Places, Environments and Health Symposium: Data, Methods and Applications of Geospatial Information to Integrate Biology and Social Context for Health

This event will bring together speakers from across the country and distinguished researchers within North Carolina for interdisciplinary discussions to explore the nexus of data, methods and applications of geospatial information that inform biology and social context for health. In-person and virtual options are available, with lunch provided for in-person attendees.

This symposium is co-sponsored by the UNC Gillings School for Global Public Health, the UNC Center for Environmental Health and Susceptibility, and the UNC Lineberger Comprehensive Cancer Center.

Learn more at Places, Environments and Health Symposium 2023.

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Integrated Mixed Methods: Bridging Qualitative and Quantitative Methods and Results

This course will take place over three mornings (9/25/23, 9/27/23, and 9/29/23), 2.75 hours per morning, and will be offered via Zoom. Attendance is required as the course will not be recorded.

Integrated mixed methods are used to answer questions that necessitate more than one method to achieve a holistic understanding. Combining qualitative and quantitative approaches can enhance conversations about theory, practice, and/or policy. This demanding paradigm requires knowledge, skill, and expertise in quantitative and qualitative methods, as well as the art of intentionally integrating the approaches to and findings from each mode of inquiry.

This course focuses on strategies, tips, and best practices to accomplish integration in accessible and effective ways, including:
- Rationales to guide decision-making related to study design and execution
- Conceptual, theoretical, and/or logic models as roadmaps to set the stage for and guide integration
- Analytic strategies that advance frameworks and dynamic processes of connecting, building, merging, embedding, and bridging

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This online training will provide an introduction to qualitative data analysis, focusing on content/thematic analysis. The session will cover the basics of qualitative data analysis and steps in the analysis process, including: transcribing, memoing, codebook development and coding, exploring content areas or themes, and interpreting and communicating findings. Participants will have the opportunity to practice developing and applying codes.

Please note: We will not be demonstrating how to use qualitative data analysis software during this session.

Presenters:

MaryBeth Grewe, MPH
Engagement and Qualitative Research Specialist
NC TraCS Institute

Simone Frank, MPH
Community Engagement and Outreach Specialist
NC TraCS Institute

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This 2-part (9/26/23 & 9/28/23), 4-hour course will be offered via Zoom, over two mornings. Attendance is required as the course will not be recorded.

In this course, participants will learn how to keep track of the code they use in their research using the version control system Git and the collaboration platform GitHub. Git allows you to keep track of changes to your code, easily revert to previous versions, and “tag” versions of code used in publications so that the exact code used can be retrieved at a later date. GitHub allows Git users to collaborate with each other on projects by managing simultaneous changes to the same files and allowing users to review and discuss each others’ code. Git and Github are applicable to any text-based programming or analysis language, including R, Python, Stata, Julia, and others.

Participants should create a github account at github.com and install git prior to the class. Windows users can download git at https://git-scm.com/download/win; there are multiple installation options, the first link is fine. Mac users can install git by opening the terminal application (in Applications -> Utilities -> Terminal) and typing “git version” (no quotes) and pressing enter. If git is not installed, you will be prompted to install it.

Register

Introduction to Multilevel Modeling is a two-day (9/27/23 and 9/29/23) course focused on the application and interpretation of multilevel models, also known as hierarchical linear models and mixed models, for the analysis of nested data structures.

Nesting can arise from hierarchical data structures (e.g., siblings nested within family; patients nested within therapist), longitudinal data structures (repeated measures nested within individual), or both (repeated measures nested within patient and patient nested within therapist).

It is well known that the analysis of nested data structures using traditional general linear models (e.g., ANOVA or regression) is flawed, oftentimes substantially so: Tests of significance are likely biased and within- and between-group effects are confounded with one another. All of these limitations can be addressed within the multilevel model.

This workshop provides an introduction to the application of multilevel models with nested data, including software implementation in SAS, SPSS and Stata.

Register

Integrated Mixed Methods: Bridging Qualitative and Quantitative Methods and Results

This course will take place over three mornings (9/25/23, 9/27/23, and 9/29/23), 2.75 hours per morning, and will be offered via Zoom. Attendance is required as the course will not be recorded.

Integrated mixed methods are used to answer questions that necessitate more than one method to achieve a holistic understanding. Combining qualitative and quantitative approaches can enhance conversations about theory, practice, and/or policy. This demanding paradigm requires knowledge, skill, and expertise in quantitative and qualitative methods, as well as the art of intentionally integrating the approaches to and findings from each mode of inquiry.

This course focuses on strategies, tips, and best practices to accomplish integration in accessible and effective ways, including:
- Rationales to guide decision-making related to study design and execution
- Conceptual, theoretical, and/or logic models as roadmaps to set the stage for and guide integration
- Analytic strategies that advance frameworks and dynamic processes of connecting, building, merging, embedding, and bridging

Register

This one-day course will be offered via Zoom only. Course schedule is 9:00am – 3:00pm, with a 1 hour lunch and (2) 10-minute breaks. Attendance is required as it will not be recorded.

This course features Dynamic Narrative Inquiry – theory and principles for narrative research design and analysis drawing on the richness of expressive language. With this approach to qualitative inquiry, researchers can build on communication in daily life where diverse practices for sharing experience, making sense of experience, and imagining social change occur in personal narratives, sociocultural communications, and institutional policies.

This course involves a sequence of presentations and practical workshops with narrative analysis strategies applicable to small or large studies that sample verbal and/or visual discourse. We begin with an introduction to dynamic narrative inquiry, followed by modules with three analysis strategies that yield findings to a wide range of relevant social science research questions. Using examples from prior published studies, rationales and methodology are presented, including character mapping (to identify meaning in narrative actors, actions, and relationships); plot analysis (to identify how narrative structures express logics and intentions); and values analysis (to identify speaker/author/ purposes). The final hour of the day is devoted to discussing course participants’ insights from the workshops and implications for future research, practice, and policy.

Register

This 2-part (9/26/23 & 9/28/23), 4-hour course will be offered via Zoom, over two mornings. Attendance is required as the course will not be recorded.

In this course, participants will learn how to keep track of the code they use in their research using the version control system Git and the collaboration platform GitHub. Git allows you to keep track of changes to your code, easily revert to previous versions, and “tag” versions of code used in publications so that the exact code used can be retrieved at a later date. GitHub allows Git users to collaborate with each other on projects by managing simultaneous changes to the same files and allowing users to review and discuss each others’ code. Git and Github are applicable to any text-based programming or analysis language, including R, Python, Stata, Julia, and others.

Participants should create a github account at github.com and install git prior to the class. Windows users can download git at https://git-scm.com/download/win; there are multiple installation options, the first link is fine. Mac users can install git by opening the terminal application (in Applications -> Utilities -> Terminal) and typing “git version” (no quotes) and pressing enter. If git is not installed, you will be prompted to install it.

Register

AI Health Virtual Seminar: 7 Problems Facing Artificial Intelligence for Health (and what’s being done about them)

Artificial Intelligence has been promoted as the key to solving key problems in healthcare, but the challenges facing us are complex and sometimes intractable - with potential even to bring harm. In this 1-hour seminar, we'll discuss this landscape at a high level, including a history of unrealistic expectations, overhype that too often permeates this space, serious issues of bias and fairness, and other areas. We'll discuss illustrations and case studies drawn from the current scientific literature, informed by AI Health's longstanding Weekly Roundup of news and publications in AI. We'll also talk about the researchers and approaches that are informing responses to these problems, and the opportunities that exist to actively develop ethical and equitable data science. This session is free and open to anyone in the world.

Speaker:
Jonathan McCall, MS
Communications Director
Duke AI Health

Host:
Shelley Rusincovitch, MMCi
Managing Director
Duke AI Health

This event is sponsored by Duke AI Health; the Duke University Departments of Biostatistics & Bioinformatics, Computer Science, and Electrical and Computer Engineering; the Duke Center for Computational Thinking; Duke CTSI; and the Pratt School of Engineering. It is being cross-promoted by the North Carolina BERD Consortium (Duke University School of Medicine, University of North Carolina-Chapel Hill, and Wake Forest School of Medicine).

Introduction to Multilevel Modeling is a two-day (9/27/23 and 9/29/23) course focused on the application and interpretation of multilevel models, also known as hierarchical linear models and mixed models, for the analysis of nested data structures.

Nesting can arise from hierarchical data structures (e.g., siblings nested within family; patients nested within therapist), longitudinal data structures (repeated measures nested within individual), or both (repeated measures nested within patient and patient nested within therapist).

It is well known that the analysis of nested data structures using traditional general linear models (e.g., ANOVA or regression) is flawed, oftentimes substantially so: Tests of significance are likely biased and within- and between-group effects are confounded with one another. All of these limitations can be addressed within the multilevel model.

This workshop provides an introduction to the application of multilevel models with nested data, including software implementation in SAS, SPSS and Stata.

Register

Integrated Mixed Methods: Bridging Qualitative and Quantitative Methods and Results

This course will take place over three mornings (9/25/23, 9/27/23, and 9/29/23), 2.75 hours per morning, and will be offered via Zoom. Attendance is required as the course will not be recorded.

Integrated mixed methods are used to answer questions that necessitate more than one method to achieve a holistic understanding. Combining qualitative and quantitative approaches can enhance conversations about theory, practice, and/or policy. This demanding paradigm requires knowledge, skill, and expertise in quantitative and qualitative methods, as well as the art of intentionally integrating the approaches to and findings from each mode of inquiry.

This course focuses on strategies, tips, and best practices to accomplish integration in accessible and effective ways, including:
- Rationales to guide decision-making related to study design and execution
- Conceptual, theoretical, and/or logic models as roadmaps to set the stage for and guide integration
- Analytic strategies that advance frameworks and dynamic processes of connecting, building, merging, embedding, and bridging

Register

Navigating the Use of Patient-Reported Outcomes in Research and Practice: The PROTEUS Consortium

This NIH Collaboratory Rethinking Clinical Trials Grand Rounds features:

Claire Snyder, PhD
Professor
Johns Hopkins Schools of Medicine and Public Health

Norah Crossnohere, PhD
Assistant Professor
Ohio State University College of Medicine

Anne Schuster, PhD
Research Scientist
Ohio State University College of Medicine

Learn More