The effects of trust in physician on self-efficacy, adherence and diabetes outcomes

Trust in physicians has been associated with a range of patient behaviors. However, previous research has not focused on the mechanisms by which trust affects health outcomes and mostly has made use of self-rated health. This study tested a theoretical model of variables influencing the relations of trust to both objective and self-rated health. We hypothesized that patients who trust their physicians more were likely to have stronger self-efficacy and outcome expectations. We expected this, in turn, to be associated with better treatment adherence and objective health outcomes. In addition, we hypothesized that highly trusting patients would be more likely to report better health status through enhanced self-efficacy. Data for this research came from a sample of 480 adult patients with type 2 diabetes in Taiwan. Patients completed measures of trust, self-efficacy, outcome expectations, adherence, and the SF-12 health survey. Objective outcomes, including body mass index, glycosylated hemoglobin, blood lipid, and diabetes-related complications, were assessed by follow-up chart review. The structural equation analyses which were implemented by LISREL VIII resulted in a proper solution that exhibited adequate fit. All hypothesized paths were statistically significant and in the predicted directions. The mediation roles of self-efficacy and outcome expectations were further confirmed by the results of structural equation modeling and bootstrap analyses. In the multivariate regression, although the relations of patient trust to blood lipid and self-rated health were confirmed, the direct link of trust to glycosylated hemoglobin was only significant in the bivariate model. This study clarifies the association of trust with different types of health outcomes and provides the empirical evidence that trust in physicians is associated with both self-rated health and therapeutic response. However, a more longitudinal study design is necessary to precisely determine both the strength and causality of these relationships.