Development of a taxonomy of unprofessional behavior in clinical learning environments using learner-generated critical incidents

Abstract

Purpose

Few studies have examined medical residents’ and fellows’ (trainees) direct experience of unprofessional behavior in clinical learning environments (CLE). The purpose of this study was to create a taxonomy of unprofessional behavior in CLEs using critical incidents gathered from trainees.

Method

In step 1 (data collection), the authors collected 382 critical incidents from trainees at more than a dozen CLEs over a six-year period (2013–2019). In step 2 (model generation), nine subject matter experts (SMEs) sorted the incidents into homogenous clusters and this structure was tested with principal components analysis (PCA). In step 3 (model evaluation), two new groups of SMEs each re-sorted half of the incidents into the PCA-derived categories.

Results

A 13-component solution accounted for 62.46% of the variance in the critical incidents collected. The SMEs who re-sorted the critical incidents demonstrated good agreement with each other and with the 13-component PCA solution. The resulting taxonomy included 13 dimensions, with 48.7% of behaviors focused on displays of aggression or discriminatory conduct.

Conclusions

Critical incident methodology can provide unique insights into the dimensionality of unprofessional behavior in the CLE. Future research should leverage the taxonomy created to inform professionalism assessment development in the CLE.

Keywords:

• Professionalism
• assessment
• methods

Disclosure statement

No potential conflict of interest was reported by the author(s).

Glossary

Principal Component Analysis, or PCA: Is a dimension-reduction method for identifying patterns in data, and highlighting that data in order to identify similarities and differences. It can be used to reduce the dimensionality of large data sets by transforming a large set of variables into a smaller one that still contains most of the information in the large set. Since identifying patterns in raw data can be difficult, PCA can be a very powerful technique for helping to make sense of large data sets.

Additional information

Notes on contributors

Michael J. Cullen

Michael J. Cullen, PhD, is senior director of assessment, evaluation and research for graduate medical education, University of Minnesota Medical School, Minneapolis, Minnesota.

Charlene Zhang

Charlene Zhang, PhD, is a graduate student, Department of Psychology, University of Minnesota–Twin Cities, Minneapolis, Minnesota.

Taj Mustapha

Taj Mustapha, MD, is assistant professor, Departments of Internal Medicine and Pediatrics, and director of clinical coaching, University of Minnesota Medical School, Minneapolis, Minnesota.

Ezgi Tiryaki

Ezgi Tiryaki, MD, is associate professor, Department of Neurology, University of Minnesota Medical School, and associate chief of staff for education, Minneapolis Veterans Affairs (VA) Health Care System, Minneapolis, Minnesota.

Brad Benson

Brad Benson, MD, is professor, Departments of Internal Medicine and Pediatrics, University of Minnesota Medical School, and chief academic officer, M Health Fairview, Minneapolis, Minnesota.

Mojca Konia

Mojca Konia, MD, PhD, is professor and vice chair of education, Department of Anesthesiology, University of Minnesota Medical School, Minneapolis, Minnesota.

Paul R. Sackett

Paul R. Sackett, PhD, is professor of psychology, Department of Psychology, University of Minnesota–Twin Cities, Minneapolis, Minnesota.

Susan M. Culican

Susan Culican, MD, PhD, is professor, Department of Ophthalmology and Visual Neurosciences, and associate dean for Graduate Medical Education, University of Minnesota Medical School, Minneapolis, Minnesota.