Objective: Despite substantial progress in the development and identification of psychosocial evidence-based treatments (EBTs) in mental health, there is minimal empirical guidance for selecting an optimal set of EBTs maximally applicable and generalizable to a chosen service sample. Relevance mapping is a proposed methodology that addresses this problem through structured comparison of client characteristics in a service sample to participant characteristics from studies of EBTs.
Method: The authors demonstrate the feasibility of relevance mapping using data from 1,781 youths in a statewide mental health system and a study data set including 437 randomized clinical trials. Relevance mapping (a) reveals who is "coverable" by any EBT, under different definitions of matches between study participants and clients, and (b) identifies minimum sets of treatments needed to serve maximum numbers of clients, across different levels of analysis for defining treatment operations.
Results: In the illustration sample, all problems targeted by the study data set review were fully coverable when matching only required clients to have the same problem as EBT study participants. At the other extreme, when matching also required age, gender, ethnicity, and setting, the percentage of noncoverable youths increased to 86% in this sample. Two minimal sets of only 8 EBTs were identified that, when added to the one EBT already in place in that system, covered 100% of coverable youths when matching required problem, age, and gender.
Conclusions: This methodology offers promise for the empirically guided selection and coordination of EBTs, thereby addressing one aspect of the gap between knowledge and practice.