A visual impairment can affect cognitive, emotional, neurological, and physical development. Visual impairment impairs reading speed and comprehension, and is often mistaken for a learning disability. Learning is accomplished through complex and interrelated processes, one of which is vision. As a result, visual impairments limit the range of experiences and kinds of information to which one is exposed. A reliance on visual cues in health and risk messages intensifies these effects with regard to health information. The millions of children and adults who are affected by visual impairments worldwide thus require specific consideration regarding how best to make health information accessible for them. The reliance on caretakers to address the health information needs of those living with visual impairments violates their privacy and threatens their emotional well-being. Technological and modality advances that rely on touchscreens that lack tactile or auditory cues marginalize a broad segment of society that is in need of gateways to overcome barriers to accommodating visual impairment. In designing strategic health and risk messages, consideration should be given to this scope of possible limitation and its implications for access to and processing of health and risk information. Health and risk message designers should understand both the realities of challenges to accessing information for the visually impaired and strategies for addressing these realities and the scope of the issue worldwide and across the lifespan.
Marla L. Moon
Brenda L. Berkelaar and Millie A. Harrison
Organizational socialization is the process by which people learn about, adjust to, and change the knowledge, skills, attitudes, expectations, and behaviors needed for a new or changing organizational role. Thus, organizational socialization focuses on organizational membership, which includes how people move from being outsiders to being insiders and how people move between organizational roles within and across organizations over time. To date, research has focused on how employment organizations encourage newcomers to align with existing role expectations via tactics that encourage assimilation. However, organizational socialization is a dynamic process of mutual influence. Individuals can also influence and shape the organization to align with their desires, via personalization tactics. Thus, organizational socialization describes the process by which an individual assumes a new or changing role in ways that meet organizational and individual needs. Most research on organizational socialization focuses on how newcomers enter paid work environments. Researchers often focus on the tactics organizations use to encourage people to assimilate into the organization during the early or entry stage. Less attention has been given to the later stages of organizational socialization (active participation, maintenance, exit, and disengagement), non-work organizations, and transitions between roles within an organization. However, a growing body of research is considering organizational socialization into volunteer roles, new or changing roles, and later stages of socialization such as exit and disengagement. Scholars and practitioners also increasingly recognize how individual, organizational, contextual, and technological factors (e.g., socioeconomic status, race, gender, new information and communication technologies, time, and boundaries) may alter how organizational socialization works and with what effects—thereby offering insight into the underlying processes implicated in organizational socialization. Future areas of research related to context, time, boundaries, communication, and the ethics of organizational socialization are highlighted.
Steven H. Kelder, Deanna M. Hoelscher, and Ross Shegog
Social cognitive theory (SCT) is an action-oriented approach to understanding the personal cognitive, environmental, and behavioral influences on behaviors and to developing theory-based interventions to improve the health status and inequities of societies. It has broad applications across a diverse array of health-enhancing and health-compromising behaviors and has been used successfully in a variety of cultures, with many different intervention methods. Social cognitive theory provides empirically based concepts to explain health behavior and provides useful constructs and processes with which to design interventions. Intervention design using SCT typically follows a sequence of information gathering and project development steps. Literature about the magnitude of the heath problem, its’ risk factors, and the success of previous intervention attempts is carefully reviewed and summarized. The review is presented to the community and qualitative and quantitative assessments are undertaken with the recipients of the intervention to identify the most salient and powerful SCT constructs that are associated with the targeted behavior. Taken together, these preliminary data are then used in the application of SCT constructs for intervention design. Given the recognized differences in how SCT constructs manifest with different ethnic and cultural groups, the careful delineation, tailoring, measurement, and application of these constructs are critical for successful interventions.
Since the 1990s there has been an increasing interest in knowledge, knowledge management, and the knowledge economy due to recognition of its economic value. Processes of globalization and developments in information and communications technologies have triggered transformations in the ways in which knowledge is shared, produced, and used to the extent that the 21st century was forecasted to be the knowledge century. Organizational learning has also been accepted as critical for organizational performance. A key question that has emerged is how knowledge can be “captured” by organizations. This focus on knowledge and learning demands an engagement with what knowledge means, where it comes from, and how it is affected by and used in different contexts. An inclusive definition is to say that knowledge is acquired theoretical, practical, embodied, and intuitive understandings of a situation. Knowledge is also located socially, geographically, organizationally, and it is specialized; so it is important to examine knowledge in less abstract terms. The specific case engaged with in this article is knowledge in hazardous industry and its role in industrial disaster prevention. In hazardous industries such as oil and gas production, learning and expertise are identified as critical ingredients for disaster prevention. Conversely, a lack of expertise or failure to learn has been implicated in disaster causation. The knowledge needs for major accident risk management are unique. Trial-and-error learning is dangerously inefficient because disasters must be prevented before they occur. The temporal, geographical, and social scale of decisions in complex sociotechnical systems means that this cannot only be a question of an individual’s expertise, but major accident risk management requires that knowledge is shared across a much larger group of people. Put another way, in this context knowledge needs to be collective. Incident reporting systems are a common solution, and organizations and industries as a whole put substantial effort into gathering information about past small failures and their causes in an attempt to learn how to prevent more serious events. However, these systems often fall short of their stated goals. This is because knowledge is not collective by virtue of being collected and stored. Rather, collective knowing is done in the context of social groups and it relies on processes of sensemaking.
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.