Module 1.1: Epidemiology of IPV in pregnancy and postpartum, IPV measurement, and evidence gaps (AO*)

Module 1.2: Introduction of various datasets in existence (AO*)

Module 1.3: Data analytics using R (AO+IP*)

• • Introduction to R and R Studio

  • Data wrangling

  • Data visualization

  • Missing data

  • Statistical tests and models

  • Putting reproducible research principles into practice

Module 1.4: Machine learning fundamentals and implementation using R (AO+IP*)

• • Introduction to machine learning

  • Applications of machine learning in health research

  • Machine learning in action - R code implementation

  • Fundamentals of machine learning and intuitions behind algorithms

  • Machine learning performance evaluation, model fairness, and explainability

Module 1.5: Natural language processing (NLP) and machine learning (AO+IP*)

• • Theoretical background to NLP and Internet-based data, NLP basics

  • Fundamental problems in NLP of Internet-based data

  • Data/text mining pipeline for Internet-based and social media data

  • Introduction to NLP

  • Text representation and similarities

  • Prompting large language model (ChatGPT)

  • Introduction to supervised machine learning in NLP

  • Evaluation and validation of NLP system

Module 2.1: Trauma-informed computing (SO*)

Module 2.2: Trauma-informed mHealth tools to prevent IPV (SO*)

Module 2.3: Trauma-informed digital games to prevent teen dating violence (IP*)

Module 3.1: Ethical issues posed by big data analytics and digital health technologies among vulnerable populations (SO*)

Module 3.2: Consideration of digital security and privacy—tech abuse among IPV survivors (SO*)