On-Call Workload Differences in Neurosurgery: Resident Call "Weather" and a Departure from Colored Cloud Labels

BACKGROUND AND OBJECTIVES: Labeling residents as "black" or "white" clouds based on perceived or presumed workloads is a timeworn custom across medical training and practice. Previous studies examining whether such perceptions align with objective workload patterns have offered conflicting results. We assessed whether such peer-assigned labels were associated with between-resident differences in objective, on-call workload metrics in three classes of neurosurgery junior residents. In doing so, we introduce more inclusive terminology for perceived differences in workload metrics. METHODS: Residents were instructed to complete surveys to identify "sunny", "neutral", and "stormy" residents, reflecting least to greatest perceived workloads, of their respective classes. We retrospectively reviewed department and electronic medical records to record volume of on-call work over the first 4 months of each resident's 2nd postgraduate academic year. Inter-rater agreement of survey responses was measured using Fleiss' kappa. All statistical analyses were performed with a significance threshold of P < .05. RESULTS: Across all classes, there was strong inter-rater agreement in the identification of stormy and sunny residents (Kappa = 1.000, P = .003). While differences in on-call workload measures existed within each class, "weather" designations did not consistently reflect these differences. There were significant intraclass differences in per shift consult volume in two classes ( P = .035 and P = .009); however, consult volume corresponded to a resident's weather designations in only one class. Stormy residents generally saw more emergencies and, in 2 classes, performed more bedside procedures than their peers. CONCLUSION: Significant differences in objective on-call experience exist between junior neurosurgery residents. Self- and peer-assigned weather labels did not consistently align with a pattern of these differences, suggesting that other factors contribute to such labels.