Cameo F Stanick, Heather M Halko, Elspeth A Nolen, Byron J Powell, Caitlin N Dorsey, Kayne D Mettert, Bryan J Weiner, Melanie Barwick, Luke Wolfenden, Laura J Damschroder, & Cara C Lewis
University of Washington affiliated authors are displayed in bold.
Published: November 2019
Christopher G. Kemp, Bradley H. Wagenaar, and Emily E. Haroz
University of Washington affiliated authors are displayed in bold.
Published: November 2019
Successful implementation reflects the interplay between intervention, implementation strategy, and context. Hybrid effectiveness-implementation studies allow investigators to assess the effects of both intervention and implementation strategy, though the role of context as a third independent variable (IV) is incompletely specified. Our objective is to expand the hybrid typology to include mixtures of all three types of IVs: intervention, implementation strategy, and context.
We propose to use I to represent the IV of intervention, IS to represent implementation strategy, and C to represent context. Primary IVs are written first and in upper case. Secondary IVs are written after a forward slash and in lower case; co-primary IVs are written after a dash and in upper case. The expanded framework specifies nine two-variable hybrid types: I/is, I-IS, IS/i, IS/c, IS-C, C/is, C/i, I-C, and I/c. We describe four in detail: I/is, IS/c, IS-C, and C/is. We also specify seven three-variable hybrid types. We argue that many studies already meet our definitions of two- or three-variable hybrids.
Our proposal builds from the typology proposed by Curran et al. (1), but offers a more complete specification of hybrid study types. We need studies that measure the implementation-related effects of variations in contextual determinants, both to advance the science and to optimize intervention delivery in the real world. Prototypical implementation studies that evaluate the effectiveness of an implementation strategy, in isolation from its context, risk perpetuating the gap between evidence and practice, as they will not generate context-specific knowledge around implementation, scale-up, and de-implementation.
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**
Lisa Saldana, Ian Bennett, Diane Powers, Mindy Vredevoogd, Tess Grover, Holle Schaper, & Mark Campbell
University of Washington affiliated authors are displayed in bold.
Published: June 2019
Alex R. Dopp, Kathryn E. Parisi, Sean A. Munson, & Aaron R. Lyon
University of Washington affiliated authors are displayed in bold.
Published: December 2018
Cameo F. Stanick, Heather M. Halko, Caitlin N. Dorsey, Bryan J. Weiner, Byron J. Powell, Lawrence A. Palinkas & Cara C. Lewis
University of Washington affiliated authors are displayed in bold.
Published: November 2018
Implementation science measures are rarely used by stakeholders to inform and enhance clinical program change. Little is known about what makes implementation measures pragmatic (i.e., practical) for use in community settings; thus, the present study’s objective was to generate a clinical stakeholder-driven operationalization of a pragmatic measures construct.
The pragmatic measures construct was defined using: 1) a systematic literature review to identify dimensions of the construct using PsycINFO and PubMed databases, and 2) interviews with an international stakeholder panel (N = 7) who were asked about their perspectives of pragmatic measures.
Combined results from the systematic literature review and stakeholder interviews revealed a final list of 47 short statements (e.g., feasible, low cost, brief) describing pragmatic measures, which will allow for the development of a rigorous, stakeholder-driven conceptualization of the pragmatic measures construct.
Results revealed significant overlap between terms related to the pragmatic construct in the existing literature and stakeholder interviews. However, a number of terms were unique to each methodology. This underscores the importance of understanding stakeholder perspectives of criteria measuring the pragmatic construct. These results will be used to inform future phases of the project where stakeholders will determine the relative importance and clarity of each dimension of the pragmatic construct, as well as their priorities for the pragmatic dimensions. Taken together, these results will be incorporated into a pragmatic rating system for existing implementation science measures to support implementation science and practice.
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**
Meredith R. Boyd, Byron J. Powell, David Endicott, Cara C. Lewis
University of Washington affiliated authors are displayed in bold.
Published: July 2018
Meredith R. Boyd, Byron J. Powell, David Endicott, & Cara C. Lewis
University of Washington affiliated authors are displayed in bold.
Published: July 2018
Cara C. Lewis, Predrag Klasnja, Byron J. Powell, Aaron R. Lyon, Leah Tuzzio, Salene Jones, Callie Walsh-Bailey and Bryan J. Weiner
University of Washington affiliated authors are displayed in bold.
Published: May 2018
The science of implementation has offered little toward understanding how different implementation strategies work. To improve outcomes of implementation efforts, the field needs precise, testable theories that describe the causal pathways through which implementation strategies function. In this perspective piece, we describe a four-step approach to developing causal pathway models for implementation strategies.
First, it is important to ensure that implementation strategies are appropriately specified. Some strategies in published compilations are well defined but may not be specified in terms of its core component that can have a reliable and measureable impact. Second, linkages between strategies and mechanisms need to be generated. Existing compilations do not offer mechanisms by which strategies act, or the processes or events through which an implementation strategy operates to affect desired implementation outcomes. Third, it is critical to identify proximal and distal outcomes the strategy is theorized to impact, with the former being direct, measurable products of the strategy and the latter being one of eight implementation outcomes (1). Finally, articulating effect modifiers, like preconditions and moderators, allow for an understanding of where, when, and why strategies have an effect on outcomes of interest.
We argue for greater precision in use of terms for factors implicated in implementation processes; development of guidelines for selecting research design and study plans that account for practical constructs and allow for the study of mechanisms; psychometrically strong and pragmatic measures of mechanisms; and more robust curation of evidence for knowledge transfer and use.
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**
Aaron R. Lyon, Elizabeth Connors, Amanda Jensen-Doss, Sara J. Landes, Cara C. Lewis, Bryce D. McLeod, Christopher Rutt, Cameo Stanick, Bryan J. Weiner
University of Washington affiliated authors are displayed in bold.
Published: September 2017
Bryan J. Weiner, Cara C. Lewis, Cameo Stanick, Byron J. Powell, Caitlin N. Dorsey, Alecia S. Clary, Marcella H. Boynton and Heather Halko
University of Washington affiliated authors are displayed in bold.
Published: August 2017
Implementation outcome measures are essential for monitoring and evaluating the success of implementation efforts. Yet, currently available measures lack conceptual clarity and have largely unknown reliability and validity. This study developed and psychometrically assessed three new measures: the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM).
Thirty-six implementation scientists and 27 mental health professionals assigned 31 items to the constructs and rated their confidence in their assignments. The Wilcoxon one-sample signed rank test was used to assess substantive and discriminant content validity. Exploratory and confirmatory factor analysis (EFA and CFA) and Cronbach alphas were used to assess the validity of the conceptual model. Three hundred twenty-six mental health counselors read one of six randomly assigned vignettes depicting a therapist contemplating adopting an evidence-based practice (EBP). Participants used 15 items to rate the therapist’s perceptions of the acceptability, appropriateness, and feasibility of adopting the EBP. CFA and Cronbach alphas were used to refine the scales, assess structural validity, and assess reliability. Analysis of variance (ANOVA) was used to assess known-groups validity. Finally, half of the counselors were randomly assigned to receive the same vignette and the other half the opposite vignette; and all were asked to re-rate acceptability, appropriateness, and feasibility. Pearson correlation coefficients were used to assess test-retest reliability and linear regression to assess sensitivity to change.
All but five items exhibited substantive and discriminant content validity. A trimmed CFA with five items per construct exhibited acceptable model fit (CFI = 0.98, RMSEA = 0.08) and high factor loadings (0.79 to 0.94). The alphas for 5-item scales were between 0.87 and 0.89. Scale refinement based on measure-specific CFAs and Cronbach alphas using vignette data produced 4-item scales (α’s from 0.85 to 0.91). A three-factor CFA exhibited acceptable fit (CFI = 0.96, RMSEA = 0.08) and high factor loadings (0.75 to 0.89), indicating structural validity. ANOVA showed significant main effects, indicating known-groups validity. Test-retest reliability coefficients ranged from 0.73 to 0.88. Regression analysis indicated each measure was sensitive to change in both directions.
The AIM, IAM, and FIM demonstrate promising psychometric properties. Predictive validity assessment is planned.
**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**