Programs and Communities
The collection collates collections by schools, centers, programs, and research groups.
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- All Subjects: Mental Health
- All Subjects: Land use
There is an increase in the prevalence of mental health problems in the United States. Healthy People 2020’s leading mental health indicator is to increase the delivery of care to those with mental health issues and lower the number of youth who experience a major depressive disorder. Teachers and non-teaching staff are well placed in the community to identify youth undergoing emotional distress and facilitate early interventions, yet do not receive adequate training in mental health.
A project was undertaken to determine if a mental health training intervention affected the community youth mentors knowledge, attitude and self-efficacy towards helping youth with mental health issues. Three instruments with good validity and reliability namely Mental Health Literacy Scale (MHLS), Attitudes to Severe Mental Illness (ASMI) scale, and Gatekeeper Behavior Scale were used in pre intervention, immediately post intervention and two weeks post intervention questionnaires. The Wilcoxon Signed Ranks test indicated changes in the pre and post intervention scores as significant in knowledge, and attitude between pre intervention and immediately post intervention time periods. Cohen’s effect size value suggested large, medium, small, and minimum clinical significance in the variables over period of time.
Mental health literacy narrows the gap between symptom onset and intervention. Numerous mental health trainings are currently available worldwide. Schools and after school clubs in collaboration with hospital mental health and other community agencies are better equipped to bridge the gap. School staff report better confidence in addressing mental health and behavioral health issues among youth when equipped with additional resources within the school in the form of psychologists, social workers, and counselors.
Method: This project aimed to provide an evidence-based education for intake nurses to understand prevalence of PTSD and to use a screening tool Primary Care PTSD for DSM-5 (PC-PTSD-5) in a non-VA behavioral health facility.
Setting: The project site was a civilian behavioral health facility located in West Phoenix Metropolitan area. The behavioral health facility serves mental health and substance abuse needs. Project implementation focused on the intake department.
Measures: Sociodemographic data, PTSD diagnosis criteria, prevalence and PC-PTDSD-5 screening tool knowledge collected from pre and posttest evaluation. Patients’ charts for those admitted 6-week before and 6-week after the education to calculate numbers of screening tools completed by nurses at intake assessment.
Data analysis: Descriptive statistics was used to describe the sample and key measures; the Wilcoxon Signed Rank Test was used to examine differences between pre-test and post-test scores. Cohen’s effect size was used to estimate clinical significance.
Results: A total of 23 intake nurses (87.0% female, 65.2% 20-39 years old, 52.2% Caucasian, 95.6% reported having 0-10 years of experience, 56.5% completed Associate’s degree) received the education. For PTSD-related knowledge, the pre-test score (Mdn = 6.00) was significantly lower than the post-test score (Mdn = 10.00; Z= -4.23, p < .001), suggesting an increase of PTSD knowledge among nurses after the education. Regarding the diagnosis, the percentage of patients who were diagnosed with PTSD increased from (0.02% to 20% after the education).
Discussion: An evidence-based education aimed at enhancing intake nurses’ knowledge, confidence and skills implementing a brief and no-cost PTSD screening tool showed positive results, including an increase of PTSD diagnosis. The implementation of this screening tool in a civilian primary mental health care facility was feasible and helped patients connect to PTSD treatment in a timely fashion. Continued use of paper version of screening tool will be maintained at facility as an intermediary solution until final approval through parent company is received to implement into electronic medical records.
Methods: A project was undertaken at an outpatient behavioral setting in urban Arizona to determine the use and effectiveness of a mental health app called insight timer to reduce anxiety symptoms. Adult clients with anxiety symptoms were provided with the insight timer app to use over a period of eight weeks. Anxiety was evaluated with the GAD-7 scale initially and after the eight weeks of app use. Usability and the quality of the app were assessed with an app rating scale at the end of the eight weeks.
Results: Findings of the Wilcoxon Signed Ranks test indicated changes in pre and posttest assessment scores as significant (p = .028), which is a significant reduction in anxiety among seven clients who completed the 8-week intervention. the mean TI score was 15.57 (SD = 4.9), and the mean T2 score was 7.71 (SD = 5.7). Besides, Cohen's effect size value (d = 1.465) suggested large clinical significance for GAD7 in pre and posttest.
Discussion: Evidence suggests that the use of an evidence-based app can effectively reduce anxiety symptoms and improve the quality of life. The use of mental health apps like insight timer could reduce health care costs associated with unnecessary hospital admissions as well as re-hospitalizations. The routine use of apps such as the insight timer may also be beneficial to all the clients who have anxiety symptoms in outpatient as well as inpatient settings.
Phoenix is the sixth most populated city in the United States and the 12th largest metropolitan area by population, with about 4.4 million people. As the region continues to grow, the demand for housing and jobs within the metropolitan area is projected to rise under uncertain climate conditions.
Undergraduate and graduate students from Engineering, Sustainability, and Urban Planning in ASU’s Urban Infrastructure Anatomy and Sustainable Development course evaluated the water, energy, and infrastructure changes that result from smart growth in Phoenix, Arizona. The Maricopa Association of Government's Sustainable Transportation and Land Use Integration Study identified a market for 485,000 residential dwelling units in the urban core. Household water and energy use changes, changes in infrastructure needs, and financial and economic savings are assessed along with associated energy use and greenhouse gas emissions.
The course project has produced data on sustainable development in Phoenix and the findings will be made available through ASU’s Urban Sustainability Lab.
Hybrid system models - those devised from two or more disparate sub-system models - provide a number of benefits in terms of conceptualization, development, and assessment of dynamical systems. The decomposition approach helps to formulate complex interactions that are otherwise difficult or impractical to express. However, hybrid model development and usage can introduce complexity that emerges from the composition itself.
To improve assurance of model correctness, sub-systems using disparate modeling formalisms must be integrated above and beyond just the data and control level; their composition must have model specification and simulation execution aspects as well. Poly-formalism composition is one approach to composing models in this manner.
This dissertation describes a poly-formalism composition between a Discrete EVent System specification (DEVS) model and a Cellular Automata (CA) model types. These model specifications have been chosen for their broad applicability in important and emerging domains. An agent-environment domain exemplifies the composition approach. The inherent spatial relations within a CA make it well-suited for environmental representations. Similarly, the component-based nature of agents fits well within the hierarchical component structure of DEVS.
This composition employs the use of a third model, called an interaction model, that includes methods for integrating the two model types at a formalism level, at a systems architecture level, and at a model execution level. A prototype framework using DEVS for the agent model and GRASS for the environment has been developed and is described. Furthermore, this dissertation explains how the concepts of this composition approach are being applied to a real-world research project.
This dissertation expands the tool set modelers in computer science and other disciplines have in order to build hybrid system models, and provides an interaction model for an on-going research project. The concepts and models presented in this dissertation demonstrate the feasibility of composition between discrete-event agents and discrete-time cellular automata. Furthermore, it provides concepts and models that may be applied directly, or used by a modeler to devise compositions for other research efforts.