The ACHIEVE program includes virtual seminars that will cover a range of career development and methodological content for all trainees to attend over the course of their one-year training. Trainees from sub-Saharan Africa also have an opportunity to select more in-depth coursework during their 2-3 months at our consortium site. Potential topics are included below.
| D&I Science | |
| Introduction to Dissemination & Implementation Science | This course provides a basic introduction to D&I. It covers topics such as distinguishing D&I from efficacy and effectiveness research; commonly used theories and frameworks; multilevel barriers and facilitators to implementation; implementation strategies; and study designs and methods for D&I. It will also cover critical topics such as de-implementation, sustainability, and scale-up |
| Developing and Evaluating Implementation Strategies in Health and Social Services | This seminar provides an introduction to implementation strategies; demonstrates how to propose and test potential mechanisms of change; reviews systematic approaches for designing and tailoring implementation strategies to address contextual needs; explores trade-offs of different methods and designs for evaluating implementation strategies; and provides multiple examples of research to advance knowledge of implementation strategies at the patient-, provider-, organizational-, policy- and system-level. |
| Methods, Metrics, and Measures for Dissemination and Implementation Research | This seminar focuses on epidemiological and experimental methods and designs with distinctive utility for D&I research. It covers key observational, quasi-experimental, and experimental designs that can be applied to evaluate D&I strategies. Special emphasis will be placed on designs that are critical within implementation science such as hybrid effectiveness-implementation designs, cluster RCTs, and stepped wedge designs. The course will also cover innovative designs such as Sequential Multiple Assignment Randomized Trials (SMART) and the Learn as You Go (LAGO) design. |
| Emerging Topics in Dissemination and Implementation Research | The purpose of this seminar is to: 1) provide trainees with access to cutting edge thinking in the field, 2) provide opportunities for application of core concepts from the other coursework, and 3) create space for trainees to workshop ideas for grants, manuscripts, and applied implementation projects. Given these purposes, some of these sessions will feature guest lectures from research sites. These sessions will focus on emerging topics such as: de-implementation, mechanisms of change in implementation, sustainability, scale-up, adaptation of interventions and implementation strategies, economic evaluation in D&I, ethnographic methods, user-centered design, stakeholder engagement, and building institutional capacity for D&I. These sessions will be interactive, with trainees having ample opportunity to engage with experts and share the challenges and opportunities associated with their implementation projects. Other sessions will feature the ongoing or proposed work of the trainees more prominently, allowing them to receive feedback from their peers and D43 mentors. |
| Data Science | |
| Algorithms & Data Structures | Exposes participants to core computer science concepts including fundamental algorithms, data structures, computational models, proof techniques, machine organization, software design and implementation in an object-oriented programming language (currently Java). |
| Data Wrangling | Introduces participants to tools and techniques for how to collect, maintain, and process large-scale datasets. Participants review the conceptual, practical, and ethical considerations involved using human-generated data; students use various tools (Python, Linux, HTML, Git) for ingesting, cleansing, and organizing data. The course will prepare participants for efficient use of multimodal and voluminous data. |
| Machine Learning & Advanced Machine Learning Part (I) | Covers the fundamental principles of supervised learning, including generalization, overfitting, regularization, cross-validation, model selection. It reviews: core ML techniques & algorithms, including linear models, logistic regression, gradient descent, tree-based ensemble methods, kernel methods, & artificial neural networks. |
| Machine Learning & Advanced Machine Learning (II) | Advanced topics in measurement and unsupervised learning (principal component analysis, factor analysis, item response theory, singular value decomposition, latent Dirichlet allocation, clustering algorithms); deep learning |
| Advanced Algorithms | Emphasizes the design and analysis of optimal algorithms. Participants receive instruction on basic structures common to large classes of optimization problems, as well as the process of generating and comparing multiple feasible solutions. The course also practices communicating the efficiency of solutions through formal proofs. |
| Artificial Intelligence | Topics include knowledge representation, problem-solving via search, game playing, logical and probabilistic reasoning, planning, machine learning (decision trees, neural nets, reinforcement learning, and genetic algorithms). Participants engage in programming in Python to learn AI. |
| Bayesian Methods in Machine Learning | Bayesian probability allows us to model and reason about all types of uncertainty. The result is a powerful, consistent framework for approaching many problems that arise in machine learning, including parameter estimation, model comparison, and decision making. The seminar will begin with a high-level introduction to Bayesian inference, then proceed to cover more-advanced topics. These will include inference techniques, Bayesian: decision theory, model comparison, nonparametrics, and optimization. |
| Professional Development | |
| Grant Writing | Trainees will create a focused research plan incorporating well- formulated hypotheses, rationales, specific objectives and long-range research goals; organize and present a sound research plan and proposed research activities; develop and justify a budget for the proposed research activities; avoid many common grant-writing mistakes; discuss the peer review process in grant evaluation and formulate a grant proposal that is maximally compatible with that process. |
| Manuscript Writing | By the end of this workshop, trainees will be able to 1) Discuss the core components of a successful scientific manuscript 2) Identify strategies and next steps for completing a full manuscript for peer review submission; and 3) Anticipate reviewers concerns in discussing and presenting research in writing and how to best respond. |
| Peer-to-peer Publication Meetings | Trainees will present their current work (e.g., study protocols, preliminary results, draft presentations) to their fellow trainees and the mentoring team for feedback. PDs and mentoring team members will attend each meeting via zoom. Each meeting will at least have one PD in attendance. Each session will last for about 2 hours and will include didactics on fundamental research skills (e.g., searching the peer-reviewed literature, choosing appropriate experimental controls, power point presentation, delivering a scientific presentation, data management and analysis). This is mandatory for all trainees in the program. |
| Presentation Skills | Trainees will learn basic communication techniques and communicate why research allows science to become relatable, make research relevant to the public, and self-motivate. This skill is critically important for D&I research, to ensure research findings are adapted in clinical care settings. |
| Responsible Conduct of Research | All trainees will be required to attend this seminar focused on the responsible conduct of research. |