Planning a Stigmatized Nonvisible Illness Disclosure: Applying the Disclosure Decision-Making Model

ABSTRACT

This study applied the disclosure decision-making model (DD-MM) to explore how individuals plan to disclose nonvisible illness (Study 1), compared to planning to disclose personal information (Study 2). Study 1 showed that perceived stigma from the illness negatively predicted disclosure efficacy; closeness predicted anticipated response (i.e., provision of support) although it did not influence disclosure efficacy; disclosure efficacy led to reduced planning, with planning leading to scheduling. Study 2 demonstrated that when information was considered to be intimate, it negatively influenced disclosure efficacy. Unlike the model with stigma (Study 1), closeness positively predicted both anticipated response and disclosure efficacy. The rest of the hypothesized relationships showed a similar pattern to Study 1: disclosure efficacy reduced planning, which then positively influenced scheduling. Implications of understanding stages of planning for stigmatized information are discussed.

KEYWORDS:

• Efficacy
• nonvisible illness
• self-disclosure planning
• stigma

Funding

Research reported in this paper was supported in-part by the Cancer Center Support Grant (CCSG-Core Grant; P30 CA008748; PI: Craig B. Thompson, MD). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Notes

¹ Participant eligibility requirements included having a non-visible illness. That is, an illness that most others would not be able to identify without being told. Examples of and most frequently reported conditions were mental health conditions (e.g., ADD/ADHD, anorexia/bulimia, and alcoholism) and acquired/behavioral illness (e.g., diabetes, high cholesterol, and sexually transmitted infections); specific conditions are reported in Table 1. Examples of excluded conditions were allergies, color blindness, hypertension, and migraines. Participants assessed their health condition as generally stable (M = 2.71, SD = 1.22, with a higher score indicating greater instability).

² When participants arrived at the study site (one central location at specific times), they were screened privately by one researcher with medical training. This researcher used three questions related to health conditions and treatments to screen based on inclusion criteria described. If participants qualified for the study, then they proceeded with the consent and filled out the survey.

³ One coder with background in the medical field generated the coding scheme for health conditions; after this, the research team discussed codes and arrived at consensus on any disagreements (less than 5%).

⁴ This study used four goodness-of-fit indices to gauge the model fit. The x²/df adjusts the x² statistics for sample size. Model fit was assessed with the comparative fit index (CFI) and the nonnormed fit index (NNFI). The RMSEA accounts for errors of approximation in the population (Bollen, 1989 Hooper, Coughlan, & Mullen, 2008 Kline, 2010). The error variance for each latent variable in the model was fixed to (1−α) (σ²) to account for unreliability within the measures.

⁵ In Study 1, the deleted item was, “I told this person on the spur of the moment” (R). In Study 2, the deleted item was, “I spent a lot of time planning to tell this person.”

⁶ Examples of personal information that the participants shared with someone most frequently include family relationships (e.g., family relationships and traditions), self-concept (e.g., mental health and achievements in school), and intimacy/attraction (e.g., sexual relations and infidelity). Topics of information shared in Study 2 are presented in Table 3.

⁷ In the current study, both models were further tested with planning and scheduling items treated as separate factors on a latent planning variable. When the models were tested in this manner, the model fits were generally acceptable compared to the fit of each comparable model in the current study. That is, planning and scheduling may also be considered subparts of a single component in future disclosure decision making for personal and stigmatized information.

Additional information

Notes on contributors

Soe Yoon Choi

Soe Yoon Choi is a doctoral student in the Department of Communication at Rutgers University. Her current research interests are self-disclosure in both offline and online contexts, self-disclosures shaped by technological affordances of social media, and the role of psychological distance in forming a sense of connections on social media.

Maria K. Venetis

Maria K. Venetis is an Assistant Professor in the Brian Lamb School of Communication at Purdue University. Dr. Venetis's research interests include physician-patient interactions, the influence of communication during medical visits on patient psychological outcomes, and topic avoidance about sensitive health problems between couples.

Kathryn Greene

Kathryn Greene is a Professor in the Department of Communication at Rutgers University. Dr. Greene's research interests include decision-making related to various health risks, modeling the process of self-disclosures of nonvisible illness, and effective message features for the awareness and prevention of risk-taking behaviors.

Kate Magsamen-Conrad

Kate Magsamen-Conrad is an Assistant Professor in the Department of Communication at Bowling Green State University. Dr. Magsamen-Conrad's research interests include interpersonal and health/risk communication, the influence of interpersonal communication on personal and health outcomes, and organizational communication.

Maria G. Checton

Maria G. Checton is an Associate Professor in Health Administration at the College of Saint Elizabeth. Dr. Checton's research focuses on how communication in health contexts influences individuals' and couples' disclosure decision-making and health condition management.

Smita C. Banerjee

Smita C. Banerjee is an Assistant Attending Behavioral Scientist at Memorial Sloan Kettering Cancer Center. Dr. Banerjee's research interests are in using communication theory and methods to promote cancer prevention and control outcomes.