Waking up the next morning: surgeons’ emotional reactions to adverse events
Bibliographic record
Abstract
CONTEXT: The adverse patient event is an inherent component of surgical practice, but many surgeons are unprepared for the profound emotional responses these events can evoke. This study explored surgeons' reactions to adverse events and their impact on subsequent judgement and decision making. METHODS: Using a constructivist grounded theory approach, we conducted 20 semi-structured, 60-minute interviews with surgeons across subspecialties, experience levels, and sexes to explore surgeons' recollections of reactions to adverse events. Further interviews were conducted with six general surgeons to explore more immediate reactions after 28 adverse events. Data coding was both inductive, developing a new framework based on emergent themes, and deductive, using an existing framework for care providers' reactions to adverse events. RESULTS: Surgeons expressed feeling unique and alone in the depths of their reactions to adverse events and consistently described four phases of response, each containing cognitive and emotive components, following such events. The initial phase (the kick) involved feelings of failure ('Am I good enough?') experienced with a significant physiological response. This was shortly followed by a second phase (the fall), during which the surgeon experienced a sense of chaos and assessed the extent of his or her contribution to the event ('Was it my fault?'). During the third phase (the recovery), the surgeon reflected on the adverse event ('What can I learn?') and experienced a sense of 'moving on'. In the fourth phase (the long-term impact), the surgeon experienced the prolonged and cumulative effects of these reactions on his or her own personal and professional identities. Surgeons also described an effect on their clinical judgement, both for the case in question (minimisation) and future cases (overcompensation). CONCLUSIONS: Surgeons progress through a series of four phases following adverse events that are potentially caused by or directly linked to surgeon error. The framework provided by this study has implications for teaching, surgeon wellness and surgeon error.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".