ABSTRACT
Health information can be difficult to understand, and physician analogies might enhance patient understanding. The present study investigated if physician analogies enhance participants’ objective and perceived understanding, and perceptions of clarity. The experiment consisted of a 2 (familiar/unfamiliar health condition) x 4 (no analogies, diagnosis analogies, treatment analogies, both analogies) design with a within-subjects component of delivery format (video/vignette). An actor physician delivered a video message, diagnosing participants with a health issue. Then participants read a vignette of another physician diagnosing them with the other health issue. Participants were asked if healthcare provider analogies are helpful, and in which medical situations they are helpful. Though no main effects were found for analogies enhancing understanding-related variables, explanations containing analogies in vignette form resulted in increased objective understanding. Additionally, most participants indicated provider analogies are useful, especially when describing complex health issues.
Acknowledgments
We wish to express our appreciation to Mia I. Switzer for her help with coding responses and resolving coding agreements. We would also like to thank Dr. W. Bart Collins, Dr. Philip Edward Keller, and Dr. Maria K. Venetis, for their guidance and feedback on the project.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1. Only participants familiar with hypertension and unfamiliar with pneumothorax were recruited for the study to ensure fidelity with the hypothesis.
2. The vignettes use the same script as the videos.
3. Only percent agreement is reported for the “Group” and “Not Serious” categories (97.02% for “Group” and 98.88% for “Not Serious”) because they were infrequently occurring codes.
4. Because race/ethnicity was measured in a “select all that apply” format for the pilot and main study data, the percentages equal more than 100%.
5. Pillai’s Trace is reported for any analyses in which the Box’s M test is statistically significant because it is recommended in this situation (Warner, Citation2013).
Additional information
Funding
Notes on contributors
Grace M. Hildenbrand
Grace M. Hildenbrand, Ph.D. (Purdue University, 2021), is an assistant professor of leadership studies at Louisiana State University in Shreveport.
Evan K. Perrault
Evan K. Perrault, Ph.D. (Michigan State University, 2014), is an associate professor of health communication in the Brian Lamb School of Communication at Purdue University.