How do residents respond to uncertainty with peers and supervisors in multidisciplinary teams? Insights from simulations with epistemic fidelity
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».