Title page for ETD etd-08012012-040258

Type of Document Master's Thesis
Author Katz, Robert A.
URN etd-08012012-040258
Title Evaluating a mental health needs assessment technique on a sample of the elderly population of the New River Valley
Degree Master of Science
Department Psychology
Advisory Committee
Advisor Name Title
Winett, Richard A. Committee Chair
Harrison, David W. Committee Member
Neff, Debra F. Committee Member
  • Quality of life
Date of Defense 1988-05-15
Availability restricted
The purpose of this study was to evaluate the effectiveness of the Quality of Life~Contribution Model (QOLC) developed by Murrell and Norris (19S3) as a mental health needs assessment technique for the rural elderly. In this field survey method, measures of mental health areas and program targets within each mental health area are compared and prioritized according to their relative contributions to a subjective index of quality-of-life (QOL).

An in—home survey of 60 older adults was conducted. Needs were defined in terms of problems, services, and community support and were measured across the following mental health areas: 1) Depression; 2) Organic Brain Syndrome; 3) Alcohol and Drug Abuse; 4) Anxiety; 5) Caregiver Problems; 6) Schizophrenia; and 7) Health Habits. The utility of the QOLC model was evaluated via the descriptive conclusions generated by multiple regression analysis of the sample survey data, with QOL as the dependent variable and the different need measures and mental health areas as the independent variables. A cost analysis was also completed comparing the net total cost of the QOLC with the hypothesized net total cost of a more traditional mental health needs assessment (consisting of a key informant plus a service use statistics component). The results suggest that although the QOLC mental health needs assessment costs more than simpler needs assessment techniques, it can yield important information that can prevent wasteful spending on increased direct mental health services and can also be used to determine the criteria that should be used to segment the target population.

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