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Abstract

Cancer information in the media often contains ambiguous, conflicting, or contestable knowledge, which may lead to the cultivation of cancer fatalism. Many believe cancer fatalism poses a significant barrier to cancer prevention and detection behaviors in the population. This meta-analysis synthesized empirical results from 100 studies regarding the associations between cancer fatalism and four categories of communicative and behavioral correlates: (a) media exposure, (b) cancer beliefs, (c) cancer prevention and detection engagement, and (d) cancer information management. Our findings show that cancer fatalism is positively linked to TV exposure and negatively linked to radio or Internet exposure. Cancer fatalism is also positively associated with information avoidance and negatively associated with cancer detection behaviors. This study demonstrates the nature and magnitude of the relationships between cancer fatalism and its antecedents or outcomes and offers useful insights for future investigation and theoretical development in understanding the role of cancer fatalism in communication.

Keywords

cancer fatalism cancer information health communication meta-analysis

Greater exposure to cancer information should ideally empower people to make informed decisions regarding cancer prevention and treatment (Woolf et al., 2005). This is a central justification for communication efforts in health promotion and patient education (Stacey et al., 2017). However, the magnitude and complexity of cancer information reaching the public have exploded nowadays. This may cause unnecessary confusion and misinterpretation about cancer prevention as well as other unintended adverse effects, such as the cultivation of cancer fatalism (Lee & Niederdeppe, 2011). Cancer fatalism is a belief that a cancer diagnosis is a matter of fate and beyond human control. In fact, large-scale surveys revealed that cancer fatalistic beliefs such as “it seems like everything causes cancer” and “there is not much people can do to lower chances of getting cancer” were highly prevalent among the public (Kobayashi & Smith, 2016). Many believe that cancer fatalism poses a significant barrier to cancer prevention behaviors among the population (Straughan & Seow, 1998). In particular, fatalistic beliefs about cancer can shape how individuals perceive the risks and consequences of cancer, how they make prevention decisions, and how they manage cancer information in the long term.

In recent years, the salience, amount, and diversity of cancer fatalism research have grown incrementally. Extant empirical research has offered important insights into the connections between cancer fatalism, media exposure, cancer prevention behaviors, and cancer information management (Sardessai-Nadkarni, 2021). However, tremendous inconsistency is revealed in the existing research findings on cancer fatalism; for example, rich evidence shows that cancer fatalism undermines individuals’ perceived efficacy and value of cancer prevention (e.g., Altintas et al., 2017; Smith-Howell et al., 2011), prompts their avoidance of cancer information (e.g., Freres, 2008; He & Li, 2021), and impedes their adherence to timely cancer screening (e.g., Clarke et al., 2021; Tolma et al., 2014) and healthy lifestyles (e.g., Kim & Lwin, 2017, 2021; Niederdeppe et al., 2007). Other studies, however, suggest that cancer fatalism has little or no effect on these (or other) outcomes (e.g., Heiniger et al., 2015; Kissal et al., 2018; Kulakci et al., 2015; Miles et al., 2008), or that it elicits effects contrary to those advocated (e.g., Guo et al., 2021; Lee & Shi, 2021; Lee et al., 2013; Shen et al., 2009). Despite the inconsistency, no systematic synthesis has been conducted of the relationships between cancer fatalism and these communicative/behavioral antecedents or outcomes to determine the nature and magnitude of these relationships. We thus perform a meta-analysis to bring a degree of synthesis to the cancer fatalism scholarship.

Furthermore, existing empirical research has examined the antecedents or outcomes of cancer fatalism in a wide range of focuses. In particular, communication research is emerging on the relationships between cancer fatalism, media exposure, and cancer information management. Yet, theoretical understanding of the role of cancer fatalism has been lacking in communication, despite the modern media environment that makes consumers more vulnerable to inaccurate and inconsistent cancer information. Although the ultimate goal of health communication is to reinforce or change a given health behavior, it should be recognized that communication, at best, creates or changes specific beliefs (Fishbein & Cappella, 2006). Understanding cancer fatalism is important in this context, as it can be the consequence of cancer information exposure which, in turn, impacts how individuals perceive and respond to cancer information. Therefore, guided by the S-O-R model (Mehrabian & Russell, 1974), the current study provides a comprehensive framework that connects the communicative and behavioral correlates of cancer fatalism in a theoretically and empirically meaningful way. In this framework, cancer fatalism is viewed as an organism representing an internal psychological state fostered by environmental stimulus and eliciting various behavioral outcomes, which offers a potential explanation of the cultivation and consequences of cancer fatalism in the current information environment regarding cancer.

In summary, the current meta-analysis examines the antecedents and outcomes of cancer fatalism using the S-O-R model (Mehrabian & Russell, 1974). We encompass four categories of potential correlates of cancer fatalism: media exposure, cancer beliefs, cancer prevention engagement, and cancer information management. More specifically, the objectives of the current meta-analysis are to (a) quantify the relationships between cancer fatalism and the four categories of potential correlates and (b) test moderators that may explain the heterogeneity in effect sizes across studies. The current work represents the first attempt to systematically evaluate and synthesize the roles of cancer fatalism documented in the existing evidence via meta-analysis. This meta-analysis could help to understand how information intended to inform and promote optimal health decision-making ends up impeding desired health outcomes by cultivating harmful health beliefs.

Cancer Fatalism: Definitions and Correlates

According to Shen et al.’s (2009) explication, fatalism is a set of health beliefs that encompass the dimensions of predetermination, luck, and pessimism. Cancer fatalism may develop if a person, especially when in poverty, focuses more on day-to-day survival than health promotion and disease detection (Freeman, 1989). However, cancer is likely beyond an early stage once symptoms are present, leaving limited treatment options. This reinforces cancer fatalism because people witness a cycle of late cancer diagnosis, poor outcomes, and death. Based on their observation of cancer prevention practices among African Americans, Powe and Johnson (1995) indicated that the universal experiences of angst and nihilism might converge to shape the presence of cancer fatalism. Cancer fatalism is a situational manifestation of fear and learned helplessness in which individuals face realities such as racism, discrimination, unemployment, and inadequate healthcare.

Further, Straughan and Seow (1998) added to the conceptualization of cancer fatalism by introducing the cancer attribution to preternatural forces such as fate and luck. With modern science and medicine advancements, cancer progression can be arrested by early detection and active prevention activities. A zest has accompanied these developments for advancing healthy lifestyles. As a result, however, the unknown causes of cancer are understated in health campaigns. Then, confusion arises because of the anomalies; that is, people who embrace healthy lifestyles still get cancer, and people who live risky lifestyles do not. Individuals’ coping strategy to resolve this paradoxical effect is to rationalize their belief in the notion of fate or luck. Based on this, Straughan and Seow (1998) proposed that fatalism is a belief that some health issues are beyond human control.

Overall, the scholarship has concluded that cancer fatalism is a cognitive construct with multiple dimensions. It is characterized by (a) a perceived lack of control over one’s health, (b) the attribution of cancer to predestination, fate, or luck, and (c) powerlessness and hopelessness caused by the perceived inevitability of death. Since it was discovered, cancer fatalism has been a piece of the puzzle in understanding factors that impede cancer prevention practices among diverse populations. The relationships between cancer fatalism and its correlates are worthy of note, as the knowledge acquired through such inquiry would benefit health interventions that seek to advance the health and well-being of all.

Theoretical Framework of the S-O-R Model

A theoretical framework that could logically demonstrate a potential internal connection among a set of concepts/variables helps guide the systematic overview of the communicative and behavioral correlates of cancer fatalism in the large body of scattered evidence. Originating from environmental psychology, the S-O-R model (Mehrabian & Russell, 1974) comprises three main components—stimulus, organism, and response—to explain the formation process of individuals’ beliefs and behaviors. It proposes that various stimuli (S) in the environment engender changes in an individual’s internal cognitive or affective state (O), further eliciting psychological or behavioral responses (R). Moreover, factors in the inner states (i.e., individuals’ affects and cognitions) are considered to interact reciprocally (Vieira, 2013). The S-O-R model has been extensively employed in studying health-related emotions, cognitions, and behaviors, including health anxiety (Yang et al., 2021), pandemic fear (Luo et al., 2021), and information seeking and avoidance during health crises (Soroya et al., 2021). The existing evidence indicates that the S-O-R model is a suitable overarching framework for understanding the formation of an individual’s psychological and behavioral responses when processing information.

Three motives make the S-O-R model a well-suited theoretical framework for the current meta-analysis. First, in the S-O-R model, media exposure is inherently viewed as a stimulus that can have several consequences (e.g., Soroya et al., 2021). This view is consistent with the assumption in the literature that cancer fatalism is likely to be an adverse outcome of cancer information exposure (Niederdeppe et al., 2010; Ramondt & Ramírez, 2017). Second, Bagozzi (1986) defined the organism in the S-O-R model as the “internal processes and structures intervening between stimuli external to the person and the final actions, reactions, or responses emitted. The intervening processes and structures consist of perceptual, physiological, feeling, and thinking activities” (p. 46), which is aligned well with the potential role of cancer fatalism in shaping one’s beliefs and behaviors proposed in the literature. Third, the “response” in the S-O-R model refers to psychological or behavioral outcomes influenced by the organism. The current meta-analysis also considers behavioral changes in individuals as responses to cancer fatalism.

Overall, the S-O-R model provides an organized and visualized framework for the current meta-analysis to investigate the correlates—specifically, potential antecedents and outcomes—of cancer fatalism. When individuals are exposed to cancer information in the media, they may develop cancer fatalism, and their cognitions and behaviors may be further influenced. In other words, the current meta-analysis deems media exposure as the stimulus (S), cancer fatalism as the organism (O), individuals’ cancer beliefs as correlated states with the organism, and both cancer prevention behaviors and information behaviors as the responses (R). Supplemental Appendix A illustrates the structure of this framework.

The Relationship Between Cancer Fatalism and Media Exposure

The public relies heavily on the media for cancer information (Viswanath, 2005). However, overabundant, complicated, and contradictory information can impair an individual’s capability to form healthy cognitions and make informed decisions (Nagler & Hornik, 2012). Media exposure is considered one potential cause of cancer fatalism (Ramondt & Ramírez, 2017). Previous communication studies further suggest that different types of media might play contrasting roles in influencing cancer fatalism (e.g., Lee & Niederdeppe, 2011; Niederdeppe et al., 2010); such discrepancy probably contributes to the contradiction in the evidence that the effect of media exposure is present in some studies but absent in others, or more significant in some studies than in others.

Cultivation theory suggests that habitual exposure to TV can shape people’s beliefs about the prevalence and importance of social issues (Gerbner & Gross, 1976). People with heavier exposure to TV can be more likely to perceive social reality as portrayed by TV (Gerbner et al., 1980). Longitudinal studies revealed that exposure to TV, especially local TV, can cultivate cancer fatalism (Lee & Niederdeppe, 2011; Niederdeppe et al., 2010). More specifically, the competitive pressure can drive local TV to focus more on sensationalism but less on story quality, leading to the audience’s repeated exposure to the novel and controversial findings of cancer risks rather than established evidence. The excessive emphasis on those uncertain cancer causes and the scarce mention of well-documented cancer prevention methods may contribute to the sense that everything causes cancer and nothing can help reduce cancer risks (Niederdeppe et al., 2010).

Moreover, limited time and space are left for TV to convey sufficient details of a particular cancer cause or offer follow-up information for audiences to seek further verification and prevention guidance (Gantz & Wang, 2009; Niederdeppe et al., 2010). Over time, cumulative exposure to stories lacking convincing strength and credibility nurtures cancer fatalism. Nevertheless, inconsistent associations between cancer fatalism and exposure to other mass media, such as print media and radio, were found in the literature. For example, in a two-wave nationally representative survey (Lee et al., 2012), print media use was found to prompt cancer fatalism, but it was not found in some studies (e.g., Lee & Ho, 2015; Niederdeppe et al., 2010). Similarly, Chung (2014) found that radio use could decrease cancer fatalism, while other researchers did not detect such an effect (Niederdeppe et al., 2010). The influences of these media on cancer fatalism thus remain unclear.

On the contrary, online media exposure was negatively related to cancer fatalism (Lee et al., 2012). Some scholars pointed out that compared to which in local TV, cancer information on the Internet can be similarly inaccurate and contentious (Benigeri & Pluye, 2003); the hypertextuality of the Internet can also lead to disorientation and cognitive overload, which may distract audiences and impair their ability to process, learn, and verify (Eveland & Dunwoody, 2001). However, Lee et al. (2012) argued that online media exposure could reduce cancer fatalism by assimilating information on cancer prevention. First, online health information is not as harmful as people had expected, as it is usually not factually wrong but just outdated (Eysenbach et al., 2002) or easy to verify even if it is inaccurate or incomplete (Eysenbach et al., 2002). Second, although hypertextuality might cause overwhelm or confusion, it can also contribute to elaboration and comprehensiveness (Eveland & Dunwoody, 2001). Some studies found that hypertext is more advantageous for learning structural knowledge (e.g., Eveland et al., 2004). As knowledge regarding cancer prevention includes both complex structural information and simple factual knowledge, exposure to online media may help develop comprehension of cancer information and thus diminish cancer fatalism. To examine these links, a research question is raised:

RQ1: What is the nature and average effect size of the relationship between cancer fatalism and exposure to (a) print media, (b) radio, (c) TV, and (d) the Internet across previous studies?

The Relationship Between Cancer Fatalism and Cancer Beliefs

Beliefs about cancer are nested within individual, social, cultural, and broader structural factors. They determine how people perceive and cope with cancer. Knowing how cancer fatalism is related to these factors helps understand the mechanism behind the consequences of cancer fatalism. The Health Belief Model (Rosenstock, 1974) has frequently been applied to explain or predict health behaviors (Carpenter, 2010). With roots in an expectancy-value theory model, it postulates that health behaviors are the result of decision-making based on (a) perceived susceptibility, (b) perceived severity, (c) perceived benefits of action, and (d) perceived barriers to action (Rosenstock, 1974). Perceived severity and susceptibility represent an individual’s evaluation of the consequences of the disease and personal risk, respectively. Behavior is also a result of the belief that the benefits brought by performing it will outweigh any barriers. Self-efficacy, an individual’s perceived confidence in the ability to execute a behavior, was later added to the model (Rosenstock et al., 1988).

The associations between cancer fatalism and perceived susceptibility and severity are observed among various populations in cross-sectional studies (e.g., Bakan et al., 2021; Lee & Shi, 2021; Miles et al., 2008). Many of these studies suggest that people with high levels of cancer fatalism tend to consider themselves at higher risk of cancer (e.g., Lee et al., 2013) and deem cancer a severe disease (e.g., Miles et al., 2008). The uncertainty brought by cancer fatalism makes people overestimate their cancer risks or exposure to cancer causes (P. K. Han et al., 2006; P. K. J. Han et al., 2007). Perceiving that cancer is unavoidable if it is predestined enhances misperceptions about the population prevalence of cancer (Klein et al., 2014). Besides, the belief that a cancer diagnosis is a death sentence was prevalent among cancer fatalists (Moser et al., 2021). When they think about cancer, they could automatically think about death (Kobayashi & Smith, 2016; Paige et al., 2021), which is an evident sign of the fatalists’ overrated severity perception of cancer.

Cancer fatalism can also decrease individuals’ perceived benefits and increase their perceived barriers to cancer prevention (e.g., Altintas et al., 2017; Cohen et al., 2021; Kulakci et al., 2015; Smith-Howell et al., 2011). Because cancer fatalism brings intense cancer fear and worry (Vrinten et al., 2016), it makes individuals avoid the discussion of cancer (Miles et al., 2008; Williams et al., 2019) and engage less in related educational programs (Chavez et al., 1997; Mandelblatt et al., 1999). Fatalists tend to find it difficult and reluctant to make decisions on cancer prevention measures (Austin et al., 2002), and they would prefer not to know the diagnosis of cancer (Smits et al., 2018). The lack of awareness further deters them from engaging in appropriate preventive activities. Additionally, cancer fatalism might reduce preventive actions due to the perception that cancer is unpreventable, leading them to belittle the effectiveness and underrate the values of cancer prevention.

Lastly, cancer fatalism counteracts self-efficacy by nature. A series of empirical studies also detected their correlation, observed in a wide range of populations, including samples in Europe (e.g., Miles et al., 2008, 2011), Asia (e.g., Altintas et al., 2017; Bhandari et al., 2021), and America (e.g., Carter-Harris et al., 2020; Smith-Howell et al., 2011). Cancer fatalism is characterized by predestination, pessimism, and the attribution of one’s cancer diagnosis to fate. People high in cancer fatalism can perceive a lack of control over their health, resulting in low confidence in cancer prevention. However, high efficacy means a high level of self-belief in one’s capability to exercise personal control over one’s life events. If people believe that there is very little they can do to overcome the “fate” of cancer, powerlessness can override self-efficacy to take preventive actions (Straughan & Seow, 1998).

Accordingly, two hypotheses are proposed, and one research question is raised:

H1: Cancer fatalism is positively associated with (a) perceived susceptibility, (b) perceived severity, and (c) perceived barrier.

H2: Cancer fatalism is negatively associated with (a) perceived benefit and (b) self-efficacy.

RQ2: What is the average effect size of the relationship between cancer fatalism and (a) perceived susceptibility, (b) perceived severity, (c) perceived barrier, (d) perceived benefit, and (e) self-efficacy across previous studies?

The Relationship Between Cancer Fatalism and Cancer Prevention

Cancer fatalism has been associated with lower engagement in cancer prevention and detection behaviors. Individuals with higher levels of cancer fatalism tend to live less healthy lifestyles: they have less regular exercise (e.g., Kim & Lwin, 2017), consume fewer fruits and vegetables (e.g., Kim & Lwin, 2021), are less willing to apply sunscreen (e.g., Jensen et al., 2020), and are more likely to smoke (e.g., Niederdeppe et al., 2010). Studies also reported that people high in cancer fatalism have lower cancer screening attendance rates. This phenomenon is observed in the screening adherence to various types of cancer, including breast cancer (e.g., Betancourt et al., 2010), cervical cancer (e.g., Seow et al., 2000), prostate cancer (e.g., Hararah et al., 2015), and colorectal cancer (e.g., Gorin, 2005).

The mechanism behind these links can come down to the nature of cancer fatalism. First, one might not see regular engagement in cancer prevention activities as a viable option because they have low confidence in controlling their health. Second, to live a healthy lifestyle to prevent cancer, a certain level of self-discipline, perseverance, and patience when facing inconvenience, discomfort, and boredom is usually required, as engaging in both health promotion behavior (e.g., physical exercise) and cancer screening behavior (e.g., colonoscopy) is unlikely to be pleasant and instantly effective. Since individuals with cancer fatalism tend to attribute whether they will get cancer to fate and luck, which are unpredictable and irrevocable, they can be tempted by “carpe diem” (enjoy the moment without concern for the future) and give up efforts to take any measures to prevent cancer. Finally, when a person believes that death is inevitable when cancer is present, he or she can be cast into a shadow of powerlessness, hopelessness, and meaninglessness. It seems natural to refuse to screen for cancer—the outcome makes no difference whether cancer is early or lately detected.

However, despite the abundant evidence revealing the links between cancer fatalism and cancer prevention and detection engagement (Cohen, 2013), studies that controlled for possible confounding variables showed mixed results on such associations. Specifically, when adjusted for demographic variables, many studies still demonstrated such significant links (e.g., Gorin, 2005; Talbert, 2008), while some did not (e.g., Hay et al., 2019; Mayo et al., 2001; Russell et al., 2006). On the other hand, in Shen et al.’s (2009) fatalism scale development study, they examined the relationship between fatalism and healthy behavior engagement, while the direction of the correlations they detected was the opposite of what they predicted, that is, fatalism encouraged engagement in healthy behaviors. Besides, in Lee et al.’s (2013) survey of 802 Singaporean women, cancer fatalism was correlated to women’s intention to engage in breast cancer screening at the zero-order level, while when demographic variables, media attention, and interpersonal communication were controlled in their regression model, the significance of this link disappeared. Shen et al. (2009) explained that the inconsistency might lie in the simplified measurement of behavior/intention. Nevertheless, the entire available empirical evidence on this relationship has yet to be systematically integrated and substantively interpreted. Thus, a research question is raised to address the inquiry:

RQ3: What is the nature and average effect size of the relationship between cancer fatalism and (a) cancer detection intentions, (b) cancer detection behaviors, (c) health promotion intentions, and (d) health promotion behaviors across previous studies?

The Relationship Between Cancer Fatalism and Information Management

The acquisition of health information can help people make informed medical decisions and engage in preventive behaviors (Bennett & Glasgow, 2009), resulting in positive health outcomes (Wigfall & Friedman, 2016). Informed by the Risk Information Processing Model, Lee and Shi (2021) pointed out that cultural worldviews such as fatalism can shape how people perceive and cope with illness, thus influencing their information management strategies. Specifically, cancer information seeking and avoidance are two different cancer information management strategies (Lu et al., 2021). The former refers to “active efforts to obtain specific information outside of the normal patterns of exposure to mediated and interpersonal sources” (Niederdeppe et al., 2007, p. 155), while the latter is defined as “any behavior intended to prevent or delay the acquisition of available but potentially unwanted information” (Sweeny et al., 2010, p. 341).

Cancer fatalism is believed to be associated with information seeking, although the causal direction has not been established. On the one hand, cancer fatalism can be diminished through information seeking. Scholars suggest that proactive information seeking can satisfy people’s information needs, increase their understanding of cancer, and close their knowledge gap (Lee et al., 2012; Miles et al., 2008). Several researchers employed such assumptions (e.g., Freres, 2008; Valera et al., 2018). On the other hand, an alternative direction is that cancer fatalism reduces the motivation to seek cancer information. Individuals high in cancer fatalism might be passive in information seeking since they tend to believe that there is not much they can do to prevent cancer, and that their efforts to seek cancer information are meaningless (Johnson, 1997). Recent empirical studies in information seeking have adopted this direction (e.g., He & Li, 2021; Lee & Shi, 2021; Lu et al., 2021). The current meta-analysis also employs this direction, as information seeking is considered one of the behavioral responses in the S-O-R model.

Nevertheless, the existing evidence on the correlation between cancer fatalism and information seeking is mixed. For example, a survey covering 1,664 participants in the US found a significant negative association (Freres, 2008), while such an association was not significant in the investigation by Valera et al. (2018) as well as Lee and Shi (2021). Additionally, the association appeared significantly positive in some recent studies (e.g., He & Li, 2021; Lu et al., 2021).

Information avoidance, the other side of the same coin, is found to be positively correlated with cancer fatalism (e.g., Miles et al., 2008). People may intentionally delay information acquisition because the information necessitates belief change, unwanted behavior, or unpleasant emotions (Sweeny et al., 2010). According to the Extended Parallel Processing Model (EPPM), a high level of perceived threat and a lack of perceived efficacy can motivate people to enter the fear control processing, resulting in defensive avoidance and inattentiveness to information. Those who feel overwhelmed with controlling cancer risks may engage in information avoidance to reduce fear, anxiety, and cognitive dissonance (Case et al., 2005). The positive association was consistently reported in previous studies (e.g., He & Li, 2021; Lee & Shi, 2021; Lu et al., 2021).

Accordingly, the following research question is raised:

RQ4: What is the nature and average effect size of the relationship between cancer fatalism and (a) information seeking and (b) information avoidance across previous studies?

Potential Moderators

When there are substantially inconsistent result patterns across studies, it is usually informative to investigate the sources of this inconsistency through moderator analyses. The goal of conducting these moderator analyses is to identify characteristics of the studies that contain heterogeneity in effect sizes.

An important observation from prior cancer fatalism research is that there is diversity in the interpretation of cancer fatalism among different races (Sardessai-Nadkarni, 2021). For example, for African Americans, cancer fatalism is rooted in their experience of angst and nihilism under poverty and discrimination (Powe & Johnson, 1995), while Asians’ cancer fatalism is grounded in the idea that cancer is a result of karma or kismet coming from their social taboos and religion (Jun & Oh, 2013). The diverse cultural origins of cancer fatalism may differ in their power to shape cancer beliefs and behaviors among different racial groups. Moreover, the prevalence of cancer fatalism is not equal among the races. For instance, several studies compared the level of cancer fatalism among different racial groups in the US. These studies revealed that compared to African Americans, Hispanic Americans reported higher levels of cancer fatalism (e.g., Facione et al., 2002; Powe et al., 2009); further, higher cancer fatalism was found among African Americans than white non-Hispanic Americans (e.g., Powe et al., 2007; Russell et al., 2006). Scholars have suggested that cancer fatalism could perpetuate racial health disparities. It is thus valuable to examine whether some racial populations are more influenced by cancer fatalism than others.

Age may also moderate the relationships between cancer fatalism and its correlates. Advancing age is the most critical risk factor for cancer (National Cancer Institute, 2021). The elderly may perceive cancer as more threatening and “real” than younger people. The more they are exposed to cancer progression and related death issues, the more frequently they may attribute cancer to fate; subsequently, the more likely they are to be influenced when they make sense of cancer or make health decisions regarding cancer prevention. It is found that the elderly are generally higher in cancer fatalism (Powe & Finnie, 2003). For younger people, cancer may be a less relevant and pressing issue to be worried about in their plans, so even though they believe in cancer fatalism, the role of cancer fatalism can be less dominant. Given this, the influence of cancer fatalism may be stronger among the elderly than among younger people.

Lastly, when exploring the relationships between cancer fatalism and health promotion behaviors, the current meta-analysis also considers the type of behaviors as a potential moderator. Many health behaviors differ in their natures (F. Shen et al., 2015), which influences the strength of their relationships with cancer fatalism. For example, cancer fatalism may pose different impacts on planned behaviors (e.g., diet and exercise) and addictive behaviors (e.g., smoking and alcohol drinking) due to the difference in perceived behavioral control (Fishbein & Ajzen, 2009). Since cancer fatalism has been extensively studied in various health contexts, it is essential to understand whether the role of cancer fatalism varies in influencing different health behaviors. Thus, one research question is raised to understand:

RQ5: What factors (i.e., race and age of the sample, and type of behaviors), if any, moderate the relationships between cancer fatalism and its correlates?

Overview of the Current Meta-Analysis

In summary, guided by the S-O-R model, the current meta-analysis aims to examine the nature and strength of the associations between cancer fatalism and four distinct categories of potential correlates, namely (a) media exposure, (b) cancer beliefs, (c) cancer prevention engagement, and (d) cancer information management. It is warranted for several reasons. First, no quantitative synthesis has been delved into summarizing results from the existing empirical evidence. A meta-analytic review will facilitate the understanding of cancer fatalism. Second, summarizing the statistical nature and strength of these associations will allow the exploration of integrating cancer fatalism into existing conceptual models of health behaviors and information behaviors. This may subsequently inform the development and design of theory-driven health campaigns and behavioral interventions. Lastly, despite the wealth of empirical evidence for these associations, there are some inconsistencies in the significance of the effects. A meta-analysis will allow moderator analyses to explore the conditions under which cancer fatalism is most likely related to its correlates.

Method

The procedures followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a protocol intended to facilitate the preparation and reporting of systematic reviews and meta-analyses (Page et al., 2021).

Literature Search and Eligibility Criteria

Studies were collected in four steps (Card, 2012). First, to cover a broad range of journal publications, conference papers, book chapters, theses, and dissertations, with a cutoff time point of 30 June 2021, a systematic literature search in the databases ABI/INFORM Collection, EBSCOhost (including Academic Search Complete, CINAHL Complete, Communication & Mass Media Complete, ERIC, Global Health, APA PsycArticles, APA PsycInfo), PubMed (including MEDLINE), Web of Science/Social Science Citation Index, ProQuest Dissertations & Theses Global, All Academic, and Google Scholar was conducted. For search terms, three keywords, namely fatalism, fatalistic, and fatalist, were used and paired with the term cancer. At least one pair of the search terms had to appear in the title or abstract of the studies to be retrieved in the database search, which resulted in an initial pool of 2,118 studies. After excluding duplicate and irrelevant studies, a preliminary sample of 860 potential studies was identified as relevant to cancer fatalism and its potential correlates.

Next, the 860 studies were examined on a full-text basis using six eligibility criteria (for a flow diagram, see Supplemental Appendix B). The following eligibility criteria were required:

(1) The study includes a quantitative report on relevant statistics (e.g., sample size, means, SDs, t values, exact p values, frequencies, and zero-order correlations) to permit effect size computation. When necessary statistical data were missing, attempts were made to contact the corresponding authors to obtain relevant information. Studies were excluded if they were not conducted using an empirical approach (n = 54), or an empirical approach but a qualitative research design was employed (n = 397), or did not provide sufficient statistics that could be converted into effect sizes (n = 62).

(2) The study examines the relationships between cancer fatalism and the variables inquired about (i.e., cancer prevention engagement, cancer beliefs, information management, and media exposure¹) in the current meta-analysis. Studies were excluded if the relationships in question were beyond the scope of the current meta-analysis (n = 55).

(3) The study focuses on the population without a cancer history. Studies were excluded if their participants were comprised of cancer patients or cancer survivors (n = 146).

(4) The study utilizes cancer fatalism measurements that meet the definition of cancer fatalism. Fatalism is operationalized as a construct consisting of three dimensions: predetermination, luck, and pessimism (Shen et al., 2009). To be included, a study’s operationalization of cancer fatalism should demonstrate at least one dimension, or embody at least some items that are equivalent to or synonymous with the items in Shen et al.’s (2009) validated fatalism scale. Studies were excluded if their measures of cancer fatalism were incompatible with the central dimensions of cancer fatalism (n = 15).

(5) The study using experimental designs reports baseline data. Studies were excluded if they only assessed cancer fatalism post-manipulation or intervention (n = 12).

(6) When multiple studies reported results from the same or overlapping samples, only the one with the largest sample was included. Only the corresponding journal paper was included if a conference paper or a dissertation was subsequently published in a journal. It should be noted that the Health Information National Trends Survey (HINTS) assessed cancer fatalism in its investigation from 2003 to 2020, resulting in the most extensive survey data inquiring about cancer fatalism. And 19 studies in the preliminary sample of this analysis were based on different cycles of HINTS data.² Therefore, the raw data of the 12 cycles of HINTS were obtained and used in this analysis as one single study, while the 19 studies employing HINTS data were excluded. In total, 22 studies were excluded from this procedure.

After the eligibility check, 97 studies were included. Step three of the literature search was a backward search, and step four was a forward search to find potential studies that were not located by the search terms. The former examined the reference lists of the preliminary sample, and the latter checked a cited reference search of these studies through Google Scholar’s “cited by” feature. Twelve studies were identified, and three remained after the eligibility check. Combined, 100 studies constituted the final sample for the meta-analysis.

Coding of Study Characteristics

In addition to the effect sizes, the sample characteristics of each study were coded. More specifically, race, gender composition, mean age, and sample size were recorded.

Effect Size Computation and Analysis

In terms of the four categories of potential correlates of cancer fatalism, district components were identified and separately examined. First, for cancer prevention engagement, health promotion behaviors/intentions and cancer detection behaviors/intentions were investigated. Moreover, the examination of cancer beliefs covered perceptions of benefit, barrier, severity, susceptibility, and self-efficacy. Third, for information management, the examination included information seeking and information avoidance. Lastly, exposure to different media outlets was considered. The unit of analysis (k) was the individual relationship between cancer fatalism and a specific correlate.³

For cancer detection intentions, health promotion behaviors and intentions, cancer beliefs, information seeking, information avoidance, and media exposure, Pearson’s r correlation coefficient was selected to represent the effect size, as these variables were usually measured as continuous variables. Cohen’s d, the standardized mean difference, was a more appropriate effect size indicator for cancer detection behaviors, which were treated as dichotomous variables in most studies included in the current meta-analysis. However, Cohen’s d was converted to Pearson’s r in summary statistics to make results comparable. Analyses were done separately for each correlate to avoid violations of interdependence. Each effect size was weighted by the inverse of its variances to account for sampling error. The average effect size was computed using a random-effects model, as it is assumed that the effect sizes across studies were not homogeneous. Analyses were conducted with the Comprehensive Meta-Analysis (CMA) program (v3; Borenstein et al., 2014).

Heterogeneity Tests

Heterogeneity tests were performed using Q statistics to estimate the amount of variation in effect sizes among studies. A statistically significant Q statistic implies heterogeneity. Additionally, I² values describe the magnitude of heterogeneity among studies. Following Huedo-Medina et al.’s (2006) suggestions, I² ≈ 25% represents a small amount of heterogeneity, I² ≈ 50% represents a medium amount of heterogeneity, and I² ≈ 75% is regarded as a large amount of heterogeneity.

Moderator Analyses

Moderator analyses were conducted to test for systematic differences in effect sizes among studies. The literature indicated that the nature of the relationships between cancer fatalism and its potential correlates could differ for different behaviors among different racial or age groups (Cohen, 2013). Thus, behavior types, the race of participants, and the mean age of participants were considered moderators in the current meta-analysis. Due to the insufficient number of studies that focused specifically on a particular domain of moderators, the choices of potential moderators also varied for different correlates. More specifically, participants’ race was tested as a potential moderator for cancer detection behaviors. For health promotion behaviors, behavior type was assessed as a potential moderator. The mean age of participants was tested for all associations with significant heterogeneity as a continuous moderator.

Publication Bias Analysis

Publication bias is one of the main problems in meta-analyses. Studies with small sample sizes or non-significant results are less likely to be published. However, their systematic absence can threaten the validity of meta-analyses. Two methods were used to assess the possibility of publication bias. First, the funnel plot was visually inspected. Asymmetry of the plot would indicate potential bias. Second, Begg and Mazumdar’s rank correlation test and Egger’s regression test were used to formally test the significance of the asymmetry of the plot. A significant statistic indicates funnel plot asymmetry.

Results

Description of Studies

In total, 226 effect sizes were yielded by 100 studies included in the current meta-analysis (N = 115,043). These studies were published between 1995 and 2021, with the median year of publication being 2015. The studies’ sample sizes ranged from 68 to 44,030, with a median sample size of 447. The average age of participants was between 20.45 and 75.28, with a mean of 50.28. More than 80% of the sample had mean ages of 40 or above. Most studies (62%) used samples from the United States. Sixteen studies (16%) were from the Asia-Pacific regions such as China, Hong Kong, Taiwan, Korea, Singapore, Thailand, Nepal, and Australia. Fourteen studies (14%) were from countries in the Middle East, such as Turkey, Israel, Iran, Palestine, Egypt, and Jordan. Six studies (6%) were conducted in European countries like the UK, Ireland, Denmark, and Germany. And two studies (2%) employed multinational samples across the US and China or Singapore, respectively.

Average Effect Size

To answer RQ1, results revealed that TV exposure (r = .05, 95% CI [0.03, 0.07], p < .001) was positively associated with cancer fatalism, while Internet exposure (r = −.05, 95% CI [−0.08, −0.01], p = .004) and radio exposure (r = −.03, 95% CI [−0.05, −0.01], p = .002) were negatively associated with cancer fatalism. Besides, the relationships of cancer fatalism with general mass media exposure (r = −.06, 95% CI [−0.31, 0.19], p = .624) and print media exposure (r = .02, 95% CI [−0.04, 0.07], p = .557) were not significant (see Supplemental Appendix C).

Additionally, addressing RQ2, the average effect sizes for cancer beliefs suggested that higher cancer fatalism was related to higher perceived barrier (r = .22, 95% CI [0.16, 0.27], p < .001), perceived severity (r = .11, 95% CI [0.03, 0.19], p = .005), perceived susceptibility (r = .11, 95% CI [0.07, 0.14], p < .001), and lower self-efficacy (r = −.12, 95% CI [−0.20, −0.04], p = .005), while no significant link between cancer fatalism and perceived benefit (r = −.06, 95% CI [−0.18, 0.06], p = .314) was found. Thus, H1 and H2(b) are supported, while H2(a) is not supported.

Furthermore, to answer RQ3, the average effect size for cancer prevention engagement indicated that higher cancer fatalism was associated with lower cancer detection intentions (r = −.10, 95% CI [−0.19, −0.00], p = .047) and behaviors (r = −.13, 95% CI [−0.17, −0.09], p < .001). Although the association between cancer fatalism and health promotion intentions (r = −.10, 95% CI [−0.40, 0.22], p = .538) did not reach statistical significance, higher cancer fatalism was significantly related to lower actual engagement in health promotion (r = −.07, 95% CI [−0.09, −0.05], p < .001).

Lastly, cancer fatalism was positively correlated with information avoidance (r = .25, 95% CI [0.15, 0.35], p < .001), while its correlation with information seeking (r = .08, 95% CI [−0.10, 0.26], p = .395) was non-significant. RQ4 is answered.

Moderators

RQ5 explored the factors that may moderate the relationships between cancer fatalism and its correlates. A significant and large amount of heterogeneity was observed among most of the average effect sizes, except for those of the associations of cancer fatalism with radio exposure, TV exposure, and Internet exposure (see Supplemental Appendix C). A series of moderator analyses were performed for the correlates with significant Q values to test whether potential moderators significantly accounted for variability in effect sizes.

Because there were less than three studies in a subgroup for many correlates, as categorical moderators, participants’ race was tested for cancer detection behavior only; behavior type was tested for health promotion behavior only. Categorical moderators were examined using subgroup analyses with random effects within subgroups and fixed effects across subgroups (i.e., a mixed-effects model) to test for significant differences (Q_B) between subgroup mean effect sizes (Hedges & Pigott, 2004). The continuous moderator in this analysis, the mean age of participants in each study, was examined by a random-effects meta-regression using the Z distribution with unrestricted maximum likelihood estimation. More specifically, the effect sizes were regressed to the mean age of participants in each study. A significant prediction indicates that the effect sizes vary linearly with the mean age of participants; in other words, the mean age of participants systematically relates to the association between cancer fatalism and screening behavior.

As the categorical moderators in the current meta-analysis have more than three levels, the significant between-group heterogeneity indicates that at least some groups differ from others, but exactly where those differences lie is unclear. Card (2012) suggested that a series of all possible two-group comparisons be performed as follow-up analyses to identify which groups differ in the magnitudes of their effect sizes. This approach parallels Fisher’s Least Significant Difference test in ANOVA. However, like in ANOVA, the problem with using this approach in categorical moderator analyses is that it allows for higher-than-desired rates of type I error in the follow-up comparisons (Card, 2012). A second problem with this approach occurs when different groups have different effective sample sizes or amounts of within-group heterogeneity (Card, 2012). In these situations, this approach can yield surprising results, in which groups that appear to have quite different average effect sizes are not found to differ. In contrast, groups with more similar average effect sizes are found to differ. Thus, interpreting these results merits caution.

As presented in Supplemental Appendix D, participants’ race was a significant moderator of the association between cancer fatalism and cancer detection behavior, Q(5) = 18.34, p = .003. The effect size among the Asian population was not significantly different from that among the Hispanic population but significantly larger than that among other races (see Supplemental Appendix E). Additionally, behavior type was a significant moderator of the association between cancer fatalism and health promotion behavior, Q(4) = 10.54, p = .032. The effect sizes for smoking avoidance, sunscreen use, and healthy diet were negative and significant, although no significant differences were found (see Supplemental Appendix F). However, the effect sizes for physical activity and alcohol avoidance were not substantial.

Moreover, the mean age of participants only explained the heterogeneity of the effect size for cancer detection behavior. The results of meta-regression (see Supplemental Appendix G) showed that the mean age of participants significantly predicted the association between cancer fatalism and cancer detection behavior (β = .004, SE = 0.002, 95% CI [0.000, 0.007], p = .046). A 1-year increase in the mean age of participants brought a 0.004 increase in the size of the negative association. As cancer fatalism increased, older people were less likely to engage in cancer detection (i.e., a stronger negative association) than younger people.

Publication Bias

There was limited evidence of publication bias. The visual inspection of the funnel plots (see Supplemental Appendix H) was supported by Egger’s regression test and Begg and Mazumdar’s rank correlation test, which found that most asymmetries were insignificant (see Supplemental Appendix I). Nevertheless, for some of the correlates, due to the limited number of included studies, such results need to be interpreted cautiously.

Discussion

Informed by the S-O-R model, the current meta-analysis examined the associations between cancer fatalism and four categories of correlates: (a) media exposure, (b) cancer beliefs, (c) cancer prevention engagement, and (d) cancer information management. Most of the average effect sizes were statistically significant. Correlations of r lower than .3 might be considered small by traditional standards. Nonetheless, given that individually tiny influences could cumulate to produce meaningful outcomes (Abelson, 1985), effect sizes of this magnitude can be substantively important in the formation of health beliefs and behaviors. Additionally, drawing from the binomial effect size display (BESD) (Rosenthal & Rubin, 1982), the proportions of the population with better health outcomes vary noticeably between people high and low in cancer fatalism (see Supplemental Appendix J), suggesting that cancer fatalism is a necessary construct to consider in health communication research.

The existing empirical evidence on the effects of TV exposure is consistent and clear: TV exposure can cultivate cancer fatalism. Some studies indicate that TV news coverage’s overemphasis on new cancer risks while omitting prevention suggestions is to blame for this cultivation (e.g., Niederdeppe et al., 2010). This inference is tempting, while cancer is also a frequent topic in other TV genres such as dramas, talk shows, educational programs, and advertisements. Future work is warranted to further investigate how cancer is portrayed in different TV formats and genres and their differential effects on cultivating cancer fatalism. On the other hand, some scholars argue that simply watching TV can make people passive and lethargic by putting them in stasis (Kubey & Csikszentmihalyi, 2002). It is possible that TV viewing may reinforce a state where one feels a lack of control over his or her life. More theoretical and empirical efforts are needed to clarify the mechanisms and circumstances that underlie the effects of TV exposure on cancer fatalism.

Exposure to the radio and the Internet plays a different role than TV: it reduces cancer fatalism. The interactive nature of the Internet may encourage users to be more actively engaged, which heightens a sense of control that could be transferred to a proactive attitude toward cancer prevention. Rich online content from credible sources like the World Health Organization could deliver efficacy information that cancer is preventable and help establish positive social norms for being actively engaged in cancer prevention. These might reduce the likelihood of developing cancer fatalism. However, the conditions under which the unique characteristics of radio and its cancer information content influence cancer fatalism remain unclear. Future research is necessary to make sense of how radio and Internet use reduce cancer fatalism. Nevertheless, the current results show potential for radio and the Internet as promising channels to bridge gaps in cancer fatalism among the population. Groups with lower socioeconomic status are found to be higher in cancer fatalism (Facione et al., 2002; Mayo et al., 2001; Niederdeppe et al., 2007; Straughan & Seow, 1998), while socioeconomic status differences in accessibility, motivations, and skills to use radio and the Internet for health information are minimal (Kreps et al., 2007; Smith et al., 2011). Thus, radio- or Internet-based cancer communication campaigns can be a beneficial avenue to reduce health disparities by empowering less advantaged groups.

Moreover, information avoidance is positively associated with cancer fatalism, providing a glimpse of individuals’ coping strategies for extensive cancer information. When cancer information unintentionally arouses fear and confusion, cancer fatalism may represent the consequence of one’s maladaptive response, intertwining with avoidance, denial, or reactance. This offers insights into how cancer fatalism can affect or be shaped in the media environment flooded with extensive cancer information, although causal relationships need to be confirmed in future research. On the other hand, information seeking was not a significant correlate of cancer fatalism. One possible reason for the weak or null relationships is that only a few studies investigated this variable, and inconsistent results were found. Future endeavors are necessary to examine this link.

There are significant and negative relationships between cancer fatalism and cancer detection engagement. This indicates that reducing cancer fatalism may contribute to the improvement of cancer screening adherence. More specifically, according to the BESD, the effect size of cancer fatalism on cancer detection behavior indicates that, on average, 56.5% of the individuals who score low in cancer fatalism are expected to engage in cancer screening, compared with 43.5% of those scoring high. This difference is nontrivial when considering the population as a whole. Moreover, as Abelson (1985) emphasized, a psychological variable that affects one’s beliefs, intentions, or behaviors, every time it happens, will have an effect that could cumulate over time. Cancer fatalism can have cumulative effects every time people make decisions on cancer prevention, leading to important consequences for many health outcomes in the long term. Thus, it is worthwhile for researchers and practitioners to develop communication strategies addressing cancer fatalism to increase cancer screening attendance and promote healthy lifestyles in the population.

Despite the non-significant association between cancer fatalism and perceived benefits, the results on cancer beliefs indicated that the characteristics rooted in cancer fatalism go along with the perception of greater barriers, severity, susceptibility, and lower self-efficacy regarding cancer prevention. This shows the need to integrate cancer fatalism into health communication theories and models to extend our understanding of human health decision-making and health information processing. For example, considering its significant relationship with severity, susceptibility, and self-efficacy, cancer fatalism can be placed in the EPPM as a determinant of threat/efficacy appraisals or a form of maladaptive response, to examine the role that cancer fatalism plays when individuals are facing cancer-related messages, especially those fear-arousing ones.

According to the results of moderator analyses, the negative relationship between cancer fatalism and cancer detection behavior was stronger among the Asian and Hispanic populations. Using the odds ratio for ease of interpretation,⁴ it is evident that cancer fatalism predicted lower odds of engaging in cancer detection in the Asian and Hispanic populations, indicating that cancer fatalism may further exacerbate the long-documented health disparities among different racial groups. Health disparities are complex, involving the interaction of populations’ cultures, values, and socioeconomic status, likely resulting from inequalities in social systems. Thus, researchers should pay extra attention to the interpretations of cancer fatalism within a specific racial group, including the role of family, culture, and spirituality. Besides, health communication in Asian and Hispanic communities should take cancer fatalism into account and develop culturally tailored messaging when addressing the adverse effects of cancer fatalism. Moreover, older people are more affected by cancer fatalism, consistent with previous empirical evidence (Powe & Finnie, 2003). Thus, it can be valuable for cancer fatalism interventions to target the elderly population. Furthermore, it is also suggested that a higher level of cancer fatalism was significantly linked to less engagement in smoking avoidance, sunscreen use, and healthy diet; behavioral change interventions targeting these behaviors will benefit from tackling cancer fatalism.

Some important limitations should be considered when interpreting the current study’s results. First, although significant efforts were made to conduct a thorough literature search, it is still possible that some relevant studies were missed in the current meta-analysis. For some correlates, only a small number of relevant studies were located. Meta-analyses of such small sizes are not uncommon. In the Cochrane Database of Systematic Reviews, for example, the median number of studies included in a meta-analysis was six (Davey et al., 2011). Jackson and Turner (2017) recommended that five or more studies be needed to achieve reasonable power in random-effects models. Nevertheless, the low number of studies precludes the examination of more understudied correlates and moderators. For example, we could not analyze the link between social media use and cancer fatalism because only two studies assessed this relationship (i.e., Chung & Lee, 2019; He & Li, 2021). And some variance between studies remained unexplained by the non-significant moderator analysis. As empirical studies on cancer fatalism emerge, future reviews should expand the range of potential correlates and moderators to identify the factors that account for effect size heterogeneity and explore the mechanisms underlying the effects of cancer fatalism.

Additionally, the nature of the data used in the current meta-analysis does not allow us to establish the causal direction⁵ of the associations that could go either way. For example, some scholars noted that cancer fatalism might result from one’s inability to maintain healthy behaviors (e.g., L. Shen, 2017). Functioning as a sense-making structure, cancer fatalism might help populations with low socioeconomic status cope with difficult situations and cognitive dissonance by justifying their failure to improve their health status (L. Shen, 2017). In this view, cancer fatalism might be the effect of poor health outcomes instead of the cause. Similarly, it is also possible that cancer fatalism is a maladaptive response resulting from a situation where an individual perceives a great barrier, a high threat, and a low efficacy of reducing cancer risks. The unspecified causal direction also applies to the relationship between information avoidance and cancer fatalism. Nevertheless, some longitudinal studies have confirmed that specific types of media exposure could cause cancer fatalism (e.g., Lee & Niederdeppe, 2011), which is the default assumption in the current meta-analysis. We call for more longitudinal or experimental studies to clarify the causes and effects of cancer fatalism.

Despite these limitations, the current meta-analysis took the initial step toward synthesizing the vast amount of empirical evidence on the associations between cancer fatalism and four categories of correlates. Cancer fatalism has often been examined as a covariate in previous studies rather than understanding its role from a theoretical perspective. Using the S-O-R model, the current meta-analysis thus situates cancer fatalism in the context of media effects, considering cancer fatalism as a mechanism behind the behavioral effects of media exposure. It also represents an effort to understand how information designed to facilitate better health decision-making could instead lead to undesirable health outcomes by cultivating a harmful health belief. This study illuminates fruitful directions worthy of future empirical investigation and theoretical development. We hope our findings spur more research to make better sense of this important health communication issue.

Supplemental Material

sj-docx-1-crx-10.1177_00936502231205735 – Supplemental material for Cancer Fatalism in the Information Age: A Meta-Analysis of Communicative and Behavioral Correlates

Supplemental material, sj-docx-1-crx-10.1177_00936502231205735 for Cancer Fatalism in the Information Age: A Meta-Analysis of Communicative and Behavioral Correlates by Minyi Chen and Hye Kyung Kim in Communication Research

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Minyi Chen

Hye Kyung Kim

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biographies

Minyi Chen is a PhD candidate at the Wee Kim Wee School of Communication and Information at Nanyang Technological University in Singapore. Her research focuses on media effects and persuasive health messages. She is particularly interested in the role of individual differences in health-decision making and the processing of health information.

Hye Kyung Kim (PhD, Cornell University) is an Associate Professor at the Wee Kim Wee School of Communication and Information at Nanyang Technological University in Singapore. Her overarching research goal is to apply communication and social psychological theories to understand the processing and effects of communicative interactions in health.

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