Randomized Trial of a Data-driven Technical Assistance System for Drug Prevention Coalitions

Overview

This project is designed to test the Coalition Check-Up (CCU)-a theory-based and data-driven technical assistance (TA) system that supports community coalitions' implementation of evidence-based programs (EBPs) for drug prevention. The primary aims of the project are to: 1) Estimate the impact of the CCU on coalition capacity. Coalitions will be randomly assigned to the CCU or a 'TA as usual' condition to evaluate whether the CCU improves coalition capacity as measured by coalition member reports of team processes, network composition, and collaborative structure. 2) Estimate the impact of the CCU on implementation of evidence-based programs. The study will test the hypothesis that coalitions receiving the CCU will implement EBPs with greater: a) quantity, b) quality, and c) sustainability. The study will also test coalition capacity as a mediator of CCU impact on EBP implementation. 3) Estimate the impact of the CCU on youth substance use. The study will test the hypothesis that communities receiving the CCU will reduce youth substance use relative to communities in the comparison condition. The study will also test EBP implementation as a mediator of CCU impact on youth substance use.

The overall goal of this five-year R01 study is to test the Coalition Check-Up (CCU) technical assistance (TA) system for supporting community coalitions' implementation of evidence-based drug prevention programs (EBPs). Over 5,000 community anti-drug coalitions operating in the U.S serve as a cornerstone of federal drug prevention. These coalitions, however, have only demonstrated efficacy in preventing substance use when they use TA and implement EBPs, a key research-to-practice gap. The CCU supports coalitions by identifying and addressing gaps in EBP implementation capacity. The proposed study advances implementation science by applying Wandersman's Interactive Systems Framework to test the effects of CCU on coalition EBP implementation capacity and youth outcomes. Despite the popularity of community anti-drug coalitions as a mechanism for EBP dissemination, scant research addresses how to support coalitions for optimal EBP implementation. Lacking adequate support, coalitions and EBPs often fail. Intensive TA provided in evidence-based coalition models is effective but often too expensive to scale in real-world settings. The CCU provides a lower-cost TA system that is broadly applicable across coalition models. The study's main objective is to test the overall effectiveness of the CCU, including how it contributes to EBP implementation and prevention of youth substance use. Building on the Interactive System Framework, the central hypothesis is that the CCU can enhance the prevention support system, thereby increasing coalition capacity for EBP implementation and the probability that EBPs will reduce youth substance use. The study will test this central hypothesis by pursuing three specific aims. The first aim is to estimate the impact of the CCU on coalition capacity, including team processes, network composition, and collaborative structure. Coalitions will be randomly assigned to the CCU or a 'TA as usual' condition. The second aim is to estimate the impact of the CCU on implementation of EBPs, including EBP reach, implementation quality, and sustainability. The third aim is to estimate the impact of the CCU on youth substance use, including alcohol, tobacco, marijuana, and opioids. The CCU is innovative in its emphasis on proactive monitoring and data-driven TA, its use of motivational interviewing to enhance coalition-driven action planning, and its examination of network structure to enhance coalition capacity. The proposed study's contribution is highly significant because the field currently lacks clear evidence of the effectiveness of a TA model applicable to the heterogeneous mix of drug prevention coalitions in operation. The research will enhance community coalition ability to bridge the research to practice gap in drug prevention programming. Results are expected to have a positive impact on the field by establishing the evidence-base for a low-cost, data-driven, manualized TA model that identifies how to intervene with community coalitions to support sustained implementation of evidence-based drug prevention programs and policies known to promote community health.