Award Date

December 2015

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Environmental and Public Affairs

First Committee Member

Lee Bernick

Second Committee Member

Mary A. Overcamp-Martini

Third Committee Member

Simon Gottschalk

Fourth Committee Member

Christopher Stream

Number of Pages

147

Abstract

This dissertation studies citizen participation, mental health care and block grants. Each state has a Mental Health Planning and Advisory Council (MHPAC) that is required by the Community Mental Health Services Block Grant program. Councils must consist of at least 50 percent citizens. This dissertation looked at MHPAC activity levels of the 50 states (and Washington, DC) and developed an activity level index to measure council activity from 2008-2011. Two main questions were posed. First, do planning council differ in their level of activity? If so, what explains this variation? Second, do differences in activity levels of MHPAC’s explain variations in mental health outcomes? Additionally, four National Outcome Measures (NOMs) were analyzed to see if a relationship existed between these outcome measures and council activity levels. Three levels of analyses were conducted that included a Logit regression, an Analysis of Variance (ANOVA) and a linear regression analysis. The Logit regression showed that differences in activity levels do exist. Unfortunately, the results indicated that only one variable (state ideology) was significant; thus while states do differ in their level of activity; none of the hypotheses could conclude why activity levels differ. The ANOVA indicated that higher activity levels were associated with higher homeless rates. A poisson regression indicated that the model was significant and that higher activity levels were associated with lower numbers of evidenced based practices being implemented. The linear regression indicated that high council activity levels were significant and did play a role in mental health outcomes for three of the four NOMs studied. Overall, each NOM model was significant.

Disciplines

Public Administration | Public Affairs, Public Policy and Public Administration

Language

English


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