts of cognitive refocusing during the potentially more-critical moments of operative practice. Further, the authors challenge the conception of automaticity as effortless, arguing that automatic behavior can be attentive (fine-tuning) as well as inattentive (drifting).
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".