Advice is a recommendation for action that includes both suggestions for behavior and ways of feeling and thinking about the problem. It is a ubiquitous phenomenon in personal and professional settings, and functions as a form of both social support and social influence. Advice often improves coping and decision-making outcomes but can also be perceived as intrusive, threaten recipient’s sense of competence and autonomy, and damage relationships. Although advisors often have expertise that can benefit the recipient, advice recipients often discount and underutilize advice, to their disadvantage. Recipients are more likely to utilize advice from advisors they trust, who engender confidence, and who have more expertise or experience. They are also more likely to seek and use it when they have not thought of solutions independently. Recipients who are overconfident, have more expertise, or have more power than an advisor are much less likely to seek and utilize advice. When giving advice, advisors often consider different factors than they would if they were making decisions for themselves, resulting in advice that is more normative and less tailored to individual preferences. Advice can be delivered in a variety of ways, and this stylistic variation has consequences for recipient outcomes. For example, highly direct or blunt forms of advice underscore the advisor’s implicit claim to status and often generate more negative evaluations of the advice and advisor. Advice message content also influences recipients’ advice evaluation. Content that emphasizes efficacy of the action, feasibility, and limitations of the advice tends to improve evaluation and utilization of advice. This research is synthesized in advice response theory (ART), which indicates that advice outcomes are influenced by message content and style, interaction qualities, advisor characteristics, recipient traits, and features of the situation for which or in which advice is sought. Behaviors that co-occur with advice, such as argumentation, emotional support, and planning, also influence outcomes. The sequencing of advice in interaction also matters; the integrated model of advice (IMA) indicates that advice in supportive interactions is best placed after emotional support and problem analysis. The contexts in which advice are given influence the exchange and outcomes of advice. These include personal and professional relationships, in which relational cognitions and professional norms affect the process and outcomes of advising; groups and organizations, in which advising processes become complex due to the multiplicity of relationships, goals, and expectations; cultures, in which advice-seeking and advice-giving varies in perceived appropriateness; and digital environments, which are often valued for advice that is unobtainable elsewhere.
Erina MacGeorge and Lyn Van Swol
Bradford William Hesse
The presence of large-scale data systems can be felt, consciously or not, in almost every facet of modern life, whether through the simple act of selecting travel options online, purchasing products from online retailers, or navigating through the streets of an unfamiliar neighborhood using global positioning system (GPS) mapping. These systems operate through the momentum of big data, a term introduced by data scientists to describe a data-rich environment enabled by a superconvergence of advanced computer-processing speeds and storage capacities; advanced connectivity between people and devices through the Internet; the ubiquity of smart, mobile devices and wireless sensors; and the creation of accelerated data flows among systems in the global economy. Some researchers have suggested that big data represents the so-called fourth paradigm in science, wherein the first paradigm was marked by the evolution of the experimental method, the second was brought about by the maturation of theory, the third was marked by an evolution of statistical methodology as enabled by computational technology, while the fourth extended the benefits of the first three, but also enabled the application of novel machine-learning approaches to an evidence stream that exists in high volume, high velocity, high variety, and differing levels of veracity. In public health and medicine, the emergence of big data capabilities has followed naturally from the expansion of data streams from genome sequencing, protein identification, environmental surveillance, and passive patient sensing. In 2001, the National Committee on Vital and Health Statistics published a road map for connecting these evidence streams to each other through a national health information infrastructure. Since then, the road map has spurred national investments in electronic health records (EHRs) and motivated the integration of public surveillance data into analytic platforms for health situational awareness. More recently, the boom in consumer-oriented mobile applications and wireless medical sensing devices has opened up the possibility for mining new data flows directly from altruistic patients. In the broader public communication sphere, the ability to mine the digital traces of conversation on social media presents an opportunity to apply advanced machine learning algorithms as a way of tracking the diffusion of risk communication messages. In addition to utilizing big data for improving the scientific knowledge base in risk communication, there will be a need for health communication scientists and practitioners to work as part of interdisciplinary teams to improve the interfaces to these data for professionals and the public. Too much data, presented in disorganized ways, can lead to what some have referred to as “data smog.” Much work will be needed for understanding how to turn big data into knowledge, and just as important, how to turn data-informed knowledge into action.
Gary L. Kreps
Ehealth, also known as E-health, is a relatively new area of health communication inquiry that examines the development, implementation, and application of a broad range of evolving health information technologies (HITs) in modern society to disseminate health information, deliver health care, and promote public health. Ehealth applications include (a) the widespread development of specialized health information websites (often hosted by government agencies, health care systems, corporations, professional societies, health advocacy organizations, and other for-profit and nonprofit organizations); (b) the widespread use of electronic health record (EHR) systems designed to preserve and disseminate health information for health care providers, administrators, and consumers; (c) an array of mobile health education and support applications that have often been developed for use with smartphones; (d) mobile health behavior monitoring, tracking, and alerting equipment (such as wearable devices and systems imbedded in vehicles, clothing, and sporting equipment); (e) interactive telemedicine systems for collecting health data and delivering health care services remotely; (f) interactive adaptive tailored health information systems to support health education, motivate health behaviors, and to inform health decision making; (g) online social support groups for health care consumers, caregivers, and providers; (h) health promotion focused digital games to engage consumers in health education and train both providers and consumers about health promoting procedures; (i) dedicated computer portals that can deliver a variety of digital health information tools and functions to consumers, caregivers, and providers; and (j) interactive and adaptive virtual human agent systems that can gather and provide relevant health information, virtual reality programs that can simulate health environments for training and therapeutic purposes, and an ever-increasing number of digital applications (apps) for addressing a range of health conditions and activities. As information technology evolves, new ehealth applications and programs are being developed and introduced to provide a wide range of powerful ehealth systems to assist with health care and health promotion. Ehealth technologies have been found by many researchers, practitioners, and consumers to hold tremendous promise for enhancing the delivery of health care and promotion of health, ultimately improving health outcomes. Many popularly adopted ehealth applications (such as health websites, health care portals, decision support systems, and wearable health information devices) are transforming the modern health care system by supplementing and extending traditional channels for health communication. The use of new ehealth applications enables the broad dissemination of relevant health information that can be personalized to the unique communication orientations, backgrounds, and information needs of individuals. New ehealth communication channels can provide health care consumers and providers with the relevant health information that they need to make informed health care decisions. These ehealth communication channels can provide this information to people exactly when and where they need it, which is especially important for addressing fast-moving and dangerous health threats. Yet, with all the promise of ehealth communication, there is still a tremendous amount of work to be done to make the wide array of new ehealth applications as useful as possible for promoting health with different audiences. This article describes the current state of knowledge about the development and use of HITs, as well as about strategies for improving ehealth communication applications to enhance the delivery of health care and the promotion of public health.