What’s Behind the Scenes? Exploring the Unspoken Dimensions of Complex and Challenging Surgical Situations
Bibliographic record
Abstract
PURPOSE: Physicians regularly encounter challenging and/or complex situations in their practices; in training settings, they must help learners understand such challenges. Context becomes a fundamental construct when seeking to understand what makes a situation challenging and how physicians respond to it; however, the question of how physicians perceive context remains largely unexplored. If the goal is to teach trainees to deal with challenging situations, the medical education community requires an understanding of what "challenging" means for those in charge of training. METHOD: The authors relied on visual methods for this research. In 2013, they collected 40 snapshots (i.e., data sets) from a purposeful sample of five faculty surgeons through a combination of interviews, observations, and drawing sessions. The analytical process involved three phases: analysis of each drawing, a compare-and-contrast analysis of multiple drawings, and a team analysis conducted in collaboration with three participating surgeons. RESULTS: Findings demonstrate that experts perceive the challenge of surgical situations to extend beyond their procedural dimensions to include unspoken, nonprocedural dimensions-specifically, team dynamics, trust, emotions, and external pressures. CONCLUSIONS: Findings show that analysis of surgeons' drawings is an effective means of gaining insight into surgeons' perceptions. The findings refine the common belief that procedural complexity is what makes a surgery challenging for expert surgeons. Focusing exclusively on the procedure during training may put trainees at risk of missing the "big picture." Understanding the multidimensionality of medical challenges and having a language to discuss these both verbally and visually will facilitate teaching around challenging situations.
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".