The course, Confronting Organizational Stress as the Effects of the COVID-19 Pandemic Unfold is co-sponsored by West Philly Promise Neighborhood (WPPN) and is being hosted as part of the UHC coronavirus response to support community organizations adapting to the pandemic. The course content will be available to registered participants for two months to complete at their own pace. Registration for this course is open until June 10 and for a reduced price of $100.

Regardless of where we work in our current stress-laden environment, the impact of chronic stress and the toll it takes on us as individuals, family members and as members of various communities, is inevitable. The COVID-19 pandemic is likely to significantly compound organizational stress that already exists. This course will focus on group dynamics and all that goes awry when organizations experience acute stress, traumatic experiences, and chronically stressful conditions. The course will allow students to make a proper assessment of what has gone wrong in the past as well as what is going wrong in the present. That analysis then can lead to figuring out how to address the underlying organizational problems that are interfering with achieving the organizational mission. The specific aim of this course is to prepare students to become more knowledgeable and capable employees and managers in whatever workplace they find themselves in.

After completing this course, participants will be able to:

• Identify the main sources of workplace stress.
• Define parallel process and trauma-organized systems.  
• Describe the ways in which chronic stress negatively impacts an organizational safety culture and guidelines for creating safety.
• Identify the various forms of a loss of emotional management, poor communication, organizational learning disabilities, and injustice within the workplace and how to effectively manage conflict.

Prerequisite knowledge: No prerequisite knowledge or skills are required for this course.

Multilevel studies and multilevel analysis have been increasingly used in the public health field. This course will discuss the rationale for multilevel studies and multilevel analysis in public health as well as differences with other study designs and other analytical approaches. Although the course will not be heavily mathematical, the basics of fitting multilevel models for different types of outcomes as well as the interpretation of estimates obtained from multilevel models will be reviewed and practiced. Emphasis will be on conceptual understanding, application and interpretation of multilevel analysis in the context of urban health research. The course will also review and critique empirical applications in urban health research and discuss conceptual and methodological challenges in using multilevel analysis.

After completing this course, participants will be able to:

• To understand the fundamentals of multilevel studies and multilevel analysis and their differences with other study designs and analytical approaches.
• To be able to fit multilevel models and interpret estimates derived from them. 
• To be familiar with applications of multilevel analysis in urban health research.
• To understand the strengths and limitations of multilevel analysis for urban health research

Prerequisite knowledge: Knowledge of regression analysis (linear, logistic, Poisson) is required for this course.

As data is collected from more and more sources and across life areas, the applications for using big data for public health purposes are growing. This class will focus on the use of large data sets, specifically claims data and other national survey sources, and provide an overview of structuring data, selecting optimal analytic approaches, and display results in compelling data visualization formats.

The class will be taught in SAS, though other statistical software is possible. Tableau will also be used (free trials are available).

After completing this course, participants will be able to:

• Identify large data set sources
• Describe methods for cleaning and structuring data for use
• Outline methods for identifying data analysis opportunities
• Review data visualization strategies to maximize reach and impact

Prerequisite knowledge: Prior experience in SAS, SPSS, STATA, R, or other statistical coding software is required for this course.

The course is co-sponsored by the Center for Public Health Practice (CPHP) at the Drexel University Dornsife School of Public Health. The CPHP is the home of the federally funded Mid-Atlantic Regional Public Health Training Center, a collaborative project with the University of Pittsburgh Graduate School of Public Health to increase the technical skills of the public health and health care workforce. The course is an introduction to the basic concepts and techniques of Geographic Information Systems (GIS). This course contains lectures and hand-on exercises in ArcGIS. The lectures introduce the basic concepts of GIS, data sources, data structure, data management, map projections and coordinate system, making maps, and spatial analysis. Students will also learn how to use the fundamental knowledge of GIS to solve real world problems. Through the hand-on exercises, students will get familiar with the interfaces and analysis tools in the ESRI software package ArcGIS. Students will also practice applying GIS as a tool and a methodological approach to spatially analyze environment and public health data.

After completing this course, participants will be able to:

• Understand basic concepts and terms of GIS, and know how to spatially analyze data using ArcGIS
• Acquire spatial data that is relevant to urban health, e.g., census data, land use data, greenspace, road networks, satellite images, etc.
• Build knowledge of data structure, data management, and coordination and projection systems
• Practice featuring symbols and making elegant maps in ArcGIS
• Apply the knowledge of GIS to solve research questions in urban health research

No prerequisite knowledge or skills are required for this course.

Improving the quality of data visualizations can help in communicating results more clearly and transparently to other scientists, stakeholders and the general public. This course will use a workshop format to help students build their own data visualization projects using R, with Urban Health examples. This course will include a general introduction to basic data visualization concepts and best practices to generate static figures. This course will also cover basic concepts regarding interactive data visualization and spatial data. This course will provide all the required code, and some of the code may be useful to apply to your own visualizations with some adjustments. Thus, participants with a stronger R background will be able to create more complex visualizations. 

Last, each student will bring their own plotting project concept to receive feedback on it and work on the code needed to get a production quality figure.

After completing this course, participants will be able to:

• Understand the basic principles of data visualization
• Perform basic data management to get data ready for visualization using R
• Use the ggplot2 R package to plot high-quality figures
• Create animated plots, interactive web-plots using R, and map spatial data using R
• Convert research/advocacy ideas into effective plots, from data to code to plot

Prerequisite knowledge: Prior basic knowledge of basic R is highly recommended for this course (see free online courses at coursera.org or codeacademy.) Advanced knowledge of R is not required for this course.

This course will provide the landscape of policy analysis in real-world settings using a continuum of data tool approaches. Since policy development and implementation occurs in diverse and fast-paced environments, approaches to studying research and policy questions that keep pace with policymakers will be described. Data approaches from individual case studies to large, publicly available data sets will be reviewed and used. Policy products (e.g., policy briefs, infographics) that resonate with policymakers and constituents will be the key deliverables generated and provide scaffolding for future policy analysis and dissemination.

After completing this course, participants will be able to:

• Identify, describe, and execute policy analysis methods
• Utilize data to form and answer policy questions
• Create engaging policy products that contribute to policy solutions

Prerequisite knowledge: Prior working knowledge of the three branches of the US government is recommended for this course.

Bayesian methods combine information from various sources and are increasingly used in biomedical and public health settings to accommodate complex data and produce readily interpretable output. This course will introduce students to Bayesian methods, emphasizing the basic methodological framework, real-world applications, and practical computing. Special consideration will be given to methods for spatial data analysis.

After completing this course, participants will be able to:

• Understand the fundamentals of Bayesian inference and the differences between Bayesian and frequentist (classical) methods
• Formulate research questions and develop Bayesian approaches to address these questions
• Be familiar with the available software for implementing Bayesian methods
• Understand advanced Bayesian methods used in the scientific literature

Prerequisite knowledge: Basic understanding of linear regression and "generalized linear models" (e.g., logistic and Poisson regression) is required for this course. Statistical programming experience is recommended but not required.