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One definition of risk communication is “communication with individuals … that addresses knowledge, perceptions, attitudes, and behavior related to risk” (Edwards & Bastian 2001, 147). We often hear about the dangers of poor lifestyle habits (e.g., smoking, drinking, not exercising, failure to vaccinate or screen for cancer) or how to engage in preventive health behaviors (e.g., taking aspirin to prevent heart disease). The basic idea behind these messages is that increasing a person’s perceived risk will motivate behavior change to prevent or diminish the threat. A comprehensive understanding of risk entails that patients/public know the antecedents (e.g., risk factors), likelihoods (probabilities), and consequences and preventive actions (pros and cons) necessary to control/avert harm. Not all communications will review each of these components. Rather, risk communication is focused usually on conveying probabilistic information, often numerically.
Numbers (e.g., percentages, frequencies) are used to convey risk magnitudes because of their appealing qualities. For example, they: (1) are precise; (2) convey an aura of ‘scientific credibility’; (3) can be converted from one metric to another (e.g., 10 percent = 1 out of 10); (4) can be verified for accuracy; (5) can be computed using algorithms (e.g., Gail Score for breast cancer; Windschitl & Wells 1996); and (6) are appreciated due to people’s mathematical training in school and/or occupation. A marker of understanding risk is whether the public or patients reflect back a numerical probability estimate. Yet, after receipt of a numerical risk, many provide a risk estimate that deviates from that received (e.g., Braithwaite et al. 2004).
The following are the most frequent reasons for such deviations: Format of information: individuals may receive their estimate in a poorly understood format. Wide variability in use of formats and a tendency to convey too much information may cause confusion. Of import, no single format is best in all situations. Low message engagement: individuals may not fully engage with the information for various reasons (e.g., relying on experts; poor math skills, topic not viewed as relevant). Biased processing: individuals may not believe or distort the risk magnitude to reduce threat or interpret it favorably (e.g., optimistic bias; Klein & Weinstein 1997). Incorporated behavior change: individuals have taken into account future actions in their estimate, thus diminishing or increasing the perceived risk. Mood states: positive and negative moods can affect processing of (risk) information (Slovic et al. 2005). Use of heuristics: individuals use mental shortcuts (i.e., heuristics) to make risk judgments. They may feel that they resemble the person at higher/lower risk (i.e., representative heuristic), recall instances that make the event less/more likely (i.e., availability heuristic), or be influenced by numbers they encountered (i.e., anchoring). Assessments of risks: assessment (e.g., numerical vs. verbal scales), may not be valid and reliable (Windschitl & Wells 1996).
How do we judge risk communication as effective? There exist very few guidelines on this issue (Weinstein & Sandman 1993). Below are ways efficacy of risk communication might be judged. Engagement in recommended behavior(s): a risk communication leads to the recommended behavior. Paying attention to the message: risk messages are attended to (e.g., information recalled, use, and dissemination to others). Acquisition of factual knowledge: communication results in greater understanding of the phenomenon (e.g., knowing the risk factors, what to do, knowing how to balance the pros and cons of action. Judging the direction of risk magnitude: an individual judges the risk as objectively higher or lower as intended by the source. Conflict/trust: presentation of risk results in greater trust/less conflict. Evocation of extreme negative/positive affect: communication does not result in undue anxiety, stress, anger, or unexpectedly high levels of positive affect in light of high probabilistic negative outcome(s).
In addition to specifying the goal(s) of the communication, accessibility and channel of dissemination, understanding the target audience (e.g., needs, values, prior experiences with health issue), context (e.g., socio-political environment), and resources (e.g., staffing, costs), attention should be given to the following questions: (1) What dimensions of a comprehensive understanding of risk will be covered? (2) Does one want to communicate information numerically, verbally, and/or graphically? (3) Will the audience bias/distort the processing of the messages (e.g., give selective attention to information, react optimistically or pessimistically, superficially, with counterarguments)? If so, one has to examine ways to curb the potential effects of biases (e.g., increase self-efficacy). (4) How will one judge whether the communication was effective (e.g, actions taken, better informed decisions)? In sum, effective risk communication involves the consideration of a multitude of factors, some of which have been highlighted here.
- Braithwaite, D., Emery, J., Walter, F., Prevost, A. T., & Sutton, S. (2004). Psychological impact of genetic counseling for familial cancer: A systematic review and meta-analysis. Journal of the National Cancer Institute, 96, 122–133.
- Edwards, A. & Bastian, H. (2001). Risk communication: Making evidence part of patient choices. In A. Edwards & G. Elwyn (eds.), Evidence-based patient choice: Inevitable or impossible? Oxford: Oxford University Press, pp. 144–160.
- Klein, W. M. & Weinstein, N. D. (1997). Social comparison and unrealistic optimism about personal risk. In B. P. Buunk & F. Gibbons (eds.), Health, coping, and well-being: Perspectives from social comparison theory. Mahwah, NJ: Lawrence Erlbaum, pp. 25–61.
- Slovic, P., Peters, E., Finucane, M. L., & MacGregor, D. G. (2005). Affect, risk, and decision making. Health Psychology, 24(4, suppl.), S35–S40.
- Windschitl, P. D. & Wells, G. I. (1996). Measuring psychological uncertainty: Verbal versus numerical methods. Journal of Experimental Psychology, Applied, 2, 343–364.
- Weinstein, N. D. & Sandman, P. M. (1993). Some criteria for evaluating risk messages. Risk Analysis, 13, 103–114.
- Witte, K., Meyer, G., & Martell, D. P. (2001). Effective health risk messages: Step-by-step guide. Thousand Oaks, CA: Sage.