Mental Skills in Surgery
Bibliographic record
Abstract
OBJECTIVE: The present study investigated the role of mental skills in surgery through the unique lens of current surgeons who had previously served as Olympic athletes, elite musicians, or expert military personnel. BACKGROUND: Recent work has demonstrated great potential for mental skills training in surgery. However, as a field, we lag far behind other high-performance domains that explicitly train and practice mental skills to promote optimal performance. Surgery stands to benefit from this work. First, there is a need to identify which mental skills might be most useful in surgery and how they might be best employed. METHODS: Using a constructivist grounded theory approach, semi-structured interviews were conducted with 17 surgeons across the United States and Canada who had previously performed at an elite level in sport, music, or the military. RESULTS: Mental skills were used both to optimize performance in the moment and longitudinally. In the moment, skills were used proactively to enter an ideal performance state, and responsively to address unwanted thoughts or emotions to re-enter an acceptable performance zone. Longitudinally, participants used skills to build expertise and maintain wellness. CONCLUSIONS: Establishing a taxonomy for mental skills in surgery may help in the development of robust mental skills training programs to promote optimal surgeon wellness and performance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".