How do residents respond to uncertainty with peers and supervisors in multidisciplinary teams? Insights from simulations with epistemic fidelity
Bibliographic record
Abstract
BACKGROUND: Residents struggle to express clinical uncertainty, often exhibiting negative cognitive, behavioral, and emotional responses to uncertainty when engaging with patients or supervisors. However, the Integrative Model of Uncertainty Tolerance posits that individuals may have positive or negative responses to perceived uncertainty. Situational characteristics, such as interactions with other health professionals, can impact whether the response is positive or negative. The team context in which residents interact with resident peers and supervisors could represent varying situational characteristics that enable a spectrum of responses to uncertainty. Understanding the situational characteristics of multidisciplinary teams that allow residents to display positive responses to perceived uncertainty could inform strategies to foster positive responses to uncertainty in other contexts. We explored resident responses to perceived uncertainty in a simulated multidisciplinary team context. METHODS: A simulation-primed qualitative inquiry approach was used. Fourteen residents from Cardiology and Obstetrics and Gynecology participated in simulation scenarios involving pregnant patients with heart disease. We incorporated epistemic fidelity through the deliberate inclusion of ambiguity and complexity to prompt uncertainty. Audio recordings of debriefing sessions were analyzed using directed content analysis. RESULTS: Residents recognized that uncertainty is unavoidable, and positive responses to uncertainty are crucial to team dynamics and patient safety. While residents had positive responses to expressing uncertainty to peers, they had predominantly negative responses to expressing uncertainty to supervisors. Predominant negative response to supervisors related to judgement from supervisors, and impacts on perceived trustworthiness or independence. Although residents recognized expressing uncertainty to a supervisor could identify opportunities for learning and resolve their uncertainty, the negative responses overshadowed the positive responses. Residents highly valued instances in which supervisors were forthcoming about their own uncertainty. CONCLUSIONS: Through participation in simulations with epistemic fidelity, residents reflected on how they perceive and respond to uncertainty in multidisciplinary teams. Our findings emphasize the role of situational characteristics, particularly peers and supervisors, in moderating responses to perceived uncertainty. The productive discussions around responses to uncertainty in debriefing sessions suggest further studies of multidisciplinary simulations could enhance our understanding of how uncertainty is expressed, and potentially be used as an instructional intervention to promote positive responses to uncertainty.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".