Avoiding Health Information

Abstract

This study investigated why and how individuals avoid health information to support the development of models of uncertainty and information management and offer insights for those dealing with the information and uncertainty inherent to health and illness. Participants from student (n = 507) and community (n = 418) samples reported that they avoided health information to (a) maintain hope or deniability, (b) resist overexposure, (c) accept limits of action, (d) manage flawed information, (e) maintain boundaries, and (f) continue with life/activities. They also reported strategies for avoiding information, including removing or ignoring stimuli (e.g., avoiding people who might provide health advice) and controlling conversations (e.g., withholding information, changing the subject). Results suggest a link between previous experience with serious illness and health information avoidance. Building on uncertainty management theory, this study demonstrated that health information avoidance is situational, relatively common, not necessarily unhealthy, and may be used to accomplish multiple communication goals.

Acknowledgments

We dedicate this article to Dale E. Brashers, who passed away in July 2010. We owe him a debt we cannot express or repay. He was our teacher, our colleague, and our friend. This article, like most of our scholarly pursuits, would not have been possible without him, and he still inspires us in ways that transcend academic life. Thank you, Dale.

Notes

¹In Sample 1, Question 2, because we asked whether participants had ever imagined “a time when [they] would want to avoid getting information about a health issue,” we had the independent raters code this subset of responses as real versus hypothetical or imagined. For Sample 1, Question 2, coders determined that 114 of 189 (60%) were real, not hypothetical or imagined examples (percent agreement [PA] = .95; κ = .89). As a check of the other response subsets, the coders also coded the other data, but calculating reliability for this variable proved problematic, because the majority of responses were coded as real. The imagined and unclear categories were used infrequently if at all by the coders. This pattern is not surprising given that the prompts did not ask for imagined or hypothetical responses. In two subsets (Sample 1, Question 1; Sample 2, Question 2), the categories were not used at all by one of the coders, making the calculation of Cohen's κ impossible, so in those cases we reported only PA (Sample 1, Question 1: PA = .88; Sample 2, Question 2: PA = .93). In such cases, Fleis, Levin, and Paik (2003) suggested that the proportion of specific agreement (p _s) may be used as an alternative when a category is likely to be rare thus inflating agreement (p. 600). For Sample 1, Question 1, p _s = 0.93; for Sample 2, Question 2, p _s = 0.97. In summary, for Sample 1, Question 1, coders identified 263 of 289 (91%, PA = .88, p _s = 0.93) as real; for Sample 1, Question 2, coders determined that 114 of 189 (60%, PA = .95; κ = .89) were real. For Sample 2, Question 1 coders determined that 191 of 198 (97%, PA = .97, κ = .59) were real; for Sample 2, Question 2, coders determined that 127 of 130 (98%, PA = .93, p _s = 0.97) were real. We reported the values for the reader's reference, and the only inference we drew from these results is that the experiences reported by participants were by and far drawn from their actual not imagined life experiences as is evident in the examples offered throughout the Results section.

²Krippendorff (2004) raised doubts about the mathematical properties of Cohen's κ, so we also calculated the indicators recommended by Krippendorff (i.e., Scott's π and Krippendorff's α). In all cases in these data, the differences between the indicators were negligible. Most were the same to the third decimal place, and no differences were greater than .01. Results for all reliability indicators are available from the first author.

³We used pseudonyms throughout this article to protect the privacy of our participants.

Note. Frequencies for each category appear in parentheses.