◆ Maria K. Venetis, Purdue University
◆ Clinton Brown, Purdue University
◆ Seth McCullock, Purdue University
Patients often modify the information they share with providers, limiting or avoiding particular health-related information. If patients do decide to disclose, they are mindful of how best to share specific pieces of information, particularly when anticipating negative reactions from providers. Although current disclosure theories provide predictive models for understanding individuals’ disclosure decisions, these models position disclosure within close, interdependent relationships. This relational expectation is often inconsistent with patient experiences; patient disclosure may occur with providers who are not interpersonally close and are perceived as more powerful. Thus, this research seeks to modify current disclosure theory to better understand patient disclosure in healthcare contexts. Grounded in the disclosure decision-making model (DD-MM), we propose an extended model that incorporates components of the revelation risk model (RRM) and one additional, empirically-supported variable, illness interference. Research supports that once a decision to disclose has been reached, individuals may select among several disclosure strategies. Variables that predict disclosure may vary by strategy. Thus, to determine reliable predictors of healthcare interaction disclosure, we explore how variables within the DD-MM including components of information assessment, recipient assessment, and disclosure efficacy; variables within the RRM including information valence, willingness to reveal, and communication efficacy; and illness interference predict seven disclosure strategies of preparation and rehearsal, directness, incremental disclosure, entrapment, indirect mediums, humor, and third party disclosure. Participants were recruited from Amazon’s Mechanical Turk when they volunteered to participate in a study about talking to your doctor (n = 1094). Eligible participants completed an online survey (n = 500). Extensive data cleaning resulted in 320 participants who have an established medical provider, anticipate a medical visit within the next two years, and identified health-specific information they avoid sharing with providers. Participants were generally women (59%), middle-aged (M = 36.10, SD = 11.13), attended college (60%), and Caucasian (80%). Many participants (55%) regarded their health-specific information as private or secret. We performed a series of linear regressions. For each regression, a disclosure strategy was the dependent variable, and predictors included stigma, relevance, visibility, illness interference, information valence, closeness, emotional reaction, reciprocity, topic avoidance, willingness to reveal because provider needs to know, and willingness to reveal because the provider asked. All variables except the willingness to reveal because provider needs to know significantly predicted at least two strategies. Significant variables comprise the newly developed medical disclosure model. Five variables were significant across five strategies. Specifically relevance, operationalized as contagion, information valence, anticipated provider negative reaction such as being critical or judgemental, anticipated provider reaction of changing the topic, and if the provider asked the patient about the information were consistent predictors of medical disclosure. Results present quantitative evidence that patients are mindful of how they share their private, health-related information, and anticipate selecting strategies as dependent on the information. For example, participants reported sharing stigmatized information via third party, incremental, or indirect strategies. Alternatively, patients report disclosure when providers ask about the information. This highlights the need for providers to directly ask questions rather than assume patients will share relevant information.