Quantifying the qualitative: exploring epistemic network analysis as a method to study work system interactions

Abstract

Studying interactions faces methodological challenges and existing methods, such as configural diagramming, have limitations. This work demonstrates Epistemic Network Analysis (ENA) as an analytical method to construct configural diagrams. We demonstrated ENA as an analytical tool by applying this method to study dementia caregiver work systems. We conducted 20 semi-structured interviews with caregivers to collect caregiving experiences. Guided by the Patient Work System model, we conducted a directed content analysis to identify work system components and used ENA to study interactions between components. By using ENA to create configural diagrams, we identified five frequently occurring interactions, compared work system configurations of caregivers providing care at home and away from home. Although we were underpowered to determine statistically significant differences, we identified visual and qualitative differences. Our results demonstrate the capability of ENA as an analytical method for studying work system interactions through configural diagramming.

Practitioner summary: A new methodology, Epistemic Network Analysis (ENA), was presented to better support the study of work system interactions through configural diagramming. ENA was applied to qualitative data to demonstrate the capabilities of this method to construct configural diagrams of the work system. This study successfully demonstrated that ENA can visually represent and describe work system configurations.

Keywords:

• Socio-technical system interactions
• ergonomics tools and methods
• configural diagram
• epistemic network analysis
• dementia caregiving

Disclosure statement

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

Additional information

Funding

This work was supported by the National Science Foundation [CISE CHS CRII 1656927], the Wisconsin Alzheimer’s Disease Research Center (NIH National Institute on Aging P30-AG062715), KL2 grant KL2TR002374 through funding from the Clinical and Translational Science Award (CTSA) program through the NIH National Center for Advancing Translational Sciences (NCATS), grant 1UL1TR002373, and grant K76AG060005 from the NIH National Institute on Aging. This project was facilitated by the University of Wisconsin Community-Academic Aging Research Network (CAARN) through funding from the UW School of Medicine and Public Health and from the Clinical and Translational Science Award (CTSA) program, through the NIH National Center for Advancing Translational Sciences (NCATS), grant 1UL1TR002373. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.