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Abstract

Many qualitative researchers reject textual conversion based on philosophical grounds although others believe it facilitates pattern recognition and meaning extraction. This article examined interview data from 52 physicians from a large academic medical center regarding work–life balance. Analysis ranked men and women in four career tracks: Clinician-Educator, Clinician-Researcher, Clinician-Practitioner, and residents. The purpose of this paper is to illustrate how a qualitatively driven (QUAL→quan) mixed method design illustrated differences between stratified groups. Although many initial codes were similar for men and women, their language was gendered and generational in context of work-life balance. Results indicated that women (and low-status men) expressed fewer strategies to successfully negotiate academic medicine. Quantitizing enhanced the interpretive description of adversity.

Keywords

Mixed Methods, Work Balance, Academic Medicine, Gender, Quantitizing

Author Bio(s)

Carol A. Isaac, PT, PhD, is an assistant professor of research at Mercer University-Atlanta. Here areas of interest are women and leadership in the science, technology, engineering, mathematics and medicine (STEMM), and the use of qualitative methods to explain empirical findings and lived experience within these institutional contexts.

Rebecca McSorley, MSc, MD, Ms. is the Family Practice Specialist in Whiteriver, Arizona. She attended and graduated with honors from University of Wisconsin Medical School in 2012. She affiliates with many hospitals including Summit Healthcare Regional Medical Center, Whiteriver Phs Indian Hospital

Alexandra Schultz, MD, is a pediatric specialist in Chicago, IL. She earned her bachelor's degree from Carleton College, her medical degree at the University of Wisconsin School of Medicine and Public Health in Madison, WI, and completed her pediatric residency through Northwestern University at Ann and Robert H. Lurie Children's Hospital of Chicago.

Acknowledgements

The authors would like to acknowledge the assistance from the following individuals: At UW-Madison: Dr. Molly Carnes for her support of Analysis 1. Abhik Bhattacharya (UW-Madison) and Barbara Lee (Keiser University) for statistical advice.

Publication Date

12-13-2016

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.

 

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