Megan Obert

Date of Award

January 2017

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Counseling Psychology & Community Services

First Advisor

Cindy Juntunen

Abstract

Although there has been an abundance of research in the last thirty years about primary care providers (PCPs) last contact with patients before they completed suicide, little is known about what may impede a PCPs ability to identify suicidal ideation in patients. The current study investigated implicit and explicit bias toward mental illness compared to physical illness among PCPs and students and how this may affect clinical decision making and identification of suicidal ideation in patients. The participants completed an online survey which assessed implicit stigma, explicit stigma, and clinical decision making in regard to a vignette that depicted a suicidal patient that presented with both physical and mental illness symptoms. The implicit and explicit stigma tasks were not significantly correlated, indicating that self-reported level of mental illness stigma is not a reliable picture of actual bias. A Discriminant Function Analysis revealed the implicit stigma task (IAT) was the best predictor of clinical decision-making in regard to what the participants would further assess for in the patient vignette. Participants with higher levels of implicit mental illness bias were less likely to assess for any mental illness issue in the patient vignette. A Binary Logistic Regression revealed the best predictor for making appropriate recommendations was what the participant chose to assess for in the patient vignette. The study findings are consistent with previous literature that identified implicit stigma as a better predictor of decision making than explicit stigma. Level of implicit mental illness stigma may be one of the many explanations for PCPs missing suicidal ideation in patients. PCPs and students should be encouraged to investigate their level of implicit mental illness stigma and educated on how this may impact their clinical decision making with at risk patients.

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