Mental toughness in surgeons: Is there room for improvement?
Bibliographic record
Abstract
Background: Mental toughness is crucial to high-level performance in stressful situations. However, there is no formal evaluation or training in mental toughness in surgery. Our objective was to examine differences in mental toughness between staff and resident surgeons, and whether there is an interest in improving this attribute. Methods: We distributed a survey containing the Mental Toughness Index (domains of self-belief, attention regulation, emotion regulation, success mindset, context knowledge, buoyancy, optimism and adversity capacity) among general surgery residents and staff at 3 Canadian academic institutions. Responses were recorded on a 7-point Likert scale. Participants were also asked about techniques they used to help them perform under pressure and interest in further developing mental toughness. Results: Eighty-three of 193 surgeons participated: 56/105 (52.8%) residents and 27/87 (31.0%) staff. The average age was 29 (standard deviation 5) years and 42 (standard deviation 8) years, respectively. Residents scored significantly lower than staff in all mental toughness domains. Men scored significantly higher than women in attention regulation and emotion regulation. Age, staff experience and resident postgraduate year were not significantly associated with mental toughness scores. Twenty residents (36%) and 17 staff (63%) reported using specific techniques to deal with stressful situations; 49 (88%) and 15 (56%), respectively, were interested in further developing mental toughness. Conclusion: Staff surgeons scored significantly higher than residents in all mental toughness domains measured. Both groups expressed a desire to improve mental toughness. There are many techniques to improve mental toughness, and further research is needed to assess their effectiveness in 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 | 0.001 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".