Contingency management in substance abuse treatment: A structured review of the evidence for its transportability

AimsExtant literature on contingency management (CM) transportability, or its transition from academia to community practice, is reviewed. The Consolidated Framework for Implementation Research (CFIR; Damschroder et al., 2009) guides the examination of this material.MethodsPsychInfo and Medline database searches identified 27 publications, with reviewed reference lists garnering 22 others. These 49 sources were examined according to CFIR domains of the intervention, outer setting, inner setting, clinicians, and implementation processes.ResultsIntervention characteristics were focal in 59% of the identified literature, with less frequent focus on clinicians (34%), inner setting (32%), implementation processes (18%), and outer setting (8%). As intervention characteristics, adaptability and trialability most facilitate transportability whereas non-clinical origin, perceived inefficacy or disadvantages, and costs are impediments. Clinicians with a managerial focus and greater clinic tenure and CM experience are candidates to curry organizational readiness for implementation, and combat staff disinterest or philosophical objection. A clinic's technology comfort, staff continuity, and leadership advocacy are inner setting characteristics that prompt effective implementation. Implementation processes in successful demonstration projects include careful fiscal/logistical planning, role-specific staff engagement, practical adaptation in execution, and evaluation via fidelity-monitoring and cost-effectiveness analyses. Outer setting characteristics—like economic policies and inter-agency networking or competition—are salient, often unrecognized influences.ConclusionsAs most implementation constructs are still moving targets, CM transportability is in its infancy and warrants further scientific attention. More effective dissemination may necessitate that future research weight emphasis on external validity, and utilize models of implementation science.