The current study examined how assessments of copresence in an immersive virtual environment are influenced by variations in how much an embodied agent resembles a human being in appearance and behavior. We measured the extent to which virtual representations were both perceived and treated as if they were human via self-report, behavioral, and cognitive dependent measures. Distinctive patterns of findings emerged with respect to the behavior and appearance of embodied agents depending on the definition and operationalization of copresence. Independent and interactive effects for appearance and behavior were found suggesting that assessing the impact of behavioral realism on copresence without taking into account the appearance of the embodied agent (and vice versa) can lead to misleading conclusions. Consistent with the results of previous research, copresence was lowest when there was a large mismatch between the appearance and behavioral realism of an embodied agent.

Objective This study examined the independent effect of a patient's weight on medical students' attitudes, beliefs, and interpersonal behavior toward the patient, in addition to the clinical recommendations they make for her care. Design Seventy-six clinical-level medical students were randomly assigned to interact with a digital, virtual female patient who was visibly either obese or non-obese. Methods Interactions with the patient took place in an immersive virtual clinical environment (i.e., virtual reality) which allowed standardization of all patient behaviors and characteristics except for weight. Visual contact behavior was automatically recorded during the interaction. Afterward, participants filled out a battery of self-report questionnaires. Results Analyses revealed more negative stereotyping, less anticipated patient adherence, worse perceived health, more responsibility attributed for potentially weight-related presenting complaints, and less visual contact directed toward the obese version of a virtual patient than the non-obese version of the patient. In contrast, there was no clear evidence of bias in clinical recommendations made for the patient's care. Conclusion Biases in attitudes, beliefs, and interpersonal behavior have important implications because they can influence the tone of clinical encounters and rapport in the patient-provider relationship, which can have important downstream consequences. Gaining a clear understanding of the nature and source of weight bias in the clinical encounter is an important first step toward development of strategies to address it.

Multilevel interventions can be uniquely effective at addressing minority health and health disparities, but they pose substantial methodological, data analytic, and assessment challenges that must be considered when designing and applying interventions and assessment. To facilitate the adoption of multilevel interventions to reduce health disparities, we outline areas of need in filling existing operational challenges to the design and assessment of multilevel interventions. We discuss areas of development that address overarching constructs inherent in multilevel interventions, with a particular focus on their application to minority health and health disparities. Our approach will prove useful to researchers, as it allows them to integrate information related to health disparities research into the framework of broader constructs with which they are familiar. We urge researchers to prioritize building transdisciplinary teams and the skills needed to overcome the challenges in designing and assessing multilevel interventions, as even small contributions can accelerate progress toward improving minority health and reducing health disparities. To make substantial progress, however, a concerted and strategic effort, including work to advance analytic techniques and measures, is needed.