AssessmentThe cancer information overload (CIO) scale: Establishing predictive and discriminant validity
Introduction
Cancer incidence and mortality are declining in the U.S. [1]. Despite this progress, national surveys document that many Americans have troubling beliefs about cancer prevention. More than a quarter of adults (28%) believe “There's not much people can do to lower their chances of getting cancer,” more than half (54%) agree “It seems like almost everything causes cancer,” and more than seventy percent (75%) think that “There are so many recommendations about preventing cancer, it's hard to know which ones to follow” [2]. Some authors suggest that these beliefs demonstrate widespread cancer fatalism, the belief that nothing can be done to prevent (or treat) cancer [3], [4]. Agreement with the three items is negatively correlated with adherence to recommendations concerning smoking, diet, and exercise [3].
It is imperative that behavioral researchers first explicate and confirm the nature of the construct(s) underlying these beliefs in order to identify promising strategies to mitigate them. While we concur that two of the items (not much people can do and everything causes cancer) are likely to constitute fatalistic beliefs, the third item (too many recommendations) appears conceptually distinct and may reflect an alternate construct, cancer information overload (CIO). CIO is defined here as feeling overwhelmed by the amount of cancer-related material in the information environment. Thus, CIO is distinct from similar cognitions (e.g., fatalism, perceived barriers to action) as it focuses on feelings about the cancer information environment rather than the illness (fatalism) or the environment at large (perceived barriers).
We contend that CIO is not a trait, but rather is cultivated by exposure to information about cancer from media, in conversations, and from healthcare providers. For instance, CIO could be a response to “carcinogen of the week” style reporting or overstated research findings in the press [5], [6], [7], [8]. If this is the case, CIO should be generally higher among individuals with a greater volume of exposure to these cancer-related information flows over time. However, individuals with higher CIO will eventually become avoidant of cancer information and perhaps disengage from certain content/channels in backlash [5]. Thus, researchers might observe complex patterns such as a positive correlation for media consumption in general (cultivation) and a negative correlation for attention to cancer/health news (avoidance). In fact, as an aversive motivational disposition, CIO should be more pronounced for individuals with an avoidance temperament [9], [10]. Moreover, individuals with lower cognitive skills (e.g., education, health literacy) should be more prone to CIO.
The model of information overload [5] posits that highly arousing content (e.g., information about cancer) strains already limited storage and processing capabilities resulting in overload [11]. Overload triggers other negative reactions such as increased fatalistic thinking and decreased intentions to engage in cancer-related behaviors (e.g., screening). Concerning the latter, CIO is negatively related to cancer-related behaviors because it undermines other cognitions that drive the performance of these behaviors. For example, the extended parallel process model (EPPM) and the health belief model (HBM) suggest that several cognitions are key predictors of cancer prevention behavior. Thus, CIO should be negatively related to constructs such as self-efficacy, response efficacy, perceived barriers, perceived benefits, and health motivation [12], [13], [14].
To explore these possibilities, two studies were conducted to develop and test the validity of a multi-item measure of CIO. The goal of the research is to determine whether CIO is a valid construct and distinct from cancer fatalism.
Section snippets
Study 1
Study 1 develops and tests a multi-item CIO scale with the goals of identifying the underlying factor structure and eliminating suboptimal items [15]. The validity of the revised scale is further evaluated in terms of prediction – in this case, predicting colonoscopy screening over an eighteen month span – and convergence/divergence with other psychosocial constructs known to predict cancer screening and hypothesized (above) to be related to CIO.
Sample and procedure
Data were collected as part of a larger worksite intervention. Adults (N = 209) were recruited from one of eight worksites (six hospitals and two manufacturing plants) via their human resource representatives. Healthcare and manufacturing workers were targeted as they have lower rates of colonoscopy screening compared to the general population [16]. The HR representatives sent out recruitment emails to all employees who were 50–75 years of age. Participants completed a survey (Time 1) that
Study 2
Study 1 demonstrated that the CIO scale is internally reliable, has a valid factor structure, converges/diverges with other constructs as expected (except for perceived susceptibility and severity), and predicts variance in behavioral outcomes. A lingering question is whether CIO is operationally different from fatalism. After all, the CIO scale includes an item, “There are so many different recommendations about preventing cancer, it's hard to know which one to follow,” from HINTS [30] that
Sample and procedure
Adults (N = 399) were recruited from one of seven shopping malls located in the Midwest. At each location, managers allowed the research team to set-up a table and twelve chairs in one of the main intersections of the mall. A team of three to five researchers recruited mall shoppers at different malls from 9am to 9pm on Saturdays for 7 weeks. Participants were recruited using six large canvas signs (with the name of the University supporting the research). When participants approached the
References (42)
SEM with simplicity and accuracy
J Consum Psychol
(2010)- et al.
Cancer statistics, 2012
CA Cancer J Clin
(2012) What does HINTS tell us about cancer perceptions and knowledge?
(2007)- et al.
Fatalistic beliefs about cancer prevention and three prevention behaviors
Cancer Epidemiol Biomarkers Prev
(2007) - et al.
Cancer fatalism: the state of the science
Cancer Nurs
(2003) - et al.
Including limitations in news coverage of cancer research: effects of news hedging on fatalism, medical skepticism, patient trust, and backlash
J Health Commun
(2011) - et al.
Does local television news coverage cultivate fatalistic beliefs about cancer prevention?
J Commun
(2010) News headlines feed on fear of cancer risk, experts say
J Natl Cancer Inst Monogr
(2001)Living can be hazardous to your health: how the news media cover cancer risks
J Natl Cancer Inst Monogr
(1999)The hierarchical model of approach-avoidance motivation
Motiv Emotion
(2006)
Approach and avoidance temperament as basic dimensions of personality
J Pers
Using the limited capacity model of motivated mediated message processing to design effective cancer communication messages
J Commun
The health belief model
Approach and avoidance motivation and achievement goals
Educ Psychol
Fear control and danger control: a test of the extended parallel process model (EPPM)
Commun Monogr
Scale development: theory and applications
Cancer screening in US workers
Am J Public Health
Cancer screening in the United States, 2008: a review of current American Cancer Society guidelines and cancer screening issues. 2008
CA Cancer J Clin
Construct validity and invariance of four factors associated with colorectal cancer screening across gender, race, and prior screening
Cancer Epidemiol Biomarkers Prev
Factorial validity and invariance of a survey measuring psychosocial correlates of colorectal cancer screening among African Americans and Caucasians
Cancer Epidemiol Biomarkers Prev
Predicting risk behaviors: development and validation of a diagnostic scale
J Health Commun
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2022, Information Processing and ManagementCitation Excerpt :Information overabundance is common in the modern new media ecosystem (Van Aelst et al., 2017). Some survey studies adapted the perceived information overload scale (Misra & Stokols, 2012) or the cancer information overload scale (Jensen et al., 2014) to the COVID-19 pandemic, and asked informants to rate their agreement with statements regarding the intensity and trustworthiness of the sources of information (e.g., Chen et al., 2022; Hong & Kim, 2020; Sarkhel et al., 2020). During the COVID-19 pandemic, a study in South Korea found no significant association between perceived information overload and intentions to take preventative measures (Hong & Kim, 2020).