There are many challenges when an innovation (i.e., a program, process, or policy that is new to an organization) is actively introduced into an organization. One critical component for successful implementation is the organization’s readiness for the innovation. In this article, we propose a practical implementation science heuristic, abbreviated as R= MC2. We propose that organizational readiness involves: 1) the motivation to implement an innovation, 2) the general capacities of an organization, and 3) the innovation-specific capacities needed for a particular innovation. Each of these components can be assessed independently and be used formatively. The heuristic can be used by organizations to assess readiness to implement and by training and technical assistance providers to help build organizational readiness. We present an illustration of the heuristic by showing how behavioral health organizations differ in readiness to implement a peer specialist initiative. Implications for research and practice of organizational readiness are discussed.

Background The relation between early and frequent alcohol use and later difficulties is quite strong. However, the degree that alcohol use persists, which is often a necessary cause for developing alcohol-related problems or an alcohol use disorder, is not well studied, particularly with attention to race and gender. A novel statistical approach, the Multi-facet Longitudinal Model, enables the concurrent study of age of initiation and persistence. Methods The models were applied to longitudinal data on youth alcohol use from ages 12 through 19, collected in the (U.S.) National Longitudinal Survey of Youth 1997 cohort (N = 8,984). Results Results confirmed that Black adolescents initiate alcohol use at later ages than do White youth. Further, after initiation, White adolescents were substantially more likely than Black adolescents to continue reporting alcohol use in subsequent years. Hispanic teens showed an intermediate pattern. Gender differences were more ambiguous, with a tendency for boys to be less likely to continue drinking after initiation than were girls. Conclusions Novel findings from the new analytic models suggest differential implications of early alcohol use by race and gender. Early use of alcohol might be less consequential for males who initiate alcohol use early, Black, and Hispanic youth than for their female and White counterparts.

In most medical research, treatment effectiveness is assessed using the Average Treatment Effect (ATE) or some version of subgroup analysis. The practice of individualized or precision medicine, however, requires new approaches that predict how an individual will respond to treatment, rather than relying on aggregate measures of effect. In this study, we present a conceptual framework for estimating individual treatment effects, referred to as Predicted Individual Treatment Effects (PITE). We first apply the PITE approach to a randomized controlled trial designed to improve behavioral and physical symptoms. Despite trivial average effects of the intervention, we show substantial heterogeneity in predicted individual treatment response using the PITE approach. The PITEs can be used to predict individuals for whom the intervention may be most effective (or harmful). Next, we conduct a Monte Carlo simulation study to evaluate the accuracy of Predicted Individual Treatment Effects. We compare the performance of two methods used to obtain predictions: multiple imputation and non-parametric random decision trees (RDT). Results showed that, on average, both predictive methods produced accurate estimates at the individual level; however, the RDT tended to underestimate the PITE for people at the extreme and showed more variability in predictions across repetitions compared to the imputation approach. Limitations and future directions are discussed.

Research increasingly emphasizes understanding differential effects. This paper focuses on understanding regression mixture models, a relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their formulation, and their assumptions are compared using Monte Carlo simulations and real data analysis. The capabilities of regression mixture models are described and specific issues to be addressed when conducting regression mixtures are proposed. The paper aims to clarify the role that regression mixtures can take in the estimation of differential effects and increase awareness of the benefits and potential pitfalls of this approach. Regression mixture models are shown to be a potentially effective exploratory method for finding differential effects when these effects can be defined by a small number of classes of respondents who share a typical relationship between a predictor and an outcome. It is also shown that the comparison between regression mixture models and interactions becomes substantially more complex as the number of classes increases. It is argued that regression interactions are well suited for direct tests of specific hypotheses about differential effects and regression mixtures provide a useful approach for exploring effect heterogeneity given adequate samples and study design.

Effective implementation of evidence-based interventions is a persistent challenge across community settings.Organizational readinessor, the motivation and collective capacity of an entity to adopt and sustain an innovationis important to facilitate implementation. Drawing on the