Everyday expertise in self-management of diabetes in the Dominican Republic: Implications for learning and performance support systems design
Abstract
An epidemic such as diabetes is an extremely complex public health, economic and social problem that is difficult to solve through medical expertise alone. Evidence-based models for improving healthcare delivery systems advocate educating patients to become more active participants in their own care. This shift demands preparing chronically ill patients with the competencies of avid lifelong learners who can harness their experiences living with the disease to develop everyday expertise in its self-management. No prior research to date has endeavored to study and support patient everyday expertise development from the learning and human performance systems design field. Moreover, studies of everyday expertise development in the self-management of diabetes by patients in the Dominican Republic are unknown. This exploratory and descriptive study examined patients with varying levels of expertise in order to: 1) Compare them in terms of demographics, health information and behaviors, and depth of knowledge to solve self-management problems, 2) Identify patterns that differentiate them in reference to three major observational learning concepts: live, verbal and symbolic modeling. Participants were 20 diabetic patients participating in a community-based diabetic education program. Data were collected through a semi-structured scenario-based interview that leveraged cognitive probing as a knowledge elicitation method. Analysis of the data involved both quantitative and qualitative approaches and the use of rubrics and multi-case study comparison. Results revealed that everyday expertise development level was associated with patient educational background and years since diagnosis. In terms of health behaviors, the more expert group reported visiting the doctor and measuring their blood glucose levels more frequently. Among diet, exercise and stress, exercise represented the most difficult self-management task for the less expert group to manage on a daily basis but not for the more experts. The more expert group applied significantly higher instances of deeper knowledge across self-management scenarios but was similar to the less expert group in the application of surface knowledge. As for observational learning patterns the former group had access to learn from live models, reacted differently to verbal instructions from doctors, and were more purposeful about consuming health information through media and other sources of symbolic modeling. A major contribution of this research with practical applications in this context consists of a set of ecologically based learning and performance support guidelines for facilitating everyday expertise development. Conclusion, implications for a wide range of stakeholders, limitations and future research are also discussed.
Degree
Ph.D.
Advisors
Schaffer, Purdue University.
Subject Area
Health education|Educational technology
Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server.