From good to excellent: Improving clinical departments’ learning climate in residency training

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.

Using the Postgraduate Hospital Educational Environment Measure to Identify Areas for Improvement in a Singaporean Residency Program

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