Slowing Down to Stay Out of Trouble in the Operating Room: Remaining Attentive in Automaticity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it