Warning bells: How clinicians leverage their discomfort to manage moments of uncertainty
Bibliographic record
Abstract
OBJECTIVES: It remains unclear how medical educators can more effectively bridge the gap between trainees' intolerance of uncertainty and the tolerance that experienced physicians demonstrate in practice. Exploring how experienced clinicians experience, appraise and respond to discomfort arising from uncertainty could provide new insights regarding the kinds of behaviours we are trying to help trainees achieve. METHODS: We used a constructivist grounded theory approach to explore how emergency medicine faculty experienced, managed and responded to discomfort in settings of uncertainty. Using a critical incident technique, we asked participants to describe case-based experiences of uncertainty immediately following a clinical shift. We used probing questions to explore cognitive, emotional and somatic manifestations of discomfort, how participants had appraised and responded to these cues, and how they had used available resources to act in these moments of uncertainty. Two investigators coded the data line by line using constant comparative analysis and organised transcripts into focused codes. The entire research team discussed relationships between codes and categories, and developed a conceptual framework that reflected the possible relationships between themes. RESULTS: Participants identified varying levels of discomfort in their case descriptions. They described multiple cues alerting them to problems that were evolving in unexpected ways or problems with aspects of management that were beyond their abilities. Discomfort served as a trigger for participants to monitor a situation with greater attention and to proceed more intentionally. It also served as a prompt for participants to think deliberately about the types of human and material resources they might call upon strategically to manage these uncertain situations. CONCLUSIONS: Discomfort served as a dynamic means to manage and respond to uncertainty. To be 'tolerant' of uncertainty thus requires clinicians to embrace discomfort as a powerful tool with which to grapple with the complex problems pervasive in clinical 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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".