Surgeon Fear in the Operating Room and Its Link to Identity
Bibliographic record
Abstract
OBJECTIVE: To gain a deeper understanding of the underpinnings of surgeon fear in the operating room (OR). BACKGROUND: Fear can affect judgement, risk-tolerance, and decision-making, all of which may impact surgeon performance, yet there is little research on surgeon fear in the OR. METHODS: Using constructivist grounded theory, we conducted semistructured interviews with 18 attending surgeons from 14 surgical subspecialties affiliated with the same large, urban, academic institution. Snowball and purposive sampling were used to achieve diversity in sampling. Data collection and analysis were iterative and guided by theoretical sampling. A conceptual framework was generated from the data. Surgeons ranged from 1 to 40+ years in practice. RESULTS: Most surgeons in this study experienced fear in the OR. Themes related to identity, identity enmeshment, and identity resilience were identified during coding; data were reanalyzed deductively using these categories, and findings suggested that surgeons who more intensely experienced fear in the OR potentially had lower identity resilience and were experiencing the fearful stimuli as identity threat. While this impacted the surgeon's experience inside the OR, surgeons with identity enmeshment were impacted beyond the OR: events, good or bad, that occurred in the OR seeped into the surgeon's personal life, thereby extending the experience of the event. CONCLUSIONS: Most surgeons in this study experience fear in the OR. This study offers a glimpse into the rarely explored experience of surgeon fear in the OR, highlighting the importance of identity management and emotion regulation in surgical education and practice.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".