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Perception of Risk of Depression: The Influence of Optimistic Bias in a Non-Clinical Population of Women

Riseley, Rebecca (2005) Perception of Risk of Depression: The Influence of Optimistic Bias in a Non-Clinical Population of Women. PhD thesis, Victoria University.

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Mental illness including depression has been estimated to account for 11% of the world's disease burden with the expectation that this figure will rise to in excess of 15% by the year 2020 (AIHM, 2002; Australian Health Ministers, 1998). Women have been reported to be twice as likely as men to experience depression, making depression a significant public health issue for women. How individuals perceive themself to be at risk has important implications for understanding help seeking behaviour and in turn, diagnosis and treatment outcomes. A number of factors have been identified in the physical health literature that account for the way perception of risk is conceptualised. The present study was designed to explore perception of risk of depression from the perspective of optimistic bias. The work of Weinstein (1980) and Moore and Rosenthal (1996) identify factors such as perceived seriousness, perceived control, stereotyping, perceived commonness, knowledge of a sufferer, perceived knowledge of the illness and attitude to the illness, as potentially influencing a person's perception of personal risk. Weinstein (1980) developed a model for integrating these factors, called optimistic bias. This model has been studied in relation to perception of risk for cancer, sexually transmitted diseases and other physical illnesses, but not in relation to mental illness. Two conceptualizations of optimistic bias were tested in this study, i) as a series of semi-independent illness specific constructs, and ii) as a global personality construct. The value of the Weinstein model for predicting perception of risk of depression was explored in relation to three physical illnesses (HIV/AIDS, Diabetes and Breast cancer). In addition women's conceptualizations of depression were explored in relation to depressive status and ability to recognize typical symptoms of depression. A non- clinical sample of one hundred and five women over the age of 18 were recruited with each participant required to complete a series of questionnaires that were quantitatively analysed. The model of optimistic bias as a series of semi-independent (state) constructs did significantly predict perception of risk for depression, accounting for 27.8% of the variance. The personality model of optimistic bias was also significant for predicting perception of risk of depression, but significantly less powerful accounting for only 8.2% of the variance. Part of the analysis for this study involved a replication of the work by Moore and Rosenthal (1996) utilising both descriptive and inferential data analyses to determine which variables predicted perception of risk of depression with two factors, perceived control and knowledge about the illness, revealed to be most significant. This study found that the illness specific model was more applicable to depression than to physical illness. Perception of risk was demonstrated by the comparative profiles to be different for each illness with the women in this study able to list a number of categories of stereotypical sufferers. A frequency analysis was also conducted to explore the similarities and differences in conceptualisation of the illnesses. Results indicated that 57% of women within the sample reported levels of clinical depression. Evidence emerged that among those who reported clinical levels of depression a subset were unable to recognise depression. A woman's depressive status and her ability to recognise depression from a scenario appeared to influence perception of risk. These results highlight important theoretical and applied implications for health promotion as well as the treatment and management of depression.

Item Type: Thesis (PhD thesis)
Uncontrolled Keywords: depression; risk; mental illness; public health issue
Subjects: RFCD Classification > 380000 Behavioural and Cognitive Sciences
Faculty/School/Research Centre/Department > School of Social Sciences and Psychology
Depositing User: Mr Angeera Sidaya
Date Deposited: 23 Oct 2006
Last Modified: 23 May 2013 16:38
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