creator Bink, Andrea B. advisor Corrigan, Patrick W. Two models are proposed to explain the development and maintenance of comorbid depressive symptoms for people with serious mental illness. Model 1 suggests duration of illness and cognitive insight will have an independent and direct effect on self-stigma harm and self-stigma harm will have a direct effect on depressive symptoms. In Model 2, duration of illness and self-stigma harm are proposed as the indicator variables with cognitive insight as the mediator directly and independently predicting depressive symptoms. Data from the current study were drawn from a larger longitudinal investigation examining predictors and correlates of mental health treatment decisions, including medication options. Potential participants were recruited from two community health centers and were screened for inclusion. Baseline and one-month data gathered during in-person interviews for 98 participants were included in the current analysis. Results of structural equation modeling found no support for either model. Thus, no clear conclusions can be made from these results. This study was limited mainly by a small sample size. However, other factors such as directionality or exclusion of certain variables might have also limited the results. Suggestions for future research are discussed. Submitted by Erma Thomas (thomase@iit.edu) on 2017-11-01T19:04:57Z No. of bitstreams: 1 etdadmin_upload_484724.zip: 329808 bytes, checksum: 41a5910ca4fd8e594f64a4dbef95c6c5 (MD5) Made available in DSpace on 2017-11-01T19:04:57Z (GMT). No. of bitstreams: 1 etdadmin_upload_484724.zip: 329808 bytes, checksum: 41a5910ca4fd8e594f64a4dbef95c6c5 (MD5) Previous issue date: 2017-05 M.S. in Pyschology, May 2017 2017 2017-05 http://hdl.handle.net/10560/4121 en Cognitive Insight Depression Self-stigma Serious mental illness Thesis born digital application/pdf In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access PSYCH / Institute of Psychology Illinois Institute of Technology Affiliated department