Multilevel Mechanisms of Implementation Strategies in Mental Health: Integrating Theory, Research, and Practice

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Abstract

A step toward the development of optimally effective, efficient, and feasible implementation strategies that increase evidence-based treatment integration in mental health services involves identification of the multilevel mechanisms through which these strategies influence implementation outcomes. This article (a) provides an orientation to, and rationale for, consideration of multilevel mediating mechanisms in implementation trials, and (b) systematically reviews randomized controlled trials that examined mediators of implementation strategies in mental health. Nine trials were located. Mediation-related methodological deficiencies were prevalent and no trials supported a hypothesized mediator. The most common reason was failure to engage the mediation target. Discussion focuses on directions to accelerate implementation strategy development in mental health.

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This article combines procedures for single-level mediational analysis with multilevel modeling techniques in order to appropriately test mediational effects in clustered data. A simulation study compared the performance of these multilevel mediational models with that of single-level mediational models in clustered data with individual- or group-level initial independent variables, individual- or group-level mediators, and individual level outcomes. The standard errors of mediated effects from the multilevel solution were generally accurate, while those from the single-level procedure were downwardly biased, often by 20% or more. The multilevel advantage was greatest in those situations involving group-level variables, larger group sizes, and higher intraclass correlations in mediator and outcome variables. Multilevel mediational modeling methods were also applied to data from a preventive intervention designed to reduce intentions to use steroids among players on high school football teams. This example illustrates differences between single-level and multilevel mediational modeling in real-world clustered data and shows how the multilevel technique may lead to more accurate results.