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date: 21 September 2017

Knowledge and Comprehension

Summary and Keywords

Knowledge and comprehension are essential components of an individual’s understanding of a health text. Whether reading a health pamphlet or watching a health campaign in the form of a public service announcement (PSA), or watching edutainment programming, individuals gain knowledge about the health topic being discussed. Knowledge, however, can only be retained if the individual can also comprehend the text or video. Often comprehension in a health context focuses on health literacy or the degree to which individuals can process and understand health information in order to make informed health decisions. Health literacy is commonly viewed in terms of the readability (e.g., reading level, complexity) of the health text or script. However, in order for individuals to gain knowledge and use that knowledge appropriately and effectively in making health decisions, individuals need to comprehend or understand what the text is conveying.

Because comprehension is such an important component of gaining and using health knowledge, we must understand how we store health knowledge in memory. A schema is a mental representation that stores knowledge as interrelated pieces of information. Schemas tend to be a fairly static representation of knowledge. A mental model is a more dynamic mental representation in that we use mental models to process, organize, and comprehend incoming information. In a mental model, there is a correspondence between an external entity and the constructed mental model of that entity that allows people to counterfactually manipulate information and engage in problem solving. A situation model is the most contextualized mental representation because it encompasses a specific event or set of interrelated events. There are several ways in which to examine comprehension processes. One way is to examine the most basic level of comprehension by investigating the importance of language and semantic representation of a text. A more complex way to examine comprehension is to view the activation levels of various words or concepts important in creating a representation of the story structure in memory. One model that specifically examines concept activation is the landscape model. The model posits that greater frequency of activation and the strength of activation of a concept determine the concept’s overall activation level. The higher the activation level of a concept in a text or video, the more likely the concept will be included in the mental representation for the text or video and stored in memory. A third way to study comprehension is to examine how concepts change throughout a text and how the concepts relate to one another. The event-indexing model describes how individuals create situation models based on five dimensions of information: time, space, protagonist, causality, and intentionality. Throughout the process of gaining information, the individual updates the situation models for a text on each of the five dimensions. When events have similar dimensions in common, the events are connected in memory; thus, describing health information with similar dimensions in common (e.g., a protagonist the entire way through the text, events happening in the same amount of time) will be better recalled later. Empirical work on comprehension of both text and video messages has demonstrated the landscape model and event-indexing model’s ability to examine comprehension processes based on the format, language, and organization of the information. Health message design can benefit from utilizing these comprehension models to ensure that knowledge is received by the intended audience and comprehended, and thus able to be used in future experiences.

Keywords: comprehension, mental models, schemas, situation models, information seeking, health literacy, health and risk message design and processing

Introduction

Knowledge is essential to guide our everyday lives. The state of “having knowledge” consists of having information, understanding that information, and having the necessary skills to use that information within a particular domain. Having a strong knowledge of health-related information is obviously important, as our very lives can depend on having accurate knowledge of our health and health-care options. Likewise, our understanding of health risks is tied to our knowledge of that risk. There are several areas of study within the domain of health communication where researchers explore how people obtain and process health information that will be discussed, including health literacy, information seeking, and comprehension of health messages.

Importance of Considering Knowledge in the Study of Health

As previously stated, having knowledge goes beyond simply remembering a litany of facts. Knowledge also requires understanding the linkages between what is already understood and the information presented in relevant health messages. Comprehending information related to prevention behaviors, health screening, prescription details, disease symptoms, nutrition, diagnoses, prognoses, and treatment plans is necessary. Additionally, with all of the modern advances in medicine, it has become all the more necessary for individuals to be able to learn and understand information related to the prevention of illness and the assessment of risk factors. Within the social sciences, researchers are identifying factors that both facilitate and hinder the comprehension of health information. Health literacy has been a major focus in this effort. Often related to health literacy are factors such as age, education level, economic status, race, culture, and religious views; these factors have been shown to influence the extent to which patients are able to acquire and understand health information (Nielsen-Bohlman, Panzer, & Kindig, 2004).

Health Literacy

Health literacy is central to the study of health knowledge and comprehension. The classic definition of health literacy is, “the degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions” (Committee on Health Literacy & Board on Neuroscience and Behavioral Health, 2004). Unfortunately, exactly what constitutes health literacy has changed across time (Pleasant, 2014). Despite the ambiguity as to what health literacy encompasses, research on health literacy has demonstrated that a significant number of individuals lack the cognitive skill set and knowledge to fundamentally understand basic health information. It is estimated that over one-third of the adult population in the United States (i.e., 36%) has basic or below-basic health literacy levels, and as many as 9 out of 10 adults in the United States cannot effectively comprehend or utilize basic health information (Benjamin, 2010; Kutner, Greenberg, Jin, & Paulsen, 2006; Ratzan, 2010).

Historically, the research on health literacy deals with the readability of health and risk texts. Traditionally, there have been two main components in the study of readability: reading level and complexity of text. In most research, reading level has been assessed by determining the academic grade-level equivalent to which one is able to read. Despite high educational attainment in the United States, the average reading skills of adults are estimated to be somewhere between the 7th and 9th grade levels. More recently, the National Assessment of Adult Literacy (NAAL) was developed to go beyond reading levels to assess literacy in other domains and uses four levels to differentiate literacy performance: below basic, basic, intermediate, and proficient (National Center for Education Statistics, 2006).

In terms of either assessment, health information texts often exceed the appropriate level of literacy for the target audience (Ad Hoc Committee on Health Literacy [AHCHL], 1999; Kaphingst et al., 2012; Williams et al., 1995). The mismatch between the literacy level of the target audience and the complexity of the text presents a major barrier to the dissemination of health and risk knowledge and comprehension. It is estimated that approximately 80 million adults in the United States are considered inadequate, or “limited,” in their level of health literacy (Berkman, Sheridan, Donahue, Halpern, & Crotty, 2011). Individuals who are older, members of an ethnic minority, have a lower socioeconomic status (SES), and are less educated have disproportionately higher rates of limited health literacy (Kutner et al., 2006). Additionally, individuals in these socially disadvantaged groups are also more likely to experience lower overall health and greater health risks (Braveman, 2006), and lower levels of health literacy almost certainly contribute to these disparities.

While much of the social scientific research on health literacy has traditionally focused on reading fluency, more recently scholars have argued that this is a limited perspective on health literacy. But as Baker (2006) argues, health literacy involves much more than text readability and includes a person’s ability to find information within texts, understand numeracy including probability and risk, and access to prior health knowledge. Further, it is imperative that individuals are able to comprehend the health information they encounter. In other words, plain language is important, but health practitioners must also be able to present information in a way that enables individuals to effectively draw inferences and create mental models that more adequately represent the problem (Morgan, Fischhoff, Bostrom, & Atman, 2002). Although health literacy is a major barrier to knowledge and comprehension in the health and risk contexts, strides are being made to raise awareness of the issue of health literacy by educating health-care providers, and working with patients in advocacy and counseling settings. Researchers have developed screening tools to assess both the readability of health texts and individual health literacy levels. Over the years, tests such as the Test of Functional Health Literacy in Adults (TOFHLA; Parker, Baker, Williams, & Nurss, 1995), the Health Activities Literacy Scale (HALS; Rudd, Kirsch, & Yamamoto, 2004), and the TALKDOC (Helitzer, Hollis, Sanders, & Roybal, 2012) have been developed to assess health literacy. Unfortunately, the number of measures of health literary has mushroomed, which can make it difficult for knowledge about health literacy to accumulate because the compatibility of the different measures is not well understood (Pleasant, 2014). While much of the extant literature on health literacy argues that it is important to assess and adjust the reading level of health information texts for the target audience (e.g., Brega et al., 2015; Rudd, Kaphingst, Colton, Gregoire, & Hyde, 2004), the available tools for evaluating reading level often require extensive data sets and advanced analysis skills and/or are difficult for physicians to administer in the brief amount of time spent with patients.

While readability is important, comprehension of the information presented is essential, and future research is needed to address the issue of how health texts are comprehended by the audience. The result of comprehension processes is a mental representation of the information. Understanding how the comprehension process influences the representation of health-related information in memory is critical for understanding how that information will later be used (or misused). So while a focus on reading level and understandability is important, it is only one step in understanding how health campaigns translate into people’s knowledge of a health issue.

Furthermore, it is important to keep in mind that the complexity of the information is increasing as well. Synonymous with the increasing number of information channels, the complexity of treatment has grown in recent years due to advances in medicine. For example, hypertension (i.e., high blood pressure) was treated by taking either potassium thiocyanate, barbiturates, bismuth, or bromides; and patients were advised to rest and avoid stress (Moser, 2006). Now, treatment includes several lifestyle changes including implementing healthy eating habits, increased physical activity, weight reduction, dietary sodium reduction, limited alcohol consumption, and stress management coupled with two or more medications including, but not limited to, thiazide-type diuretics (i.e., water or fluid pills), beta blockers, calcium channel blockers, alpha blockers, vasodilators, angiotensin converting enzyme inhibitors, and angiotensin receptor blockers (NIH, National Heart, Lung, and Blood Institute, 2015). This example highlights the fact that contemporary medical advancements require that the patient knows and understands significantly more about their diagnosis, prognosis, and treatment plan. Therefore, health and risk information seeking must increase as well.

Future research should continue to develop more effective and comprehensive ways to increase health literacy and the comprehensibility of complex health and risk information. In addition to health literacy, there are several other barriers that contribute to low health and risk knowledge and comprehension, one of which is one’s SES. Nearly 50 years ago, researchers began exploring knowledge disparities between different social classes. Tichenor, Donohue, and Olien (1970) provided evidence supporting the “knowledge gap hypothesis,” which suggests that as the dissemination of information in the mass media increases, the disparity between SES groups increases. In other words, individuals in higher SES groups are more likely to acquire new information more quickly and as a result, the “knowledge gap” widens over time. In particular to health domains, findings indicate that individuals in higher SES groups are more likely to have a higher knowledge of health-related topics and have a higher propensity for adopting health recommendations (Guttman & Salmon, 2004). Consequently, health communication campaigns can have unintended detrimental effects by reinforcing or widening the knowledge gap as opposed to closing it (Cho & Salmon, 2007). For example, an intervention promoting helmet use during bicycle riding among children was shown to be three times more effective in higher SES neighborhoods than in their lower SES counterparts (Farley, Haddad, & Brown, 1996). Recent research has further explored this phenomenon by identifying two underlying factors contributing to the knowledge gap: health-information-seeking behavior (e.g., Niederdeppe, 2008) and access to health information (e.g., Slater, Hayes, Reineke, Long, & Bettinghaus, 2009).

Seeking Health Information

It would be amiss to talk about health knowledge and comprehension without talking about where individuals go to find health information. According to Brashers, Goldsmith, and Hsieh (2002), uncertainty management goals, different perceptions about an individual’s desire for health information between the patient and the provider, cultural factors such as the social context in which the information is sought, and the available information channels can all influence one’s health information-seeking (and/or avoidance) behavior. Health information is acquired either through incidental exposure to messages (e.g., interventions, campaigns, advertisements) or by actively seeking information. According to Brashers, Goldsmith, and Hsieh (2002), there are two primary channels for health-information seeking: face-to-face encounters (e.g., conversations with physicians and family members) and mediated communication (e.g., the Internet). Due to the increasing complexity of health information (AHCHL, 1999) and the diffusion of the Internet, current trends indicate an increase in patient reliance on the Internet for health and risk information (Massey, 2016).

In 2002, the U.S. National Institutes of Health’s National Cancer Institute developed the Health Information National Trends Survey (HINTS) to biannually assess trends in how individuals seek out health and risk information, among other things (Hesse et al., 2005). Initial findings from the 2002 survey indicate that approximately two-thirds (63.7%) of adults used the Internet to look for some type of health or medical information either for themselves or for someone else. Additionally, the survey highlights a number of sociodemographic factors that influence the use of the Internet for health-information seeking. Using the Internet for health or medical information was generally more common among younger individuals (less than 65 years old); women; those who do not identify as either Black or Hispanic; and those who had higher levels of education and income (Hesse et al., 2005). More recently, data from the 2014 HINTS 4 survey suggests that approximately 84.4% of Internet-using adults in the United States have looked up health information online (Massey, 2016). Consistent with prior research, individuals who are older, male, non-White, and those who have lower education levels use the Internet to seek out health information less than their counterparts. However, trends suggest that those in these marginalized groups are using the Internet more now than in previous years (Massey, 2016).

In addition to looking to the Internet for health information, people turn to their physician(s) for guidance. In this context, effective patient–provider communication is essential. Patient–provider inequalities can hinder one’s knowledge and comprehension of health and risk information. A recent meta-analysis highlights the social class divide between provider and patient and assesses the impact that this socioeconomic divide can have on the communication styles and health outcomes of those involved (Willems, Maesschalek, Deveugele, Derese, & De Maeseneer, 2005). Physicians and health-care professionals are often different from their patients in terms of their level of education, age, and social class. This has been shown to affect patient–provider communication such that patients from lower social classes receive significantly less “positive socio-emotional utterances,” a more physician-dominated communication pattern resulting in less collaboration on health decisions, more technical language and physician question asking, and less diagnostic and treatment information is provided to the patient (Willems et al., 2005). This is problematic, as patients often rely on health information from their health-care providers, especially so if they are of a lower SES (Hesse et al., 2005).

Family members and personal social networks can influence one’s health knowledge as well. According to Makoul (2003), it is important to go beyond the patient–provider relationship and include all members of the “health-care team” when examining communication styles and preferences. This means that any family members who may be involved in the patient’s care should be taken into consideration when educating patients. This is particularly true for individuals whose cultural background is of a community- or family-based tradition (e.g., Chinese, Vietnamese, Ethiopian) such that interactions, including health-information seeking and avoiding, need to involve all parties (Brashers, Goldsmith, & Hsieh, 2002).

Finally, even if all of the abovementioned factors are in the individual’s favor, yet another barrier exists to health knowledge: being aschematic in a new health domain. Imagine that someone is relatively health literate, has the ability and motivation to seek out health information, and has adequate access to health information, but has been recently diagnosed with a disease that they know nothing about. For example, a study by Schmidt et al. (2015) found that women newly diagnosed with breast cancer had several questions based on information needs after their first appointment regarding their current working situation, supplementary neuropathy, and nutrition, among other things. Or, imagine that a parent is told that their child has a developmental disability or other chronic health condition of which they have no previous or personal experience. A recent study found that parents often take on the role of primary caregiver in this scenario, and that “caregivers [engage] in activities that [require] profound interactive and sometimes critical health literacy skills to promote participation, management, and decision-making” (Pizur-Barnekow, Darragh, & Johnston, 2011). Or, perhaps an individual has been managing a chronic illness for years, but a new pharmaceutical treatment has been released and their doctor wants them to alter their medication regimen. Tarn and colleagues (2006) found that physicians often fail to communicate critical information regarding medication use when prescribing new medications, which contributes to patients’ non-adherence to the prescribed regimen. It is imperative that these individuals obtain and understand the implications of such diagnoses, prognoses, and treatment plans. Accordingly, they will often take on the task of seeking out this information on their own. However, simply locating the information will not suffice, as it is imperative that the individual also comprehends and remembers the information.

Several models of information seeking in the health domain have been developed. The Risk Information Seeking and Processing (RISP) model (Griffin, Dunwoody, & Neuwirth, 1999; Griffin, Neuwirth, Dunwoody, & Giese, 2004) has played a major role in guiding research in this area. At a basic level, the RISP model predicts that people will seek information if they judge that a risk is potentially likely to happen to them and it has severe consequences. In that situation, they will be motivated to seek out information. However, if they judge that they have sufficient information, they won’t seek more information. But if they believe they have insufficient knowledge to judge the potential risk, then they will be motivated to seek information. It is the gap between people’s desired level of information and their perception of how much information they actually have that motivates an information search. If the gap is large, people will be more motivated to seek information relevant to the risk. More recently, the Planned Risk Information Seeking Model (PRISM) has extended the RISP model by including variables from the Theory of Planned Behavior such as people’s attitudes toward seeking information, whether people important to them think it is important to seek out information (information-seeking subjective norms), and people’s perceived control over their information-seeking behavior (Kahlor, 2010). Research has generally supported these models across a number of health-related contexts (Hovick, Liang, & Kahlor, 2014; Willoughby & Myrick, 2016). Models like RISP and PRISM were designed to predict both offline and online information searches. However, research suggests that people who have a need to feel more autonomous or in greater control of their environment are more likely to engage in online information searches for health information than people who are not driven by the need to feel autonomous (Lee & Lin, 2016).

It is well established that once information is found, it is encoded into memory so it can be activated from memory if needed. Scholars in psychology have long studied how this encoding process works and how knowledge is transferred to memory and used in future circumstances. The following sections explain how knowledge is represented in memory and can be applied later to similar life circumstances.

Knowledge and Mental Representations

Theorizing in psychology has long held that our understanding of what people know and how they use that knowledge is tied to how information is represented in memory (Schank & Abelson, 1977). Early models of semantic memory hypothesized that information was represented as a node in a network of interconnected concepts. Each node corresponded to a concept (e.g., cigarettes) and that node would be connected to other related nodes in memory (e.g., lung cancer, stress reduction, tobacco, filter). Network models of memory have been compared to dictionaries, which provide definitions of various concepts. For a network, these definitions resulted from the connections to other concepts within the network. But knowledge is more than a dictionary definition and while useful, network models do not, by themselves, seem to capture what is meant by knowledge.

Since the early work on network models of memory, researchers have hypothesized a number of different mental representations that are tied to knowledge. Three different types of mental representations will be discussed here: schemas, mental models, and situation models. These three types of mental representation have a number of similarities. They can be thought of as existing along a continuum of abstractness with schema at one end, mental models in the middle, and situation models at the other end of the continuum (Roskos-Ewoldsen, Davies, & Roskos-Ewoldsen, 2004).

Schemas

The earliest work in this area hypothesized that knowledge is represented in memory as larger mental representations referred to as schemas. A schema is a mental representation in which knowledge is represented as interrelated pieces of information. A metaphor that has been used to capture the distinction between a node in a network and a schema is that a node is more like a dictionary entry whereas a schema is more like an encyclopedia entry. For example, a node for heart disease would basically define heart disease whereas a schema for heart disease may include knowledge of the causes, symptoms, and outcomes of heart disease. Communication scholars have studied schemas due to their influence on people’s memory for information, how they interpret ambiguous information, what they attend to, and behavior (Kunda, 1999).

A number of studies focused on health behaviors have utilized schema theory as a theoretical perspective to guide research. Research has demonstrated that heavier viewing of TV programming influences people’s schemas of health-related behaviors. For example, Kean and Albada (2003) found that TV portrayals of alcohol influence schemas about alcohol use, but the findings suggest that people’s schemas reflect both their own experience with alcohol as well as portrayals of alcohol found in the media.

There has been extensive research testing schema correspondence theory (Brannon & Brock, 1994). Schema correspondence theory focuses on people’s self-schemas. A self-schema is a person’s structured representation of themselves in memory. Research has demonstrated that people’s self-schemas influence how they process messages, how easily they make decisions, and their behavior (Cross & Markus, 1994; Kunda, 1999). Schema correspondence theory hypothesizes that messages that target or are congruent with a person’s self-schema will be more effective. As several studies have demonstrated, adapting a message to the target audience’s self-schema resulted in messages that were perceived as more persuasive. This effect has been demonstrated with messages on binge drinking (Pilling & Brannon, 2007) and AIDS prevention (Brannon & McCabe, 2002). Research indicates that schema matching is more effective because people are more likely to attend to schema consistent messages (Pease, Brannon, & Pilling, 2006) and these messages result in more systematic processing of the message (Brannon & McCabe, 2002), which should translate into a greater likelihood of behaving consistently with the message.

Schema theory came under increasing attack in the late 1980s and early 1990s because of a lack of specificity in the theory (Wyer, 2004). In addition, there was little theorizing about how schemas change across time or how malleable they are to deliberate manipulations involved in everyday reasoning (Johnson-Laird, 1983).

Mental Models

A mental model is a more dynamic mental representation of knowledge than a schema (Roskos-Ewoldsen et al., 2004). We may use these mental models as a way to process, organize, and comprehend incoming information (Zwaan & Radvansky, 1998), make social judgments (Wyer & Radvansky, 1999), formulate predictions and inferences (Magliano, Dijkstra, & Zwann, 1996), or generate descriptions and explanations of how a system operates (Gentner & Stevens, 1983). A key notion of the mental model approach is that there is some correspondence between an external entity and our constructed mental representations of that entity (van Dijk & Kintsch, 1983). However, it is difficult to provide an exact definition of mental models because a mental model is a concept that has been used to capture a broad and diverse range of phenomena. There are a number of phenomena that are explained by “mental models” (Rickheit & Sichelschmidt, 1999).

One type of phenomena focuses on our understanding of complex, natural, or technical systems, as a means of predicting how these systems operate (e.g., McCloskey, 1983). For example, we have mental models of how environmental health threats or certain diseases work. Likewise, Griffin, Loh, and Hesketh (2013) demonstrated that lay people hold complex mental models of factors influencing life expectancy and that these factors interact to influence their own life expectancy. While these lay mental models identify many of the same variables that are actually used to predict life expectancy, they tend to weight information differently and there are important differences between the lay and expert models of life expectancy.

Another domain studied under the rubric of mental models focuses on deductive reasoning. In this case, mental models function to help a person formulate conclusions relative to some premise (e.g., Johnson-Laird, 1983). Research in this domain focuses on people’s mental models of how complex systems operate. Given the malleability of a mental model, research has demonstrated that people manipulate their mental models of complex systems to aid in decision making. This basis for theorizing about mental models has been used extensively in the health domain. One example of how the mental model approach is used studying decisions in the health domain includes work on pharmacists. Several studies have demonstrated that pharmacists have complex mental models that influence their interactions with clients obtaining prescriptions and their giving of advice to clients. These models reflect characteristics of the patients, the medicines, the environment, prior interactions with the patient, and the pharmacist’s relationship with the patient (Chui, Stone, Martin, Croes, & Thorpe, 2013; Witry & Doucette, 2015). Likewise, Burtscher, Kolbe, Wacker, and Manser (2011) demonstrated that when members of an anesthesia team had shared mental models of surgery and anesthesia, they work together more successfully. From a communication perspective, shared mental models may well reflect an improved ability to communicate with each other due to the overlapping understanding of the task they are performing together (Roloff & van Swol, 2007).

But perhaps the area where mental models have been utilized the most in this area is the designing of risk communication campaigns. As Morgan and colleagues (2002) demonstrated, when compared to expert mental models of health risk, lay people often have inaccurate or deficient mental models of how various health risks actually operate (see also Chowdbury, Haque, & Driedger, 2012; Newman, Seiden, Roberts, Kakinami, & Dunn, 2009). Morgan et al. (2002) reasoned that campaigns that addressed these deficiencies should be more successful at changing people’s decision making about a risk factor. Indeed, research has found that health campaigns that address the deficiencies of the lay publics’ understanding of a risk factor are successful (McComas, 2006; Morgan et al., 2002).

A final type of phenomena addressed by theorizing on mental models focuses on the mental representations of real or imagined situations. According to van Dijk (1995), these situation models are constructs in memory that represent what a situation or event described in a text is about, rather than a literal representation of the text itself (see also Wyer, 2004). This approach to mental models emerged from two areas of research: how people understand events and text comprehension. Much of the work on comprehension has focused on the relationship between mental models and situation models, which is what we discuss next.

Situation Models

A situation model is a representation of a specific event or set of higher interrelated events that has specific temporal and spatial constraints (Wyer, 2004). A situation model involves integrating existing knowledge with new incoming information to form a coherent mental representation that makes sense of the interconnected concepts and ideas. Situation models have typically been studied within the realm of text comprehension (van Dijk & Kintsch, 1983). For example, consider the “Histories” episode of the television show House (episode 10, season 1). This episode revolves around the diagnosis of a homeless woman who is suffering from seizures. One of the situation models found in the episode would include the cardboard box where the homeless “Jane Doe” (played by Leslie Hope) lived. Another situation model would include House’s office where he always meets with his team of assistants. Although this example utilizes a television show to present what situation models look like, autobiographical memories can include situation models, as well (Wyer, 2004).

Obviously, situation models and mental models are highly related. A mental model is a more abstract representation of a series of related stories. Like a situation model, a mental model has temporal and spatial constraints, but these constraints will typically be looser. A mental model of House would take place throughout the later part of the first decade of the 2000s and would be set primarily in and around Princeton-Plainsboro Teaching Hospital, but includes other parts of the city like House’s apartment, Dr. Lisa Cuddy’s (i.e., House’s boss) apartment, and other locations in the town. Importantly, situation and mental models represent knowledge about some event or events. A schema is a more abstract representation that is knowledge of something (Markman, 1999). For example, a schema for “medical dramas” would include limited temporal or spatial information. Rather, the schema would include information about what the important elements of a typical medical drama are (e.g., an ill victim, an unknown cause of the illness, someone trying to solve the medical mystery, or the possibility that the victim will die). Of course, astute readers will note that one of the classic examples of a schema—the restaurant schema—includes temporal information such as you pay for the food after you order it. However, although there may be temporal or spatial information about events within a schema, the schema itself is not contextualized within a specific time or place.

Despite their differences, situation and mental models have some similarities. First, they are malleable. Norman (1983) has observed that mental models are incomplete, lack clearly defined boundaries, and even contain errors. Similarly, Wyer and Radvansky (1999) argue that the components of a situation or mental model are interchangeable––much like building blocks can be used to construct various shapes. This characteristic of models distinguishes them from other approaches to cognition, such as schemas. Schemas are typically theorized to be more static and rigid. Likewise, network models posit that knowledge is stored in nodes and, when stimulated, they activate closely related nodes. This results in the heightened accessibility of related nodes, but the linkages between nodes are more rigid and less amenable to change. A mental model or situation model may be activated by similar processes as a node in a network, but once brought to mind these models interface with other knowledge structures in a much more dynamic way. Thus, a critical characteristic of situation and mental models is that they are dynamic. That is, they are subject to user control and may be manipulated to generate inferences, test different scenarios, or draw conclusions about information that may or may not be contained in a text or situation. For example, when engaged in conversation, people construct a model of the communication situation and make online inferences about what they think others believe and intend; only a portion of these inferences are transferred to longer-term memory (Dickinson & Givon, 1997). As another example, viewers of medical dramas may use cinematic features (e.g., editing techniques, costumes, music, or dialogue) as cues to make predictions about future events or to make inferences about previous events. When anomalous information is foregrounded by filmmakers, viewers attempt to find out why such information is presented. These predictions are generated through the manipulation of situation models that viewers create as they watch a film (Magliano, Dijkstra, & Zwann, 1996).

Knowledge and the resulting mental representations can be helpful in comprehending what a health pamphlet, public service announcement, or television show might be communicating to an audience. In various fields, scholars have attempted to explain how comprehension processes operate and have developed models to better understand these processes both within a text or video and in the mind of the reader or viewer. This next section examines advances in comprehension literature as pertaining to audience comprehension of written and audiovisual information.

Comprehension Processes

Comprehension in health communication is often studied as an important outcome variable that can be improved upon by manipulating health texts in certain ways. For example, researchers have found that including pictures in health texts increases recall of health information, particularly for individuals with low literacy skills (Houts, Doak, Doak, & Loscalzo, 2006). Another study argued for implementing a “fact box” into pamphlets as a simple tool for communicating risks and benefits of health interventions (McDowell, Rebitschek, Gigerenzer, & Wegwart, 2016). Although these assessments for possible design implementations are important to consider in identifying how comprehension can be improved upon in health texts, it is equally important to rely on theories of comprehension to determine if current health texts are adequately relaying information to the public that can be stored in memory and recalled later.

Text comprehension, in general, has been studied in depth, particularly by cognitive psychologists (van den Broek, Risden, Fletcher, & Thurlow, 1996; Zwaan, Langston, & Graesser, 1995). Numerous theories of comprehension have been proposed and tested to explain how elements of a text or visual story can influence comprehension processes (van den Broek, Risden, & Husebye-Hartman, 1995; Zwaan et al., 1995).

The literature focuses on three possible levels of comprehension (van Dijk & Kintsch, 1983). The first level examines the specific words or syntax used within a text, and the resulting semantic representation in establishing coherence. The second level examines the activation of those words to create a representation of the story structure in the reader’s memory and sheds light on the importance of concepts embedded with the text; this level of comprehension is often examined via the landscape model of comprehension (van den Broek et al., 1996). The third level examines how concepts are included in a situation model and how the model changes across a text or how concepts within a story relate to one another; this level of comprehension is often examined via the event-indexing model of comprehension (Zwaan et al., 1995). Although all three levels are important to the understanding of comprehension processes, we will focus on the second and third levels of comprehension, specifically from the perspective of the dominant theories explaining the cognitive processes at these levels.

The Landscape Model

The landscape model rests on the understanding that concepts and propositions within a text will differ in their level of activation when a text is being processed (van den Broek, Young, Tzeng, & Linderholm, 1999). Thus, the focus of the model is on the activation levels of different concepts across a story and the resulting representation in memory. By understanding the relationship between the activation levels of concepts while the story is being processed and the mental representation in memory, the landscape model posits that greater levels of activation for a particular concept during reading results in better memory for the concept later.

The landscape model of comprehension can be activated by information stemming from four different sources (van den Broek et al., 1996, 1999). First, the immediate environment (e.g., a sentence in a book or scene in a movie) will activate concepts in memory. Second, because activation dissipates across time, central concepts from the immediately preceding sentence or scene should still be activated, albeit at a lower level of activation. Third, earlier concepts activated in the story may be reactivated when they are necessary for maintaining the story coherence. Fourth, world knowledge necessary for story understanding will be activated. According to the landscape model, the cognitive representation of the story will reflect these four sources of activation. In addition, the representation of the story will reflect concepts that are co-active while processing the study. Van den Broek and colleagues (1995) suggest that the measure of concept association is a good predictor of memory because simultaneously activated concepts lead to more connections in memory (Wyer, 2004). Tests of the landscape model suggest that the model does an exceptional job of predicting people’s memory for a text (Lee, Roskos-Ewoldsen, & Roskos-Ewoldsen, 2008; van den Broek et al., 1996, 1999).

The landscape model started as a comprehension model for text, and has been successfully applied to reading of entertainment (e.g., Linderholm & van den Broek, 2002), scientific texts (e.g., van den Broek, Kendeou, Sung, & Chen, 2003), and health texts (Anderegg, Kennard, & Ewoldsen, 2015).

In examining health texts, researchers have examined how the landscape model can predict comprehension for health pamphlets focused on autism spectrum disorders (Anderegg, Kennard, & Ewoldsen, 2015). Eight variations of a pamphlet on childhood development and autism were created that differed in terms of the absence or presence of a specific protagonist, explicit context (i.e., stating that the pamphlet concerned autism spectrum disorders), and the form of the writing (i.e., a narrative versus an expository, fact-listing text). The landscape model significantly explained 72.2% to 86.2% of the variance in participant recall across pamphlets. This finding led the authors to encourage health text designers to use comprehension models, like the landscape model, to test whether the information provided can be stored and recalled by readers before the texts are distributed to the public.

More recently, the model has been extended to the comprehension of audiovisual discourse, such as to predict memory of brand placement in films (Yang & Roskos-Ewoldsen, 2007), recall of short television news stories (Lee et al., 2008), and recall of short entertainment television clips (Anderegg, Aladé, Ewoldsen, & Wang, 2014). Within these contexts, the original landscape model was extended by incorporating elements of dual coding theory (DCT; Paivio, 1991). DCT assumes that visual and auditory information have independent yet interacting representations in memory (Lee et al., 2008; Paivio, 1991). This perspective extends the landscape model to account for representations that only occur in one channel (i.e., visual or auditory). Again, tests of the dual code landscape model have provided strong support for the ability of the model to predict people’s memory for audiovisual stories (Anderegg et al., 2014; Lee et al., 2008).

Although the landscape model is important in understanding which concepts within a text are activated and connected, it is silent as to what types of concepts can affect situation model formation. A text can contain literally thousands of concepts, but certainly most of these concepts are not represented in memory. Additionally, the landscape model is not able to account for the types of dimensions that are most important in a reader’s creation of a mental model of a text. The event-indexing model of comprehension attempts to answer these questions (Zwaan et al., 1995).

The Event-Indexing Model

Zwaan and colleagues (1995) proposed the event-indexing model to describe how individuals comprehend narratives and create situation models of narrative events. The model posits that there are five independent dimensions of event information that play an important role in comprehension and situation model creation: time, space, protagonist, causality, and intentionality. The time dimension refers to when (i.e., what period of time) an event takes place in the story world, whereas space refers to where (i.e., a location in the story world) an event takes place. The protagonist dimension refers to the main characters involved in the narrative action. Causality refers to whether a story event is related to a previous story event, and intentionality (or motivation) is related to the protagonist’s goals (Magliano, Miller, & Zwaan, 2001; Zwaan, 1999; Zwaan et al., 1995).

Originally conceptualized to describe the comprehension of simplistic print narratives (Zwaan et al., 1995), the model postulates that readers create a situation model for the first story event encountered in the narrative. The situation model for this event includes information associated with each of the five event-indexing model dimensions. As the individual continues reading, the situation model in memory is updated based on shifts or changes in one or more of the event-indexing model dimensions (e.g., a change in space from a home to a workplace). In recognizing a shift or change in dimensions, a reader likely experiences an event boundary that will prompt the reader to reevaluate the current situation model (Magliano, Radvansky, Forsythe, & Copeland, 2014). If the reader recognizes a shift in one or two dimensions within the narrative, the situation model is merely updated to accommodate the change (Zwaan et al., 1995). However, if changes in several dimensions are evident, a reader must then construct a new model for the current situation (Magliano, Zwaan, & Graesser, 1998).

In terms of processing, Zwaan (1999) posits that cognitive processing of a narrative is more intensive when there are shifts in event-indexing dimensions than when there are not shifts (i.e., the processing-load hypothesis) due to the reader’s need to update or reconstruct the current situation model. Empirical evidence contributes to the understanding that if story events are continuous on the five dimensions of narrative information, processing is easier due to the information “fitting” into the currently active situation model (Zwaan & Radvansky, 1998).

In terms of memory, the event-indexing model holds that events that have similar dimensions in common (e.g., events occur at the same time, in the same place, involve the same protagonist(s), share common goals, and/or indicate causal links) will be linked in memory and thus more likely to be recalled together (i.e., the memory-organization hypothesis); the more event-indexing dimensions two events share, the stronger the link will be (Zwaan et al., 1995). Thus, describing events in a narrative that share information on multiple dimensions will increase the chances that those events will be recalled together later.

Many empirical tests of the event-indexing model and its dimensions have focused on text processing (Zwaan et al., 1995; Zwaan, Madden, & Whitten, 2000). However, researchers have begun to extend this model to other modes, including television programming (Anderegg et al., 2014) and film (Magliano et al., 2001). The findings from the studies using audiovisual stimuli are consistent with previous results of text comprehension studies, which suggest the event-indexing model can be applied to multiple modes of communication.

For example, Anderegg and colleagues (2014) examined audiovisual entertainment media from the perspective of the landscape and event-indexing models. The primary motivation for this study was to understand if the event-indexing model (Zwaan et al., 1995) can augment the landscape model in further understanding the dimensions important to the comprehension of audiovisual media.

The researchers examined the audiovisual content and participant memory for five clips from television and film. The landscape model was found to predict participant recall of the story. Furthermore, all five dimensions of the event-indexing model moderated the effect of audiovisual content on participant comprehension on the narrative. Importantly, this finding suggests that the event-indexing model may be an important model to examine as it adds more nuance to our understanding of comprehension processes by supplementing the landscape model.

Measuring Knowledge and Comprehension Outcomes

As previously stated, much of the research on health communication campaigns and health literacy is focused on developing ways to increase knowledge and comprehension of health information. Therefore, it is imperative that researchers strive toward constructing valid and reliable ways to measure the effectiveness of interventions designed to increase people’s health knowledge. At the most basic level, increases in knowledge are assessed using a number of arbitrary questions related to the information presented to participants. Often embedded in questionnaires, researchers will use (sometimes pretested) questions to determine how many “facts” participants know. Questions are either developed by the researchers to meet the specific needs of the context studied or are adapted and/or amassed from preestablished scales (e.g., Weinstein, Walsh, & Ward, 2008). It is often difficult (if not impossible) to standardize these questions given that each campaign or intervention is unique. As Eveland and Schmitt (2015) argued in the domain of political knowledge, “factual knowledge” is often assessed through the administration of surveys constructed in an “ad hoc manner, study by study” because of the broad array of research topics (p. 171). The same is likely true for research on health knowledge. In other words, the items designed to measure knowledge of a topic in a particular study are too specific in terms of the content covered/expected to be learned to be administered across research contexts.

However, researchers are working to establish scales to be used across certain studies with similar aims. For example, Nicholson, Case, Price, Higgins, and Thompson (1991) tested and established the validity and reliability of the Health Knowledge Inventory-Alpha, a 110-item survey instrument that assesses high school seniors’ knowledge across 11 different health contexts. Additionally, Pai, Lee, Yen, and Lee (2013) developed and tested the sexual health knowledge scale (SHKS) for measuring sexual health knowledge in young adolescent girls in Taiwan. The SHKS is an example of a scale that can be used and adapted for a variety of tasks, as it can be broken down into subscales, or can be used as an overall assessment of general sexual health knowledge (Pai et al., 2013).

In terms of health campaign effectiveness specifically, the same questions are frequently posed before and after exposure to determine knowledge gains over time. For example, participants in a campaign study aimed at increasing knowledge of cardiovascular risks were asked to complete a simple survey in Phase I of the study, and then repeated the survey in Phase III after exposure to the intervention (Ma, Dollar, Kibler, Sarpong, & Samuels, 2011). In a similar vein, Hlavinknova, Mentel, Kollarova, and Kristufkova (2014) assess the effectiveness of a campaign aimed at increasing awareness of STIs an HIV by employing an 11-item questionnaire before and after campaign exposure.

As previously noted, much of the research on schemas and mental models is disjointed and multidisciplinary in nature. Researchers interested in the representation of knowledge as a critical outcome of health communication campaigns can draw from research on mental models and schemas in other disciplines. For example, Morgan and colleagues (2002) developed methodologies for comparing the mental models of experts and lay audiences in the health domain. Researchers aiming to better understand schemata want to know how individuals cognitively represent concepts and situations. As a standard across several disciplines, schemata are measured using recall, aided recall, and inference testing methodologies (Wicks & Drew, 1991). Recall methods are often simple, and instructions typically ask research participants to “write as much as they can remember” after exposure to the target material(s) (e.g., Anderegg et al., 2015; Wicks & Drew, 1991). Aided recall includes instructed prompts to ensure that the participants’ responses are specific to a certain topic or context. Finally, inference testing methodologies are similar to the methodology employed in research examining “knowledge structure density” (e.g., Eveland, Marton, & Seo, 2004) such that participants agree or disagree to the extent to which certain concepts are related to the material they were expected to learn (Wicks & Drew, 1991).

Overall, there are a number of ways to measure knowledge and comprehension as critical outcomes of health communication campaigns and strategies. But the relative lack of attention paid to how knowledge is measured has placed limitations on the accumulation of knowledge within this area of study. It is difficult to compare the results of different studies when there are no standard measures of health knowledge.

Discussion of the Literature

Research has demonstrated that knowledge related to health risks and outcomes plays an important role in understanding health communication. This is the underlying premise of the research on health literacy and research in other domains such as work on knowledge gaps and how people search for medical information. The findings of these different research areas have clearly reinforced the important role of health-related knowledge. However, this research is rather disjointed and in many ways, in its infancy.

But, there is enough of a literature to form the foundation for developing more elaborate theories of the relationship between relevant health knowledge, health communication, and health outcomes. An important part of this research tradition will need to focus on how health-related knowledge is stored in memory. Schema correspondence theory was an early approach to looking at how matching a health appeal to a person’s self-schema enhances the effectiveness of the message (Brannon & Brock, 1994). By far the most theoretically driven work in this area is Morgan and colleagues’ (2002) work on people’s understanding of health risks as evidenced by their mental models of risk and the effectiveness of interventions aimed at correcting people’s comprehension of the health risks. Certainly, this research has been valuable in large part because of its focus on how the information is represented in memory. Identifying critical differences in how lay people’s mental models diverged from experts’ mental models provided critical information for providing successful interventions. Future research would do well to continue this tradition.

A particularly neglected area of research involves the comprehension of health messages. The research on health literacy has laid a solid foundation for designing more effective interventions aimed at improving people’s understanding of health-related issues. The next generation of research should consider how health-related messages are comprehended and stored in memory. This work should greatly aid in the designing of effective health communication campaigns because these models will provide guidelines for designing messages that are more memorable and linked to relevant information in memory.

We believe that it is an exciting time to be studying health knowledge and comprehension. Research clearly demonstrates the importance of understanding health knowledge. But more theoretically driven work is needed in this area. Furthermore, this work needs to consider comprehension processes if the full potential of our understanding of health knowledge is going to be achieved.

Further Reading

Brashers, D., Goldsmith, D. J., & Hsieh, E. (2002). Information seeking and avoiding in health contexts. Human Communication Research, 28, 258–271.Find this resource:

Cho, H., & Salmon, C. T. (2007). Unintended effects of health communication campaigns. Journal of Communication, 57, 293–317.Find this resource:

Lee, M., Roskos-Ewoldsen, B., & Roskos-Ewoldsen, D. R. (2008). Applying the Landscape Model to comprehending discourse from TV news stories. Discourse Processes, 45, 519–544.Find this resource:

Morgan, M. G., Fischhoff, B., Bostrom, A., & Atman, C. J. (2002). Risk communication: A mental models approach. Cambridge, U.K.: Cambridge University Press.Find this resource:

Nielsen-Bohlman, L., Panzer, A., & Kindig, D. A. (Eds.). (2004). Health literacy: A prescription to end confusion. Institute of Medicine. Washington, DC: National Academies Press.Find this resource:

Niederdeppe, J. (2008). Beyond knowledge gaps: Examining socioeconomic differences in response to cancer news. Human Communication Research, 34, 423–447.Find this resource:

van den Broek, P., Young, M., Tzeng, Y., & Linderholm, T. (1999). The Landscape Model of reading: Inferences and the online construction of a memory representation. In H. van Oostendorp & S. R. Goldman (Eds.), The construction of mental representations during reading (pp. 71–98). Mahwah, NJ: Lawrence Erlbaum Associates.Find this resource:

Wyer, R. S. (2004). Social comprehension and judgment: The role of situation models, narratives, and implicit theories. Mahwah, NJ: Lawrence Erlbaum Associates.Find this resource:

Zwaan, R. A., & Radvansky, G. A. (1998). Situation models in language comprehension and memory. Psychological Bulletin, 123, 162–185.Find this resource:

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