“I Was Worried About the Patient, but I Wasn’t Feeling Worried”: How Physicians Judge Their Comfort in Settings of Uncertainty
Bibliographic record
Abstract
PURPOSE: Clinical educators often raise concerns that learners are not comfortable with uncertainty in clinical work, yet existing literature provides little insight into practicing clinicians' experiences of comfort when navigating the complex, ill-defined problems pervasive in practice. Exploring clinicians' comfort as they identify and manage uncertainty in practice could help us better support learners through their discomfort. METHOD: Between December 2018 and April 2019, the authors employed a constructivist grounded theory approach to explore experiences of uncertainty in emergency medicine faculty. The authors used a critical incident technique to elicit narratives about decision making immediately following participants' clinical shifts, exploring how they experienced uncertainty and made real-time judgments regarding their comfort to manage a given problem. Two investigators analyzed the transcripts, coding data line-by-line using constant comparative analysis to organize narratives into focused codes. These codes informed the development of conceptual categories that formed a framework for understanding comfort with uncertainty. RESULTS: Participants identified multiple forms of uncertainty, organized around their understanding of the problems they were facing and the potential actions they could take. When discussing their comfort in these situations, they described a fluid, actively negotiated state. This state was informed by their efforts to project forward and imagine how a problem might evolve, with boundary conditions signaling the borders of their expertise. It was also informed by ongoing monitoring activities pertaining to patients, their own metacognitions, and their environment. CONCLUSIONS: The authors' findings offer nuances to current notions of comfort with uncertainty. Uncertainty involved clinical, environmental, and social aspects, and comfort dynamically evolved through iterative cycles of forward planning and monitoring.
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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 | 0.001 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".