Assessing Health Diagnosis Disclosure Decisions in Relationships: Testing the Disclosure Decision-Making Model

Abstract

Illness affects millions of Americans each year, and the disclosure of health conditions can facilitate access to social support, in addition to other physical and physiological benefits. This article tests the Disclosure Decision-Making Model (DD-MM; Greene, 2009) to predict factors that influence the likelihood of disclosing (and past disclosure of) nonvisible physical or mental health-related information. One hundred eighty-seven (n = 187) people were recruited for a study to report on both disclosing and not disclosing a nonvisible health condition. Measured variables included information assessment, relational quality, anticipated reactions (support, relational consequences), confidence in response, disclosure efficacy, and disclosure (likelihood of disclosure and depth of disclosure). Structural equation modeling results supported many of the proposed hypotheses, with a great deal of similarity across models. Specifically, assessing information predicted efficacy, and to some extent relational outcomes. Closeness was related to response overall and to efficacy in one model. Response predicted outcome overall and likelihood of disclosure in one model. Finally, efficacy predicted likelihood of disclosure and depth of disclosure. The article discusses the implications of the findings for understanding information, relationship assessments, and efficacy in disclosing health diagnoses.

Notes

¹Examples of conditions listed on the announcement as qualifying included STIs, eating disorders, cancers (except skin cancer), and lupus. Examples of conditions that were listed on the announcement as not qualifying included allergies, high blood pressure/hypertension, migraines, broken bones, and ulcers. To qualify, a person must have a current, diagnosed, qualifying condition and be under treatment (and/or in recovery in the case of addiction). Participants were screened privately by a researcher to ensure that they met all criteria.

²A series of CFAs were performed to investigate the dimensionality, reliability, and validity of measures. Two single CFAs (disclosed/undisclosed) were conducted with items measuring all variables. Examination of these CFAs revealed that items did not cross load (i.e., across variables), increasing confidence in measurement. Specifically, for the undisclosed model with with all latent variables correlated (with the exception of second order latent variables that cannot be correlated) the model fit based on two of the three criteria, χ²(596) = 1010.2, CFI = .87, RMSEA = .06. The largest modification indices primarily suggested correlations between individual items within scales for example, two items on the anticipated response scale. These are not unexpected and do not undermine the overall measurement structure proposed. Additional individual and global CFA information is available from the authors.