LAUSR

Abstract Introduction: The improvement of clinical departments’ learning climate is central to achieving high-quality residency training and patient care. However, improving the learning climate can be challenging given its complexity as a multi-dimensional construct. Distinct representations of the dimensions might create different learning climate groups across departments and may require varying efforts to achieve improvement. Therefore, this study investigated: (1) whether distinct learning climate groups could be identified and (2) whether contextual factors could explain variation in departments’ learning climate performance. Methods: This study included departments that used the Dutch Residency Educational Climate Test (D-RECT) through a web-based system in 2014–2015. Latent profile analysis was used to identify learning climate groups and multilevel modeling to predict clinical departments’ learning climate performance. Results: The study included 1730 resident evaluations. Departments were classified into one of the four learning climate groups: substandard, adequate, good and excellent performers. The teaching status of the hospital, departments’ average teaching performance and percentage of time spent on educational activities by faculty-predicted departments’ learning climate performance. Discussion: Clinical departments can be successfully classified into informative learning climate groups. Ideally, given informative climate grouping with potential for cross learning, the departments could embark on targeted performance improvement.