ABSTRACT Despite progress in the development of population-specific innovations and culturally-tailored interventions, disparities in access, receipt, use, and quality of health care delivery persist for populations that have historically been underserved, underrepresented, and marginalized in dissemination and implementation (D&I) research. This is partially due to a need for consistent language, methods, and data elements for characterizing determinants of health inequities and disparities in D&I research, which often vary based on population or issue studied. In addition, we lack systematic tools for identifying and mapping population- specific and culturally-relevant determinants onto existing implementation constructs (e.g., inner setting, innovation characteristics). The parent grant (U2CDA057717), the Research Adoption Support Center for the Helping to End Addiction Long-term® (HEAL) Data2Action (HD2A) Program, aims to increase the D&I capability of opioid use disorder and pain management treatment research. This supplement seeks to develop and validate a health equity taxonomy using mixed methods (i.e., stakeholder engagement, machine learning). The proposal breaks new ground in the D&I field by leveraging domain expertise (i.e., D&I and health equity/health disparities) and machine learning (ML) to develop and validate a taxonomy that will serve to prioritize the needs of those impacted by health disparities and inequities, articulate culturally-relevant and population-specific determinants known to influence implementation, and heighten researchers’ ability to assess which health equity issues are being addressed in D&I research and efforts. Specific aims include: 1) Develop and validate a taxonomy that serves to assess equity-focused D&I efforts; 2) Build, fine-tune, and evaluate topic models (i.e., unsupervised machine learning models) using off-the-shelf topic modeling algorithm tools to identify, extract, and describe the most important topics in a sample of HEAL research abstracts that address underserved populations; 3) Integrate taxonomy with topic modeling results via rapid feedback with a Board of Domain Experts and the Mentorship Team. The third aim involves collaborating with the Board of Domain Experts, including stakeholders, and the candidate’s mentorship team to 1) assess taxonomy completeness by comparing and contrasting taxonomy categories with themes uncovered via topic modeling, and 2) evaluate consistency between the taxonomy and uncovered topics in terms of co-occurrence and distribution of topics across categories. The candidate will refine the taxonomy categories based on feedback from the Board of Domain Experts and return to members for a final review. Results from this project will inform the development of a centralized public use product that can be utilized by future HD2A studies to assess key determinants of D&I related to health equity.

NARRATIVE Despite progress in the development of population-specific innovations and culturally-tailored interventions, disparities in access, receipt, and quality of health care delivery persist for populations that have historically been underserved, underrepresented, and marginalized in dissemination and implementation (D&I) research. This is partially due to a lack of harmonization and standardization among health equity language, data elements, and methods. The goal of this research is to develop and validate a taxonomy using stakeholder engagement and machine learning that will serve to prioritize the needs of those impacted by health disparities and inequities, articulate culturally-relevant and population-specific determinants known to influence implementation, and heighten researchers’ ability to assess which health equity issues are being addressed in D&I research and efforts.