Browsing by Author "Mzumara, Howard R."

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    Development of the CoMac Adherence Descriptor™: a linguistically-based survey for segmenting patients on their worldviews
    (Dove Medical Press, 2015) Connor, Ulla M.; Mac Neill, Robert S.; Mzumara, Howard R.; Sandy, Robert; Department of English, School of Liberal Arts
    Nonadherence to prescribed medication and healthy behaviors is a pressing health care issue. Much research has been conducted in this area under a variety of labels, such as compliance, disease management and, most recently, adherence. However, the complex factors related to predicting and, more importantly, understanding and explaining adherence, have nevertheless remained elusive. However, through an in-depth linguistic analysis of patient talk, the International Center for Intercultural Communication (ICIC) at Indiana University has produced a psycholinguistic coding system that uses patients' own language to cluster them into distinct groups based on their worldviews. ICIC's studies have shown, for example, that patients reveal their fundamental perceptions about themselves and their environment in their life narratives; clustering of individual patients based on these different perceptions is possible via the use of differential language in survey questions, and differential language can be used to tailor messages for individual patients in a manner that these individuals prefer over generically worded communication. In grant-funded research, an interdisciplinary team of researchers at the ICIC reviewed the literature and identified three basic psychosocial tenets related to adherence: control orientation, based on locus of control research; agency, based on self-efficacy; and affect or attitude and emotion. These three constructs were selected because, in the published literature, they have been consistently found to be connected to patient adherence. Based on this research, a survey, the CoMac Descriptor™ was developed. This report shows that The Descriptor™ questions and responses are valid and reliable in segmenting patients across psychosocial constructs, which will have positive implications for health care providers and patients.