JOHN BUCHOLTZ is an adjunct assistant professor and teaches courses in social welfare policy. He serves as a subject matter expert with the USC Experts Directory on issues of federal policy for veterans and their families as well as military trauma. He received his master’s degree in social work from the University of Denver and served as a military social work officer with the U.S. Air Force.
He is a board certified clinical social worker with the Veterans Affairs Medical Center in San Diego and is completing his PhD in Social Work at Smith College’s School for Social Work. He is using Millennium Cohort Family Study data for dissertation research into social environmental effects of military catastrophic injuries among lower socioeconomic status veterans and their families.
Under the President’s Commission on Care for America’s Wounded Warriors, Bucholtz was appointed a federal recovery coordinator. He served as a federal junior executive managing obstacles to care in the U.S. Department of Defense and the U.S. Department of Veterans Affairs for service members and their caregivers recovering from traumatic brain injury, post-traumatic stress disorder, and other injuries and illnesses of war.
In 2010, he established American Military Muscle, a nonprofit organization for military veterans that utilizes various evidence-based experiential therapies, cognitive behavioral interventions and structured activities familiar to those who have served in the armed forces. These activities can enhance psychological and physical healing by developing adaptive connections that transfer learning and accomplishment from these specific experiences to other useful life strengths.
University of Denver: M.S.W. 1998
University of Colorado Boulder: B.A. 1989
Areas of Expertise (9)
Industry Expertise (7)
Articles & Publications (1)
Barbara Probst, John Bucholtz
Thematic coding is a common form of qualitative analysis, yet identification of “codes” can limit understanding by directing the researcher to formulated categories rather than inviting exploration of what else the data may be trying to convey. Since categorization is an essential component of language and can scarcely be avoided, the purpose of this paper is to propose a polyphonic approach as a way to add texture and nuance. In polyphonic coding, text is coded several times along various dimensions of interest; these independently coded versions are then superimposed to identify patterns and relationships, allowing a multi-lensed view of the data as both forest and trees.