Education differences in cancer fatalism: The role of information-seeking experiences

Abstract

Cancer fatalism is the belief that cancer is uncontrollable and lethal. Individuals with less education are more likely to hold fatalistic beliefs about cancer, but the mechanism accounting for the relationship is unknown. We tested whether negative health information seeking experiences explain this relationship. Structural equation modeling was used to test this relationship across three datasets from the Health Information National Trends Survey. Across all datasets, the model showed good fit: Cycle 1 (coefficient of determination = .11, comparative fit index = .96, root mean square error of approximation = .047), Cycle 2 (coefficient of determination = .06, comparative fit index = .96, root mean square error of approximation = .046), and Cycle 3 (coefficient of determination = .08, comparative fit index = .95, root mean square error of approximation = .052). The link between lower education level and higher cancer fatalism was partially mediated by negative health information seeking experiences.

Keywords

cancer cancer fatalism education fatalism health information seeking

Individuals’ beliefs about illness can influence how they choose to prevent or cope with illness. Fatalism is the belief that health issues are out of the individual’s control, and that outcomes are negative and fatal (Powe and Finnie, 2003; Straughan and Seow, 1998). Cancer fatalism, then, is the belief that a cancer diagnosis is not preventable and fatal, and can be described as a multidimensional construct that encompasses beliefs characterized by pessimism, helplessness, and confusion about ways to avoid getting cancer (Cohen, 2013; Niederdeppe and Levy, 2007; Shen et al., 2009).

Cancer fatalism is an important barrier to participation in cancer screening and treatment (Beeken et al., 2011; Freeman, 1989; Mayo et al., 2001; Powe and Finnie, 2003). It is correlated with avoidance of cancer-related information (Miles et al., 2008) and delayed treatment (Facione et al., 2002). It also predicts non-adherence to cancer-preventative behaviors such as exercising, having a healthy diet, and not smoking (Niederdeppe and Levy, 2007). Thus, cancer fatalism is associated with a myriad of unhealthy behaviors, which may self-fulfill the fatalistic belief that cancer is unavoidable.

Cancer fatalism is more commonly observed in individuals with low levels of education, low income, and ethnic minorities (Freeman, 1989; Powe, 1996). However, a nationally representative survey found little evidence for ethnic differences in fatalistic beliefs, suggesting that associations between ethnicity and fatalism might be attributable to differences in socio-economic status (SES), particularly education (Niederdeppe and Levy, 2007).

Few attempts have been made to understand the relationship between education and cancer fatalism. This study examines whether individuals with less education have more negative experiences associated with seeking health information, and whether these negative experiences contribute to cancer fatalism.

Health information seeking experiences

Health information seeking is actively looking for health information (Niederdeppe et al., 2007), which is fairly common. Specifically regarding Internet searches, 113 million Americans use the Internet to seek health information (Anker et al., 2011). Furthermore, individuals with a personal cancer history or with a family history of cancer seek health information more than individuals with no cancer history (Arora et al., 2008). Specifically related to cancer-related searches, women, more educated, and younger individuals are more likely to seek cancer-related health information (Arora et al., 2008).

Health information seeking is associated with many positive outcomes: improved communications between patient-providers (Rees and Bath, 2001), reduced anxiety about illness (Van der Molen, 1999), and better adjustment to illness (Rees and Bath, 2001). Most studies of health information seeking (Czaja et al., 2003; Johnson, 1997) have attempted to explain why individuals choose to seek health information, but fewer studies have investigated the experience of health information seeking, or how that experience itself might affect health outcomes. Not all individuals are satisfied with their information-seeking experiences. After an unsuccessful search for information, individuals may reject the information they found (Horowitz et al., 1983), feel frustrated, and experience deflated self-esteem (Hudson and Danish, 1980). Moreover, negative health information seeking experiences are common, with a one-third to one half of individuals reporting something negative about their search for health information (Arora et al., 2008). In a sample of Japanese women with breast cancer, almost half the sample reported negative health information seeking experiences (Nakashima et al., 2012).

Health information seeking experiences and education

While few studies have examined why individuals are not satisfied with the experience of seeking health information, we can suggest who might have negative health information seeking experiences. Individuals need to be able to understand the information they seek. It is easy to find information, especially using the Internet, but finding relevant and helpful information may be more difficult. For example, it may be easy to find general information about breast cancer, but more difficult to find information about how to obtain a mammography or to understand one’s own personal risks and benefits of mammography.

One factor associated with difficulty of seeking and finding health information is education (Anker et al., 2011). Education is strongly associated with skills that are helpful in understanding health information (Echt and Burridge, 2011; Paasche-Orlow et al., 2005; Von Wagner et al., 2011). Viswanath and Ackerson (2011) suggest that education helps individuals read and understand health information, and also allows individuals to more successfully identify aspects of the information that are most important. It is thus unsurprising that higher-educated individuals report more positive experiences with health information seeking (Arora et al., 2008).

Health information seeking experiences and cancer fatalism

It has been suggested that fatalistic beliefs are often spurred by ambiguity surrounding the way in which scientific and medical information is communicated to the public (Schmidt, 2007). Information from different sources can be conflicting: causing confusion, mistrust, and uncertainty (Brashers, 2001). Information can help to decrease uncertainty when it serves to distinguish different options and to attribute meaning to events. However, it can also be distressing when the information around uncertainty is appraised as threatening or overwhelming (Brashers, 2001).

Some have speculated about an association between health information seeking experiences and cancer fatalism (Arora et al., 2008). Using the National Cancer Institute’s (NCI) 2003 Health Information National Trends Survey (HINTS), Arora et al. (2008) found that negative information-seeking experiences were common, with more than one-third of respondents reporting the information found was too hard to understand. Respondents with negative information-seeking experiences were also more likely to endorse cancer fatalism (Arora et al., 2008). Helft (2008) suggested this link could be due to less-educated individuals having a poorer, more fatalistic understanding of cancer to begin with, or that less-educated individuals misunderstanding information or finding inaccurate information which contributes to more fatalistic beliefs.

Although no previous studies have explored whether health information seeking experiences are related to cancer fatalism, the evidence does suggest that negative information-seeking experiences might contribute to cancer fatalism. In this study, we hypothesize that the relationship between education and cancer fatalism could be explained in part (i.e. mediated) by negative health information seeking experiences. We tested this hypothesis using three cycles of data from the NCI’s HINTS.

Study 1

Method

Study design and participants

We first analyzed cross-sectional data from Cycle 1 of the NCI’s 2012 HINTS (2012). HINTS was deemed exempt from Institutional Review Board (IRB) review by the National Institutes of Health (NIH) Office of Human Subjects Research. All research was conducted in accordance with the principles expressed in the Declaration of Helsinki.

Data were collected from October 2011 to February 2012 in a single-mode survey. Participants were mailed the survey packets along with a $2 incentive. The full HINTS sample consisted of 3959 participants, corresponding to a response rate of 37 percent. The analytical sample for this study consisted of those who had reported that they had ever looked for information about health or medical topics from any source and completed the items on health information seeking experiences (N = 3070; 78% of full sample). Descriptive information and scale means are presented in Table 1.

Table 1.

Demographic composition of all HINTS 4 cycles.

Characteristics	Cycle 1, n (%)	Cycle 2, n (%)	Cycle 3, n (%)
Sex
Male	1158 (48.50)	496 (41.9)	864 (46.8)
Female	1854 (51.50)	915 (58.0)	1459 (53.2)
Age range (years)
18–34	476 (30.4)	194 (24.2)	350 (27.4)
35–39	224 (9.5)	97 (9.2)	157 (10.2)
40–44	254 (9.2)	120 (8.6)	160 (10.0)
>45	2067 (50.9)	984 (58.0)	1647 (52.3)
Education
Less than HS	182 (12.85)	55 (7.1)	154 (5.4)
HS	517 (23.08)	218 (14.1)	428 (17.5)
Some college	951 (31.16)	400 (39.6)	710 (31.6)
College or more	808 (20.47)	400 (21.9)	614 (21.6)
Post-bac	558 (12.44)	329 (17.4)	422 (14.3)
Race
White	1999 (66.74)	914 (74.8)	1308 (69.6)
Black	420 (11.42)	163 (8.0)	301 (10.2)
Hispanic	310 (14.47)	161 (10.5)	328 (12.7)
Asian	125 (5.00)	30 (4.4)	88 (5.4)
American Indian	15 (0.50)	4 (0.1)	3 (0.00)
Native Hawaiian	7 (0.32)	2 (0.2)	1 (0.00)
Multiple races	54 (1.47)	43 (2.0)	70 (1.9)
Family ever had cancer
Yes	1960 (71.87)	1082 (79.8)	2000 (68.2)
No	653 (28.13)	264 (20.2)	754 (31.8)
Fatalism	M = 2.51 (SD = .69)	M = 2.47 (SD = .66)	M = 2.49 (SD = .69)
Information seeking	M = 2.05 (SD = .85)	M = 2.07 (SD = .82)	M = 2.04 (SD = .86)

HS: high school.

Sample sizes are actual N in the analytical samples, but percentages reflect application of survey weighting as used in analyses.

Measures

Cancer fatalism

Three items in the HINTS survey, measured on a 4-point Likert scale ranging from 1 (“strongly disagree”) to 4 (“strongly agree”), have been consistently used in prior work to assess fatalistic beliefs about cancer (Niederdeppe and Levy, 2007). The items were as follows: “It seems like everything causes cancer,” “There’s not much you can do to lower your chances of getting cancer,” and “There are so many different recommendations about preventing cancer, it’s hard to know which ones to follow.” Although frequently used, the internal consistency of this three-item is often low, as it was in HINTS Cycle 1 (α = .61). For this reason, we employed structural equation modeling (SEM) in order to model cancer fatalism as a latent variable and therefore reduce measurement error.

Cancer fatalism has been conceived and operationalized in a variety of ways (Shen et al., 2009). Although previous HINTS studies conducted on cancer fatalism have used the three-item scale to measure cancer fatalism (Niederdeppe and Levy, 2007), it has also been suggested that the item “Too many recommendations” may represent a different construct of cancer information overload (Jensen et al., 2014) or perceived ambiguity regarding cancer prevention recommendations (Han et al., 2007). Thus, we first sought to test three possible measurement models of our key theoretical constructs, with each model differing in its treatment of the “too many recommendations …” item. Three models were tested: (1) a model in which “Too many recommendations” served as an indicator of a latent construct of cancer fatalism (cf. Niederdeppe and Levy, 2007), (2) a model in which “Too many recommendations” was a perfect indicator of a latent factor representing cancer information overload (cf. Jensen et al., 2014), and (3) a model in which “Too many recommendations” was an indicator of the latent factor of health information seeking. In each model, each latent factor correlated freely with all other latent factors.

Health information seeking experiences

The 2012 HINTS first asked respondents if they had sought information. If they answered yes, then respondents were asked whether they have ever looked for information about health or medical topics from any source, and those responding yes were then asked three items designed to measure health information seeking experiences. Due to space limitations on HINTS, three items (from six overall items) were used from the Information-Seeking Experience Scale (Arora et al., 2008). The stem for each question was “Based on your most recent search for information about health and medical topics, how much do you agree or disagree?” The three responses options were “It took a lot of effort to get the information you needed,” “The information you found was hard to understand,” and “You felt frustrated during your search for the information.” These items were measured on a 4-point scale, with higher scores representing more negative health information seeking experiences (α = 85).

Education

Education was measured by a five-category variable (less than high school; high school; some college; bachelor’s degree; and post-baccalaureate degree).

Analytic strategy

The analytic strategy for Study 1 and subsequent studies were identical. In all analyses, we used jackknife replicate sample weights included in the HINTS dataset. These weights correct for oversampling so that estimates generalize to the American adult population as a whole (see http://hints.cancer.gov for further details). To test whether information-seeking experiences mediate the relationship between education and cancer fatalism, we used SEM in Stata 13 (“Stata Statistical Software,” 2013) which can incorporate the survey design of HINTS. Mediation was assessed with the “effects” option, which utilizes a product-of-coefficients approach to testing mediation (Sobel, 1987). Across the three studies, missing data on family history of cancer ranged from 5.9 percent (Study 3) to 14.9 percent (Study 1) and missing data on ethnicity ranged from 4.6 percent (Study 1) to 11.4 percent (Study 3); missing data on all other analytic variables was negligible (<3%). To account for the missing data, we used maximum likelihood estimation with missing data (MLMV). When using both MLMV and survey estimation, the only fit statistic available is the coefficient of determination (CD), and no clear guidelines exist for interpreting acceptable fit values for CD. Thus, to provide a broader view of model fit, we report other fit indices derived from the analyses that did not take into account the survey weights. These fit indices include the comparative fit index (CFI) and the root mean square error of approximation (RMSEA). CFIs above .90 represent good model fit. RMSEA values of .01, .05, and .08 indicate excellent, good, and mediocre fit, respectively (MacCallum et al., 1996). Because the CFI and RMSEA were derived from unweighted analyses, they should be taken as approximations although we note that all results of the unweighted and weighted analyses are substantively similar. To maintain consistency of models across studies, no post-hoc modifications were made to any of the models to improve fit.

Results

Measurement of cancer fatalism

CFIs were .99, .99, and .85, respectively, for the measurement models 1, 2, and 3. RMSEAs were .030, .032, and .162, respectively. Thus, model 1 had both good fit and the best fit of all models, and was also more parsimonious than model 2. Accordingly, we operationalized cancer fatalism using three indicators including the “Too many recommendations” item. This is in accordance with conceptualizations of fatalism as a multidimensional construct, in which uncertainty and confusion about cancer and preventive actions are conceived to reflect underlying fatalistic beliefs of lack of control over cancer occurrence (Cohen, 2013).

Relationship between education, information seeking, and cancer fatalism

Next, a structural model was estimated which predicted fatalism from education, health information seeking, and sociodemographic factors of non-Caucasian ethnicity, gender, age, and family history of cancer. The estimated model (Figure 1(a)) presented a generally good fit to the data, CD = .11, CFI = .96, RMSEA = .047. Ethnicity, gender, age, and family history predicted fatalism (all ps < .10), with white ethnicity, female gender, younger age, and family history of cancer associated with greater cancer fatalism.

Figure 1.

Structural equation model results for HINTS (a) Cycle 1, (b) Cycle 2, and (c) Cycle 3.

As expected, education was a significant predictor of both cancer fatalism β = −.19, p < .001, and health information seeking experiences, β = −.18, p < .001. Health information seeking experiences also predicted cancer fatalism, β = .22, p < .001. Most importantly, education significantly predicted cancer fatalism through the influence of health information seeking, with the indirect (i.e. mediated) effect being statistically significant, β = −.04; B = −.02, SE = .006, t = −3.32, p = .002, 95% confidence interval (CI) = −.032 to −.007. Thus, a significant portion of the relationship between lower education and greater cancer fatalism was attributable to difficulties with health information seeking experiences.

Study 2

In June 2013, Cycle 2 data from the HINTS 4 data collection was released. As the Cycle 2 dataset contained a number of the same—or similarly worded—items, we sought to replicate our findings with Cycle 2 data.

Method

Study design and participants

Cycle 2 data were conducted from October 2012 to January 2013. Data collection protocol for Cycle 2 was similar to that of Cycle 1, as participants were mailed the survey and a $2 incentive. The full sample was 3630 participants, corresponding to a response rate of 40 percent. The Cycle 2 survey asked respondents whether they have ever looked for information about cancer from any source, and those responding yes were asked items regarding health information seeking experiences regarding cancer. Respondents who completed these latter items (N = 1431; 40% of full sample) formed the analytical sample for this study.¹ Descriptive information and scale means are presented in Table 1.

Measures

Cancer fatalism

The same cancer fatalism items were assessed as in Study 1 (α = .60).

Health information seeking experiences

These items were similar to those used in Cycle 1, except that they were specific to cancer such that the stem read “Based on your most recent search for information about cancer, how much do you agree or disagree with each of the following statements?” The three items were as follows: “It took a lot of effort to get the information you needed,” “The information you found was hard to understand,” and “You felt frustrated during your search.” These items were measured on a 4-point scale, with higher scores representing more negative cancer-related health information seeking experiences (α = .84).

Education

Again, we used a five-level education variable (less than high school; high school; some college; bachelor’s degree; and post-baccalaureate degree).

Results

Measurement of cancer fatalism

In the same manner as in Study 1, we tested three possible measurement models of our key constructs. CFIs were .99, .99, and .78 for models 1, 2, and 3, respectively. RMSEAs were .030, .031, and .152, respectively. Again, model 1—in which “Too many recommendations” represented the latent factor of cancer fatalism—had good fit, and was also more parsimonious than model 2. Thus, we operationalized cancer fatalism using three indicators which included the “Too many recommendations” item (cf. Niederdeppe and Levy, 2007).

Relationship between education, information seeking, and cancer fatalism

Figure 1(b) shows the structural model relating education, health information seeking, cancer fatalism, and sociodemographic covariates. This model also provided generally good fit to the data, CD = .06, CFI = .96, RMSEA = .046. In this sample, none of the sociodemographic covariates predicted cancer fatalism.

Again, education predicted both cancer fatalism β = −.20, p < .01, and health information seeking experiences, β = −.11, p < .01. Health information seeking experiences also predicted cancer fatalism, β = .23, p < .001. Education significantly predicted cancer fatalism through the influence of health information seeking, with the indirect effect being significant, β = −.026; B = −.01, SE = .003, t = −2.43, p = .02, 95% CI = −.014 to −.001. Thus, a portion of the relationship between lower education and greater cancer fatalism was attributable to difficulties with health information seeking experiences regarding cancer specifically.

Study 3

In June 2014, HINTS Cycle 3 was released. Cycle 3 contained the exact same items as Cycle 1. Thus, we tested our hypothesis again using Cycle 3 data.

Method

Study design and participants

Cycle 3 data were conducted from September 2013 to December 2013. Data collection protocol for Cycle 3 was similar to that of Cycles 1 and 2, as participants were mailed the survey and a $2 incentive. The full sample was 3185 participants, corresponding to a response rate of 35 percent. The analytical sample was comprised of respondents who reported seeking health information from any source and who completed the health information seeking experiences items (N = 2370; 74% of full sample). Descriptive information and scale means are presented in Table 1.

Measures

Cancer fatalism

The same cancer fatalism items were assessed as in Studies 1 and 2 (α = .60).

Health information seeking experiences

Again, we used three items to assess health information seeking experiences. These items were identical to those in Study 1 (α = .84).

Education

Education was measured in the same manner as in Studies 1 and 2.

Results

Relationship between education, information seeking, and cancer fatalism

As in Studies 1 and 2, we operationalized cancer fatalism using three indicators which included the “Too many recommendations” item. Figure 1(c) shows the structural model relating education, health information seeking, cancer fatalism, and sociodemographic covariates. This model also provided good fit to the data, CD = .08, CFI = .95, RMSEA = .052. In this sample, younger age (p = .02) and female gender (p = < .01) predicted greater cancer fatalism; all other sociodemographic covariates were not significant.

Consistent with Studies 1 and 2, education predicted both cancer fatalism β = −.18, p = .001, and health information seeking experiences, β = −.13, p < .001. Health information seeking experiences also predicted cancer fatalism, β = .27, p < .001. Education significantly predicted cancer fatalism through the influence of health information seeking, with the indirect effect being statistically significant, β = −.033; B = −.016, SE = .006, t = −3.05, p = .004, 95% CI = −.027 to −.005. Thus, the relationship between lower education and greater cancer fatalism was partially attributable to difficulties with health information seeking experiences.

Discussion

Cancer fatalism is associated with maladaptive behaviors and non-adherence to both primary and secondary cancer prevention recommendations. Thus, it is important to understand the psychological mechanisms that contribute to cancer fatalism. Consistent with previous findings, our results show—across three distinct datasets—that lower education level relates to both negative health information seeking experiences and higher cancer fatalism. Importantly, SEM analyses revealed that the relationship between low education and high cancer fatalism was partially explained by information-seeking experiences. In Cycle 2, the information-seeking items were specific to cancer, whereas in the other cycles they were general.

Taken together, these findings suggest that negative health information seeking experiences may contribute to cancer fatalism. They also imply that education differences in cancer fatalism may be due in part to negative information-seeking experiences.

These findings suggest that addressing people’s difficulties with seeking health information—particularly among people of lower education levels—may be a way of helping to mitigate cancer fatalism, thereby reducing socio-economic disparities in cancer prevention and treatment (Consedine et al., 2004; Powe and Weinrich, 1999). For example, providing concrete tips on how to find the most relevant information while seeking health information alongside tips on how to manage and evaluate the information could improve health information seeking experiences.

In fact, many health communications do not provide simple recommendations, but rather present the advantages and disadvantages of the behaviors (Dobias et al., 2001). Such ambiguous recommendations may promote uncertainty, which could lead to decreased cancer screening intentions (Han et al., 2007). Similarly, for some behaviors, such as mammography screenings, different organizations recommend different screening guidelines. Interventions providing individuals with information-seeking skills or skills how to manage uncertainty—especially for those from lower education levels–could help individuals to sift through the conflicting recommendations, promoting positive information-seeking experiences. Developing information-seeking strategies that enable individuals to minimize the stress that may come with seeking information while maximizing the ability to process ambiguous or uncertain information may promote understanding, and, ultimately, an increase in physical and emotional well-being (Brashers, 2001). Intervening on information-seeking experience is more practical than increasing general education level. By conducting interventions that help individuals have more positive information-seeking experiences, the likelihood that lower educated individuals will hold fatalistic beliefs could possibly be decreased.

Education level also influences the source from which one seeks health information. Lower educated individuals are more likely to see health information from family and friends and less likely to use the Internet (Longo et al., 2010). An intervention aimed at increasing Internet use among low SES individuals found that participants offered Internet access did use the Internet to seek health information (Viswanath et al., 2013). Thus, Internet interventions aimed solely at increasing use among lower educated individuals could also add a component to help individuals know how to seek and understand credible health information found on the Internet.

Health information seeking experience may be related to health literacy, which has been defined in numerous ways. The Institute of Medicine’s definition is the “degree to which individuals have capacity to obtain, process, and understand basic health information and services needed to make appropriate decisions” (Nielsen-Bohlman et al., 2004). Other aspects of health literacy include knowledge and awareness, such as being able to recognize a health problem and be aware of the need for some sort of help (Frisch et al., 2012). Future studies could examine the link between health literacy and health information seeking experiences to understand the most useful concepts to target for intervention. Specifically, the items from this study that measure health information seeking include an affective component (frustration) that is not often measured in health literacy, and might be independently related to cancer fatalism (Frisch et al., 2012).

Several limitations to these studies should be noted. Although all studies were based on nationally representative samples of non-institutionalized Americans over the age of 18 years, the data used in this study excluded individuals who had never sought health information. This exclusion criterion was most restrictive in Study 2, where less than half of respondents reported seeking health information specifically regarding cancer. Thus, while HINTS is a nationally representative survey, the focus on subgroups in our analyses limits the extent to which the findings may be fully representative of the American population. The surveys had a relatively low response rate although this response rate is similar to other, large national surveys (Dillman, 2000; Kempf and Remington, 2007). Also, due to the cross-sectional nature of the surveys, directionality cannot be established. Other research designs, such as longitudinal or randomized controlled trials of interventions aiming to improve health information seeking experiences should be used to address causality. Moreover, the findings are based on secondary analysis of a large national survey, so measurement of the constructs was limited to a small number of pre-selected items. Other validated measurement tools, namely of cancer fatalism (e.g. Powe, 1995; Shen et al., 2009), should be considered in future research.

Despite these limitations, there are a number of strengths to these analyses. Strengths include the use of a nationally representative sample and consistency of findings across three separate samples, attesting to the replicability and generalizability of the findings. Furthermore, the studies focused on a potentially modifiable factor that likely contributes to cancer fatalism—negative information-seeking experiences—which could be targeted in future interventions.

The present studies show that negative experiences when seeking health information may be implicated in the development or maintenance of cancer fatalism. Presenting the information in an easily understandable manner and enhancing individuals’ ability to search for, select, and interpret health information could help the public to recognize that cancer is not inevitable or untreatable, and there are a number of steps that can be taken to reduce their risk for cancer.

Supplemental Material

Supplemental_Tables – Supplemental material for Education differences in cancer fatalism: The role of information-seeking experiences

Supplemental material, Supplemental_Tables for Education differences in cancer fatalism: The role of information-seeking experiences by Amber S Emanuel, Cristina A Godinho, Christopher Steinman and John A Updegraff in Journal of Health Psychology

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

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