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date: 27 May 2017

Fatalism and Locus of Control as a Consideration When Designing Health and Risk Messages

Summary and Keywords

Fatalism is a set of beliefs that encompasses such dimensions as predestination, pessimism, and attribution of one’s health (life events) to luck. Locus of control refers to the extent to which individuals believe they are in control of events that affect them. Individuals with an external locus of control perceive their life is controlled by environmental factors they can’t change, or by chance or fate. Fatalism and external locus of control are both negatively associated with health behaviors and health outcomes; and contribute to health disparities due to the link between culture and socio-economic factors.

Keywords: fatalism, locus of control, correlates, health outcomes, mitigation strategy, health and risk message design and processing


Health communication scholars have had a longstanding interest in the construct of fatalism, particularly when the health issue is severe and likely fatal such as cancer (Powe & Finnie, 2003). A large proportion of research on fatalism, mainly by Powe and her associates (Powe, 1995; Powe & Johnson, 1995; Powe, Daniels, & Finnie, 2005; Powe & Weinrich, 1999), has focused on cancer-related fatalism. Overall, the construct of fatalism has been explicated and operationalized in a variety of ways. Its definition has ranged from a passive denial of personal control (Neff & Hoppe, 1993), to the belief that the onset of a certain serious disease (e.g., cancer) basically means death penalty for the individual (e.g., Powe et al., 2005). Quite a few scholars have developed scales to measure fatalism (e.g., Cohen & Nisbett, 1998; Cuellar, Arnold, & Gonzalez, 1995; Egede & Bonadonna, 2003; Kalichman, Kelly, Morgan, & Rompa, 1997; Neff & Hoppe, 1993; Straughan & Seow, 1998; Wade, 1996; also see Powe & Finnie, 2003 for a review). The most widely used measurement instrument has been the Powe Fatalism Inventory (Heiney, Gullatte, Hayne, Powe, & Habing, 2016; Powe, 1995).

The diversity of fatalism measurement instrument can be attributed to various conceptualizations of the construct. Scholars have proposed that the essential characteristics of fatalism encompass some combination of the following dimensions: (a) perceived lack of control over one’s health (e.g., Chavez, Hubbell, Mishra, & Valdez, 1997; Davison, Frankel, & Smith, 1992; Kohn & Schooler, 1983; Neff & Hoppe, 1993; Straughan & Seow, 1998; Wade, 1996), (b) predestination of a disease or health condition fate, luck, and destiny (e.g., Cohen & Nisbett, 1998; Davison et al., 1992; Straughan & Seow, 1998; Vetter, Lewis, & Charny, 1991), and (c) powerlessness, hopelessness, and meaninglessness that arise because of the dire severity of the disease or health issue (e.g., Scheier & Bridges, 1995; Powe & Johnson, 1995). Integrating the existing body of literature, Shen, Condit, and Wright (2009) argue that fatalism is a construct that is cognitive in nature and applicable across a variety of health topics above and beyond cancer. Thus, fatalism is conceptualized as a set of health beliefs that encompasses such dimensions as predestination, pessimism, and attribution of one’s health to luck. There is also evidence for the factor structure of a fatalism scale that encompasses these dimensions, and the construct validity of the scale (Shen et al., 2009).

Culture and Fatalism

The concept of fatalism is linked to a common cultural value in the Latino community referred to as fatalismo (Falicov, 1998), and found in many other cultures as well. It has been suggested that ethnic and religious background might impact on fatalism such that certain ethnic groups such as Latinos and African Americans are more likely to be fatalistic (e.g., Cuellar et al., 1995; Powe, 1997; Niederdeppe & Levy, 2007). Data from a nationally representative sample responding to a validated uni-dimensional fatalism scale (N=1145; Shen et al., 2009) suggested that individuals living in the southern United States were by far the most fatalistic, and those living in the Midwest were the least fatalistic. Women living in the South were the least fatalistic compared to women living in the other three regions. Race and ethnicity were differentiating factors as well: “Non-Hispanic Other (i.e., neither white or black)” reported significantly higher scores on a fatalism scale than other ethnic groups including “non-Hispanic White,” “non-Hispanic Black,” “Hispanic” and “non-Hispanic more than two races,” whose scores were not significantly different from each other. These results indicate that, inconsistent with existing literature, Hispanics and African Americans might not be among the most fatalistic ethnic groups. The most important factors associated with fatalism are education and income (e.g., Powe, 2001; Mayo, Ureda, & Parker, 2001). Shen et al. (2009) reported that low income and low education individuals have higher levels of fatalism. In addition, older individuals tend to be more fatalistic than younger ones.

Fatalism and Health

Research has often revealed a pattern of negative association between fatalistic beliefs and health outcomes. Studies have reported that fatalistic beliefs are correlated with lower intentions to change behavior and with a variety of negative health outcomes regarding cancer (Powe & Finnie, 2003), cardiovascular diseases (e.g., Urizar & Sears, 2006), diabetes (e.g., Egede & Bonadonna, 2003), non-compliance with treatment/regimen (e.g., Welch, 2011), coping with extreme stress (e.g., Yeh, Inman, Kim, & Okubo, 2006); coping with HIV/AIDS risks (e.g., Varga, 2001), smoking attitudes and behavior (e.g., Schnoll et al., 2002; Unger et al., 2002), lower social function (e.g., Urizar & Sears, 2006), suicidal behavior (e.g., Travis, 1990; Roberts, Roberts, & Chen, 1998), quality of life among HIV-infected women (e.g., Sowell et al., 1997), attitude toward safety and accident prevention (e.g., Rundmo & Hale, 2003), unsafe sex practices (e.g., Kalichman et al., 1997), decreased likelihood to use seatbelts for children (e.g., Omari & Baron-Epel, 2013), minimal HIV knowledge (e.g., Ramirez et al., 2002), depression (e.g., Mirowsky & Ross, 1984; Neff & Hoppe, 1993; Roberts, Roberts, & Chen, 2000; Wheaton, 1980) and suicidality (e.g., Roberts et al., 2000). A recent meta-analysis (Cohen & Esparza-Del Villar, 2015) confirmed the association between fatalism and some risky health behavior, including non-compliance and lack of preventive and screening behaviors, with the strongest association found in studies on fatalism and lack of preventive behavior. Notably, however, findings from the latter meta-analysis suggest that the fatalism-health behavior relationship may be smaller than previously suggested by research investigators and theorists.

Due to possible impact of culture, values, and socio-economic status, fatalistic beliefs may further exacerbate existing health disparities (Powe & Johnson, 1995). Physical and mental health disparities between ethnic groups have been documented for several years and were recently addressed by the Center for Disease Control and Prevention (CDC) in the Health Disparities and Inequalities Report (CHDIR; CDC, 2013). Results from this population-based survey indicate that individuals who identify as African American, Latino/a, and/or American Indian experience poorer health outcomes and access to health care than individuals who identify as European American and/or Asian American. Given that health disparities have existed for ethnic minorities as compared to European Americans for decades and are likely a result of inequalities in social systems, it is possible that ethnic minority individuals might view the health disparity obstacle as being insurmountable and therefore out of their control (Falicov, 1998). This feeling of helplessness may lead to fatalistic thinking and/or be exacerbated by pre-existing fatalistic beliefs, possibly explaining fatalism’s relationship to poor health outcomes (Powe & Johnson, 1995).

Locus of Control

Explicated as such, the concept of fatalism overlaps with the concept of control (Falicov, 1998; Omari & Baron-Epel, 2013), in particular locus of control (Rotter, 1966). Locus of control refers to the extent to which individuals believe they are in control of events that affect them. Locus of control can either be internal or external. Individuals with an internal locus of control believe they have the control and that their life is determined by their own actions. On the other hand, individuals with an external locus of control perceive their life is controlled by environmental factors they can’t change, or by chance or fate. Rotter (1966) believes that locus of control develops as a result of learned social reinforcement, specifically, an external locus of control results when one’s efforts to succeed are ineffective, furthering the thought that one’s behavior has no relationship to outcomes.

Rotter (1966) developed a 23-item, forced choice scale to assess locus of control (the scale has 6 additional filter items). Individuals’ scores from the scale are dichotomized into either external (high scores) or internal (low scores) loci. Scholars have developed alternative measures for locus of control and in specific groups such as children (the Stanford Preschool Internal-External Control Index; Mischel et al., 1974) and adults (the Internal Control Index; Duttweiler, 1984); and for application in specific content domains such as health psychology and industrial and organizational psychology (see Furnham & Steele, 1993 for a review). Duttweiler (1984) proposes that internal locus of control consists of multiple dimensions including cognitive processing, autonomy, resistance to social influence, self-confidence, and delay of gratification. It is deemed as a better instrument than the original Rotter (1966) scale, mainly because the Duttweiler (1984) scale avoided the forced-choice format and issues related to social desirability and heterogeneity (as revealed by factor analyses).

Culture and Locus of Control

In his original conceptualization, Rotter (1966) suggests that culture might be a factor that shapes meaning, values, and outcomes associated with control; hence the conceptualization of control should vary across cultures (Cheng, Cheung, Choi, & Chan, 2013; Kiran Kumar, 1986; Lu, Kao, Cooper, & Spector, 2000). Western cultures, which are more individualistic, tend to value ideals such as independence, self-reliance, individual control, and self-sufficiency (Oettingen, 1995). Hence, individuals with such cultural backgrounds are more likely to have an internal locus of control. On the other hand, Eastern cultures, which are more collectivistic, tend to value ideals such as interdependence, adaptability, community, and commitment to family (Bond & Smith, 1996; Morling, Kitayama, & Miyamoto, 2002). Therefore, individuals with such cultural backgrounds are more likely to have an external locus of control. Research has consistently found this pattern: Individuals of collectivistic cultures and/or those who identify as an ethnic minority have higher external locus of control scores than those from individualistic cultures and/or are part of the ethnic majority (Lu et al., 2000; Rossier, Dahourou, & McCrae, 2005; Twenge, Zhang, & Im, 2004; Wade, 1996). African Americans in the United States tend to be more external than white Americans, even when socio-economic status is controlled (Berry, Poortinga, Segall, & Dasen, 1992; Shiraev & Levy, 2004).

Locus of Control and Health

By definition, and as a source of social desirability, an internal locus of control is viewed as more positive and an external locus as more negative (Abramson et al., 1989; Mirowsky & Ross, 1984; Ryan & Deci, 2000; Seligman, 1975; Twenge et al., 2004). The theory of learned helplessness (Seligman, 1975) and the hopelessness model (Abramson et al., 1989) also suggest that an external locus of control is negatively linked to health (Roberts et al., 2000; Tobin & Raymundo, 2010; Welch, 2011).

Better evidence for the negative impact of external locus of control on health came from domain-specific research than locus of control in general. There is evidence that an external locus of control is linked to obesity (Saltzer, 1982), poor mental health (Wood & Letak, 1982), cancer (Pruyn et al., 1988), poor diabetes management (Furnham & Steele, 1993), depression (Cheng, Cheung, Choi, & Chan, 2013), etc. (see Norman & Bennett, 1995 for a review). It should be noted that there has been no clear evidence that an internal locus of control would be positively associated with health outcomes.

Researchers have also examined locus of control specifically in the content domain of health—health locus of control (HLOC), which refers to one’s attribution of their own health to either personal or environmental factors including internality, powerful others, and chance (e.g., Levenson, 1981; Wallston, Wallston, & DeVellis, 1978). A recent meta-analysis (Cheng, Cheung, & Lo, 2016) found that the relationships between health locus of control and specific health behaviors (such as diet, exercise, quality of life, depression, and anxiety) were rather weak, and moderated by demographic variables including gender and age compositions, individualism, and power distance. The association between health locus of control and global health appraisals were significant and substantially larger than the associations with specific health behaviors.

Discussion of the Literature

The literature in both fatalism and locus of control suggests that it is dysfunctional for individuals to hold beliefs that their health is not a function of their own behaviors and action, but rather determined by external factors, fate, or luck. The consistent pattern of negative associations between such cognition and health behavior and outcomes tends to further confirm this dysfunctional view. However, such evidence came predominantly from correlational studies; and thus causal inferences can’t be established. More specifically, the direction of possible causation could go either way—fatalism and external locus of control could lead to adoption of risky health behaviors and/or result in poorer health status; or risky behaviors and poorer health status could lead to the adoption of fatalistic views and beliefs that are characterized by an external locus of control arising from one’s low socio-economic status, failed attempts to improve one’s health status, and major diseases.

Recent meta-analyses suggest that the association between fatalism and health outcomes (Cohen & Esparza-Del Villar, 2015) as well as that between health locus of control and health outcomes (Cheng et al., 2016) might not be as strong as researchers have suggested in individual primary studies. A plausible explanation might lie in the differences between fatalism and locus of control as global cognitive appraisal structures and such beliefs specific to one particular health issue/behavior. The TACT (Target Action, Context & Time) approach to the attitude-behavior relationship (Ajzen & Fishbein, 1977) suggests that when the mismatch between the TACT elements in the measures of beliefs (i.e., fatalism and locus of control) and those in the health measures would reduce the observed associations in empirical studies. Such a mismatch tends to be more likely in a meta-analytic study than in a primary study. The results from Cheng et al. (2016) are consistent with this argument: The associations between health locus of control scales and global health appraisals were larger than those between health locus of control scales and individual health behaviors.

Research by Condit and her associates (Cheng, Condit, & Flannery, 2008; Condit, Gronnvoll, Landau, Shen, Wright, & Harris, 2009) suggests that it is a real possibility that fatalistic beliefs might be the outcomes of one’s existing risky behaviors and/or poor health status. In their view, fatalism might not always be dysfunctional; rather, it might help low income, low education populations to cope with difficult situations. It can function as a sense-making structure that accommodates stress, uncertainty, and face-saving. To some extent, failed previous attempts’ lack of improvement despite effort could lead to learned helplessness and individuals giving up trying. Their health status (which needs improvement) vis-à-vis their lack of health behavior and/or non-compliance with medical treatment would probably result in cognitive dissonance because they are inconsistent and do not make sense. Since cognitive dissonance is uncomfortable, individuals are motivated to reduce it. To reduce cognitive dissonance, one thing individuals may do is to add cognition that is consonant with their lack of health behavior, or external justifications (i.e., environmental factors) for their (lack of) actions. Condit et al. (2009) suggest that there might exist such a “two-track” model used by most people in accounting for health outcomes: An external track and an internal track. In the external track, individuals have a script for attributing a health outcome to external factors they can’t control (e.g., genes). In the internal track, they have a script for attributing health to one’s own behavior, which they can control. It is possible that individuals might use fatalism to make sense of the obvious discrepancy between the behavior track and their risky behaviors (or lack of healthy behavior). On the other hand, the behavior script makes perfect sense when explaining other people’s health outcomes. If this is the case, it means that individuals could be using such fatalistic reasoning to justify their lifestyle, although they might be well aware that their behaviors need to be modified. In other words, for these individuals, fatalism might be a mechanism to reduce cognitive dissonance caused by the discrepancy between attitude/intention and actual behavior.

Given the link between risky health behaviors/poorer health outcomes and beliefs such as fatalism and external locus of control, there is merit in research on intervention strategies that aim to mitigate such maladaptive beliefs. Fatalism and locus of control can be general concepts that are dispositional and applicable across a variety of topics (i.e., a stable set of shared attributions across a host of health issues/behaviors); or they can be domain-specific, situational, and applicable to a specific issue (e.g., diet and exercise). While dispositional tendencies are more difficult to change because they are global and/or stable; situational and domain-specific beliefs are more likely to mend because they are more specific and concrete. Certain genes might increase a person’s risks for a particular disease (e.g., TCF7L2, which affects insulin secretion and glucose production, is associated with risk for type 2 diabetes). Dispositional factors like genes are difficult, if not impossible to change. On the other hand, certain behaviors including diet and exercise are also associated with risk for diabetes. They are more under one’s control and more open to change. The idea of gene-behavior/environment interaction (e.g., Tabery, 2007) suggests that while one’s genes might exacerbate the negative consequences of maladaptive health behaviors, they can also magnify the positive impact when the behaviors are adaptive and functional. If this is the case, then teaching the notion of gene-environment/behavior interaction, that is, one’s health is a joint function of their genes and their behavior, might be a promising way to mitigate fatalistic beliefs (presumably partially due to the perception of gene determinism) (Shen & Condit, 2011). Condit and Shen (2011) propose that metaphors might an effective way to convey the mathematic ideas involved in gene-environment interaction. Shen, Wright, Cheng, Flannery, Harris, and Condit (2012) compared the impact of four types of messages: (1) behavior message (i.e., behavior as the only determinant of one’s health), (2) gene determinism message (i.e., gene is the only determinant of one’s health), (3) gene-behavior additive message (i.e., both gene and behavior impact health, but in an additive way), and 4) gene-behavior interactive message (i.e., gene and behavior interaction impact health). Shen et al. reported that, first, it is possible to use metaphors to teach lay individuals the idea of gene-behavior interaction, despite that concepts of mathematical interaction are notoriously difficult to teach. The gene-behavior interactive message led to significantly better understanding of the gene-behavior relationship than any other messages combined. This message effect was more pronounced when the gene-behavior interactive message was presented as the second message in the sequence (i.e., after a message that does not teach the gene-behavior interaction idea). In other words, the interactive message led to the best understanding of the gene-behavior relationship, particularly when it was accompanied by another message, but presented second in the sequence.

Second, the gene-behavior interaction message helps to mitigate fatalistic beliefs and boost control beliefs. There was a significant main effect of message type on perceived control over one’s health. The gene-behavior interaction messages led to higher scores on perceived control over one’s health than gene-determinism message, although not significantly better than the message, only highlighting the impact on health from behavior. Again, this message effect is pronounced when the gene-behavior interaction message was presented with another message, but second in the sequence.

The gene-behavior interaction message also resulted in higher motivation to engage in healthy behavior. The gene-behavior interactive message, as well as the behavior only message produced significantly higher motivation than the gene determinism message or the gene-behavior additive message. Again, the message effect on motivation from the gene-behavior message was more pronounced when it was accompanied by another message but presented second in the sequence. These results showed that (1) the idea of gene-behavior interaction can be effectively taught to lay individuals; and (2) more importantly, the gene-behavior interactive message does the best job of reducing fatalistic beliefs, enhancing control beliefs, and motivating the respondents to change their behaviors, but only when it was presented second in the sequence.

These findings came from initial research studies on efforts to mitigate fatalistic beliefs and related maladaptive beliefs on health attributions. They tend to be rather exploratory and results are not conclusive. Moreover, only one possible intervention strategy (i.e., gene-behavior interaction education) was proposed and studied. Given that certain demographic groups (due to age, gender, ethnicity) are more prone to fatalism and external locus of control, and they also tend to be at the more disadvantageous positions when it comes to health disparity, it might be imperative that research studies should not only examine the association between maladaptive attributions and health behaviors/outcomes, but possible strategies and interventions to mitigate such cognitive beliefs. More theory drive and more rigorous studies are needed in this regard.

Further Reading

Cheng, C., Cheung, S., & Lo, B. C. Y. (2016). Relationship of health locus of control with specific health behaviors and global health appraisals: A meta-analysis and effects of moderators. Health Psychology Review, 10(4), 460–477.Find this resource:

Cohen, L. D., & Esparza-Del Villar, O. A. (2015). Fatalism and health behavior: A meta-analytic review. Colección Reportes Técnicos de Investigación, Serie ICSA, 26, 8–47.Find this resource:

Duttweiler, P. C. (1984). The Internal Control Index: A newly developed measure of locus of control. Education and Psychological Measurement, 44, 209–221.Find this resource:

Levenson, H. (1981). Differentiating among internality, powerful others, and chance. In H. M. Lefcourt (Ed.), Research with the locus of control construct (Vol. 1, pp. 15–63). New York: Academic Press.Find this resource:

Powe, B. D., & Finnie, R. (2003). Cancer fatalism: The state of the science. Cancer Nursing, 26, 454–465.Find this resource:

Shen, L., Condit, C., & Wright, L. (2009). The psychometric property and validation of a fatalism scale. Psychology & Health, 24, 597–613.Find this resource:


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