Children with conduct problems and elevated callous-unemotional (CU) traits whose parents participate in parent management training (PMT) start and end treatment with higher levels of conduct problems than those with conduct problems alone and face a higher risk of negative outcomes. Because these children have deficits in emotion recognition and empathy, incorporating an emotion-focused intervention into PMT may enhance child outcomes. This study describes the development and evaluation of a brief parent-focused emotion-coaching (EC) intervention combined with an evidence-based PMT program, Helping the Noncompliant Child (HNC; McMahon & Forehand, 2003), for use with clinic-referred children (3-7 years) with conduct problems and elevated CU traits (N = 43; M age = 6.08 years, SD = 1.39, 83.7% male). We employed a treatment deployment (i.e., effectiveness) model to inform the EC content and integrate it with HNC. HNC-EC is one of a growing number of interventions targeting known developmental mechanisms related to child CU traits. We examined parent and child outcomes from a pilot randomized controlled trial comparing HNC-EC and HNC. Parent-report, direct observation, and laboratory measures were employed at baseline, mid-treatment, and post-treatment. Intention-to-treat and completer analyses demonstrated that both HNC and HNC-EC led to significant improvements in children's conduct problems and CU traits. There were relatively few differences between the two groups; however, those differences favored HNC-EC in the domains of conduct problems, emotion recognition, parenting, and parent adjustment. Findings support the effectiveness of both HNC and HNC-EC in addressing the needs of this high-risk population, and suggest the added benefit of incorporating EC into PMT.

Abstract Rarely do training programs teach students how to manage a team when conducting research. Most people learn through observation and experience as members of another investigator’s team. However, not everyone runs teams efficiently and it is important to learn what everyone’s role on a team should be and how to successfully manage the team. In this chapter, the reader is introduced to the concept of Team Science. The chapter begins with a description of the study team and expertise needed for psychosocial interventions research, the team hierarchy, methods for running meetings, and how to collaborate with other investigators, community clinics, and industry. The chapter also shares information about team management tools and models, with a sample meeting agenda, material, and team management tools. The chapter also describes the process of how to run a study with two or more investigators.

Abstract This second addition of High-Quality Psychosocial Interventions Research, From Conception to Piloting to Population Based Trials is a comprehensive guide for junior and investigators new to psychosocial interventions who wish to conduct clinical trials on psychosocial treatments. This book offers practical guidance on the ethical and efficient conduct of these studies and covers a range of topics from including how to assemble and manage research teams, ethical considerations, and how to run clinical trials of psychosocial interventions, from proof of concept to pragmatic effectiveness. This updated edition builds on the first by discussing alternative trial designs and how to conduct safe and effective remote or digital clinical research. It is suitable for investigators at all career stages. Students and junior faculty will find it a valuable step-by-step resource for planning and executing their research, while more experienced researchers—particularly those new to psychosocial interventions or effectiveness research—will find selected chapters especially useful. Each chapter covers ethical management of psychosocial intervention research, considerations for diversity and health disparities, example data reporting and sharing tools, sample forms for partnering with industry, and sample budgets for the various types of clinical trials.

Abstract The advent of digital technology for health purposes has grown substantially since 2015. The COVID-19 epidemic shepherded in the need to create research methods that can be conducted remotely and shaped what we now call a remote clinical trial. This chapter covers, with examples from the authors’ first-hand experiences, the promise and pitfalls of a remote clinical trial. The chapter begins with an introduction to a remote clinical trial and provides examples of how to develop a digital intervention and how to partner with industry to test existing tools. The chapter discusses how to prepare for and screen out fraudulent actors (people who join a study simply for an incentive), how to manage data collection, and the ethical issues to be aware of when recruiting and studying a remote sample.

Abstract This chapter explains the purpose of proof-of-concept study and describes the methods used to determine whether an intervention has impacted a hypothesized target. Proof-of-concept trials are the initial step in the clinical trial pipeline and are needed before more investment is made in larger pilot and efficacy trials. This chapter explains key terms, such as the difference between a mediator, target, and mechanism of treatment, how to set a reasonable go/no-go rule to determine if your intervention has enough impact on the target to justify further study, and what sample is needed. The chapter also covers research design issues, for instance, whether a control or other comparison condition is needed to test proof-of-concept. The chapter also discusses how to ethically report on the findings from a proof-of-concept study.

Background:Although web-based mental health resources have the potential to assist millions, particularly those who face barriers to treatment, most mental health website visitors disengage before accessing resources that can help improve their mental health. Objective:We used a sequential multiple assignment randomized trial to test whether personalized tailoring improved engagement on a self-guided mental health website. Methods:Data were collected via voluntary response sampling on the Mental Health America website. Inclusion criteria included residing in the United States and viewing a postscreening survey after completing the Patient Health Questionnaire-9 (PHQ-9). Participants were randomized to 1 of 2 postscreening survey conditions: the demographics survey or the Next Steps survey, which included additional tailoring questions assessing perceived need and participants' intended next steps. Participants who viewed the following screening results page were subsequently randomized to 1 of 5 conditions that displayed nontailored or tailored messages and featured resources, as well as persistent general resources that did not vary by condition. Data were analyzed using logistic regressions predicting disengagement and clicks on featured resources (versus persistent general resources) by condition. Results:Adding questions to inform tailoring significantly increased the odds of disengaging by 14% (demographics survey: 25%; Next Steps survey: 27.5%; odds ratio [OR] 1.14, 95% CI 1.11-1.16; P<.001). Among participants who viewed a postscreening survey (n=169,647), 87,712 participants were randomized to the demographics survey condition, and 81,935 participants were randomized to the Next Steps survey condition. Among participants who submitted the demographics survey (n=38,490), tailoring resources to demographics reduced the odds of disengaging by 10% (OR 0.90, 95% CI 0.87-0.94; P<.001) and, among those who engaged, increased the odds of clicking a featured resource versus a persistent general resource by 90% (OR 1.90, 95% CI 1.79-2.01; P<.001). Among participants who submitted the Next Steps survey (n=34,204), tailoring messages to perceived need (P=.33), tailoring resources to intended next steps (P=.51), and a combination of both (P=.52) did not significantly reduce the odds of disengaging compared with the nontailored condition. However, tailoring resources to intended next steps and combining a tailored message to perceived need with tailored resources to intended next steps increased the odds of clicking a featured resource by 25% (OR 1.25, 95% CI 1.14-1.37; P<.001) and 34% (OR 1.34, 95% CI 1.23-1.47; P<.001), respectively. Tailoring resources to demographics was significantly more effective in improving engagement than tailoring to perceived need or intended next steps (P≤.004). Conclusions:There was a small but statistically significant cost to engagement from adding tailoring questions assessing perceived need and intended next steps. Among the strategies tested in this study, tailoring resources to demographics was the most effective strategy for increasing engagement among visitors who viewed their screening results. This study demonstrates how personalization may increase engagement with mental health websites and provides design implications for future research.

Background Clinicians need supports beyond training to deliver evidence-based treatments with fidelity. Workplace-based clinical supervision often is a commonly provided support in community mental health, yet too few studies have empirically examined supervision and its impact on clinician fidelity and treatment delivery. Method Building on a Washington State-funded evidence-based treatment initiative (CBT+), we conducted a randomized controlled trial (RCT), testing two supervision conditions delivered by workplace-based supervisors (supervisors employed by community mental health organizations). The RCT followed a supervision-as-usual (SAU) phase for comparison. The treatment of focus was trauma-focused cognitive behavioral therapy (TF-CBT). Clinicians ( N = 238) from 25 organizations participated in the study across the SAU baseline and RCT phases. In the RCT phase, clinicians were randomized to either symptom and fidelity monitoring (SFM) or SFM and behavioral rehearsal (SFM + BR). For BR, clinicians engaged in a short role play of an upcoming treatment element. Supervisors delivered both conditions, with regular study monitoring for drift. Clinicians audiorecorded therapy sessions with enrolled clients, and masked coders coded a subset of recordings for adherence to TF-CBT. One hundred and thirty-three clinicians had recorded TF-CBT session data for 258 youth. We examined six adherence outcomes, including potential moderators. Results Results of generalized estimating equations indicated that there were no real differences on adherence outcomes for experimental conditions (SFM, SFM + BR) compared to SAU. Adherence scores in the baseline SAU phase and the RCT conditions were high. Only one interaction was significant. Conclusions Contrary to our hypotheses, we did not see improvements in adherence with the RCT conditions. However, nonsignificant findings seem best explained by clinicians’ acceptable/high adherence in SAU. This study was conducted within the context of a long-standing, state-funded EBT initiative, in which clinicians and their supervisors receive training and support, and in which participating community mental health organizations have adopted and supported TF-CBT. ClinicalTrials.gov ID NCT01800266

Social care integration in health systems is on the rise in the United States, particularly since the National Committee for Quality Assurance introduced screening and intervention as HEDIS metrics. These policy levers outpace empirical knowledge to guide how best to operationalize social care. This study reports results from a quality improvement initiative to implement social care in an integrated health system. A quantitative effectiveness evaluation was conducted across 32 clinics in Kaiser Permanente Washington, which had recently embedded Community Resource Specialists (CRS) in their primary care teams and integrated a social health screener into their electronic health record. Using a pragmatic design with propensity score matched comparison group (PSC), we compared two intervention arms (both of whom completed a social health screener): (1) CRS-S who engaged in only a single CRS visit and (2) CRS-M who engaged in at least two CRS visits. Patients completed a survey shortly after their qualifying primary care encounter and approximately three months later that assessed the following domains: social health, patient experience with the care team, and health and functioning; healthcare utilization was obtained from the electronic health record. Patients from each arm were then purposefully sampled for qualitative interviews. Quantitative results suggest that CRS-M patients experienced exacerbated social risk severity and food insecurity over three months, but improved financial risk. For the majority of domains, no differences were observed between arms, though CRS-M demonstrated poorer coping over time whereas PSC patients showed higher use of instrumental and emotional support coping strategies. CRS-M reported worse health and need for more help with activities of daily living, but improvements in trust in their care team. Qualitative results showcased, by design, the positive potential impact of working with a CRS across all domains of interest, especially social and mental health. This quality improvement evaluation of social care integration using the CRS illustrates a potential pathway for activating social support and healthcare relationships in primary care, but more rigorous designs and longer-term follow up are needed to explore if this pathway leads to improvements in patient or population health and healthcare utilization.

BackgroundAlthough substantial progress has been made in establishing evidence-based psychosocial clinical interventions and implementation strategies for mental health, translating research into practice—particularly in more accessible, community settings—has been slow. ObjectiveThis protocol outlines the renewal of the National Institute of Mental Health–funded University of Washington Advanced Laboratories for Accelerating the Reach and Impact of Treatments for Youth and Adults with Mental Illness Center, which draws from human-centered design (HCD) and implementation science to improve clinical interventions and implementation strategies. The Center’s second round of funding (2023-2028) focuses on using the Discover, Design and Build, and Test (DDBT) framework to address 3 priority clinical intervention and implementation strategy mechanisms (ie, usability, engagement, and appropriateness), which we identified as challenges to implementation and scalability during the first iteration of the center. Local redesign teams work collaboratively and share decision-making to carry out DDBT. MethodsAll 4 core studies received institutional review board approval by June 2024, and each pilot project will pursue institutional review board approval when awarded. We will provide research infrastructure to 1 large effectiveness study and 3 exploratory pilot studies as part of the center grant. At least 4 additional small pilot studies will be solicited and funded by the center. All studies will explore the use of DDBT for clinical interventions and implementation strategies to identify modification targets to improve usability, engagement, and appropriateness in accessible nonspecialty settings (Discover phase); develop redesign solutions with local teams to address modification targets (Design and Build phase); and determine if redesign improves usability, engagement, and appropriateness (Test phase), as well as implementation outcomes. Center staff will collaborate with local redesign teams to develop and test clinical interventions and implementation strategies for community settings. We will collaborate with teams to use methods and centerwide measures that facilitate cross-project analysis of the effects of DDBT-driven redesign on outcomes of interest. ResultsAs of January 2025, three of the 4 core studies are underway. We will generate additional evidence on the robustness of DDBT and whether combining HCD and implementation science is an asset for improving clinical interventions and implementation strategies. ConclusionsDuring the first round of the center, we established that DDBT is a useful approach to systematically identify and address chronic challenges of implementing clinical interventions and implementation strategies. In this subsequent grant, we expect to increase evidence of DDBT’s impact on clinical interventions and implementation strategies by expanding a list of common challenges that could benefit from modification, a list of exemplary solutions to address these challenges, and guidance on using the DDBT framework. These resources will contribute to broader discourse on how to enhance implementation of clinical interventions and implementation strategies that integrate HCD and implementation science. International Registered Report Identifier (IRRID)PRR1-10.2196/65446

Importance:There is a need to determine the relative effect of message-based psychotherapy (MBP), an asynchronous approach that uses emails, texts, or voice or video messages to permit therapeutic exchanges, compared with video-based psychotherapy (VBP) and whether a combination of modalities would result in better outcomes for those who do not respond to either treatment alone. Objectives:To compare MBP with VBP on a commercial digital mental health platform and test combinations of modalities for participants who did not respond to single-modality treatment. Design, Setting, and Participants:In this sequential multiple assignment randomized clinical trial, psychotherapy was delivered by therapists on a commercial digital mental health platform from January 10, 2022, to January 14, 2024, among 850 participants who were 18 years of age or older, English or Spanish speaking, living in the US in a state where the digital mental health platform had available therapists, scored 10 or more on the 9-item Patient Health Questionnaire (PHQ-9), and received a diagnosis of depression during intake assessment. Interventions:At baseline, participants were randomized to MBP or weekly VBP. At week 6, nonresponders were rerandomized to MBP with weekly or monthly VBP. Participants received treatment for 12 weeks. Main Outcomes and Measures:Primary outcomes included depression severity (measured by the PHQ-9), social functioning, response to treatment, and remission. Secondary outcomes included treatment engagement, therapeutic alliance, and indicators of treatment quality and satisfaction. Analysis was performed on an intention-to-treat basis. Results:The analytic sample included 850 participants (mean [SD] age, 33.8 [10.5] years; 562 women [66.1%]; mean [SD] PHQ-9 score, 15.0 [4.8]), with 423 randomized to MBP and 427 to VBP. Treatment disengagement by week 5 was more likely for VBP than MBP (VBP, 91 [21.3%]; MBP, 56 [13.2%]; Cramér V = 0.10; 95% CI, 0.03-0.13; P = .003). There were no significant differences on depression or social functioning score changes between MBP and VBP or on depression score changes for nonresponders randomized to MBP with weekly vs monthly VBP. At week 12, MBP and VBP did not differ in the proportion of participants who responded to treatment (MBP, 144 of 303 [47.5%]; VBP, 134 of 284 [47.2%]; Cramér V < .001; 95% CI, -0.08 to 0.09; P = .99) or who experienced remission (MBP, 95 of 303 [31.4%]; VBP, 86 of 284 [30.3%]; Cramér V = 0.01; 95% CI, -0.07 to 0.09; P = .85). Among nonresponders, VBP had a stronger initial therapeutic alliance than MBP at week 4 (P < .001; d = 0.48-0.57). Among participants assessed for rerandomization, there were no statistically significant differences among those who responded to treatment by week 5 (MBP, 105 of 363 [28.9%]; VBP, 93 of 336 [27.7%]; Cramér V = 0.01; 95% CI, -0.06 to 0.08; P = .78). Therapeutic alliance ratings increased across all conditions by week 10; however, these changes were not statistically significant. Video-based psychotherapy was more frequently recommended than MBP (VBP, 69 of 71 [97.2%]; MBP, 70 of 80 [87.5%]; odds ratio, 0.18; 95% CI, 0.04-0.88; P = .03). There were no significant differences in clinical outcomes between nonresponders randomized to weekly vs monthly VBP. Conclusions and Relevance:In this sequential multiple assignment randomized clinical trial comparing MBP with VBP, there were no differences between groups on improvement in depression or social functioning. More participants in the VBP group disengaged from treatment, while VBP also had greater therapeutic alliance early in treatment among nonresponders. There were no differential effects from rerandomizing nonresponders. Findings reinforced MBP as a viable alternative to VBP. Broader insurance reimbursement for MBP could improve access to evidence-based care. Future research should explore optimizing early alliance-building in MBP. Trial Registration:ClinicalTrials.gov Identifier: NCT04513080.

Examining self-reported problems of students receiving school mental health (SMH) services holds promise for informing strategies across all tiers of school support. However, no prior research has investigated students’ self-reported needs. The current study coded open-ended youth problem statements (N = 1212) from a diverse sample of 455 students (37.4

Children with conduct problems and elevated callous-unemotional (CU) traits show poor prognosis. We describe a new parenting intervention that combines a behaviorally focused parenting management training intervention (i.e., "Helping the Noncompliant Child" [HNC]; McMahon & Forehand, 2003) with an emotion coaching (EC) intervention (Katz et al., 2020) for young children (3-7 years) with conduct problems and elevated CU traits. The new integrated intervention (HNC-EC) targets the emotional deficits and parenting difficulties in this subgroup of children with conduct problems. Because the two interventions have different theoretical orientations, several conceptually based decisions were made during treatment development to facilitate integration. We describe the emotional and parenting processes targeted by the combined HNC-EC intervention, provide an overview of its content, and describe decision points where differences between basic principles of HNC and EC were addressed.

There is a need to determine the relative effect of message-based psychotherapy (MBP), an asynchronous approach that uses emails, texts, or voice or video messages to permit therapeutic exchanges, compared with video-based psychotherapy (VBP) and whether a combination of modalities would result in better outcomes for those who do not respond to either treatment alone. To compare MBP with VBP on a commercial digital mental health platform and test combinations of modalities for participants who did not respond to single-modality treatment. In this sequential multiple assignment randomized clinical trial, psychotherapy was delivered by therapists on a commercial digital mental health platform from January 10, 2022, to January 14, 2024, among 850 participants who were 18 years of age or older, English or Spanish speaking, living in the US in a state where the digital mental health platform had available therapists, scored 10 or more on the 9-item Patient Health Questionnaire (PHQ-9), and received a diagnosis of depression during intake assessment. At baseline, participants were randomized to MBP or weekly VBP. At week 6, nonresponders were rerandomized to MBP with weekly or monthly VBP. Participants received treatment for 12 weeks. Primary outcomes included depression severity (measured by the PHQ-9), social functioning, response to treatment, and remission. Secondary outcomes included treatment engagement, therapeutic alliance, and indicators of treatment quality and satisfaction. Analysis was performed on an intention-to-treat basis. The analytic sample included 850 participants (mean [SD] age, 33.8 [10.5] years; 562 women [66.1%]; mean [SD] PHQ-9 score, 15.0 [4.8]), with 423 randomized to MBP and 427 to VBP. Treatment disengagement by week 5 was more likely for VBP than MBP (VBP, 91 [21.3%]; MBP, 56 [13.2%]; Cramér V = 0.10; 95% CI, 0.03-0.13; P = .003). There were no significant differences on depression or social functioning score changes between MBP and VBP or on depression score changes for nonresponders randomized to MBP with weekly vs monthly VBP. At week 12, MBP and VBP did not differ in the proportion of participants who responded to treatment (MBP, 144 of 303 [47.5%]; VBP, 134 of 284 [47.2%]; Cramér V < .001; 95% CI, −0.08 to 0.09; P = .99) or who experienced remission (MBP, 95 of 303 [31.4%]; VBP, 86 of 284 [30.3%]; Cramér V = 0.01; 95% CI, −0.07 to 0.09; P = .85). Among nonresponders, VBP had a stronger initial therapeutic alliance than MBP at week 4 ( P < .001; d = 0.48-0.57). Among participants assessed for rerandomization, there were no statistically significant differences among those who responded to treatment by week 5 (MBP, 105 of 363 [28.9%]; VBP, 93 of 336 [27.7%]; Cramér V = 0.01; 95% CI, −0.06 to 0.08; P = .78). Therapeutic alliance ratings increased across all conditions by week 10; however, these changes were not statistically significant. Video-based psychotherapy was more frequently recommended than MBP (VBP, 69 of 71 [97.2%]; MBP, 70 of 80 [87.5%]; odds ratio, 0.18; 95% CI, 0.04-0.88; P = .03). There were no significant differences in clinical outcomes between nonresponders randomized to weekly vs monthly VBP. In this sequential multiple assignment randomized clinical trial comparing MBP with VBP, there were no differences between groups on improvement in depression or social functioning. More participants in the VBP group disengaged from treatment, while VBP also had greater therapeutic alliance early in treatment among nonresponders. There were no differential effects from rerandomizing nonresponders. Findings reinforced MBP as a viable alternative to VBP. Broader insurance reimbursement for MBP could improve access to evidence-based care. Future research should explore optimizing early alliance-building in MBP. ClinicalTrials.gov Identifier: NCT04513080

Background Implementation strategies are theorized to work well when carefully matched to implementation determinants and when factors—preconditions, moderators, etc.—that influence strategy effectiveness are prospectively identified and addressed. Existing methods for strategy selection are either imprecise or require significant technical expertise and resources, undermining their utility. This article outlines refinements to causal pathway diagrams (CPDs), a method for articulating the causal process through which implementation strategies work and offers illustrations of their use. Method CPDs are a visualization tool to represent an implementation strategy, its mechanism(s) (i.e., the processes through which a strategy is thought to operate), determinants it is intended to address, factors that may impede or facilitate its effectiveness, and the series of outcomes that should be expected if the strategy is operating as intended. We offer principles for constructing CPDs and describe their key functions. Results Applications of the CPD method by study teams from two National Institute of Health-funded Implementation Science Centers and a research grant are presented. These include the use of CPDs to (a) match implementation strategies to determinants, (b) understand the conditions under which an implementation strategy works, and (c) develop causal theories of implementation strategies. Conclusions CPDs offer a novel method for implementers to select, understand, and improve the effectiveness of implementation strategies. They make explicit theoretical assumptions about strategy operation while supporting practical planning. Early applications have led to method refinements and guidance for the field.

Background Digital Mental Health (DMH) tools are an effective, readily accessible, and affordable form of mental health support. However, sustained engagement with DMH is suboptimal, with limited research on DMH engagement. The Health Action Process Approach (HAPA) is an empirically supported theory of health behavior adoption and maintenance. Whether this model also explains DMH tool engagement remains unknown. Objective This study examined whether an adapted HAPA model predicted engagement with DMH via a self-guided website. Methods Visitors to the Mental Health America (MHA) website were invited to complete a brief survey measuring HAPA constructs. This cross-sectional study tested the adapted HAPA model with data collected using voluntary response sampling from 16,078 sessions (15,619 unique IP addresses from United States residents) on the MHA website from October 2021 through February 2022. Model fit was examined via structural equation modeling in predicting two engagement outcomes: (1) choice to engage with DMH (ie, spending 3 or more seconds on an MHA page, excluding screening pages) and (2) level of engagement (ie, time spent on MHA pages and number of pages visited, both excluding screening pages). Results Participants chose to engage with the MHA website in 94.3% (15,161/16,078) of the sessions. Perceived need (β=.66; P<.001), outcome expectancies (β=.49; P<.001), self-efficacy (β=.44; P<.001), and perceived risk (β=.17-.18; P<.001) significantly predicted intention, and intention (β=.77; P<.001) significantly predicted planning. Planning was not significantly associated with choice to engage (β=.03; P=.18). Within participants who chose to engage, the association between planning with level of engagement was statistically significant (β=.12; P<.001). Model fit indices for both engagement outcomes were poor, with the adapted HAPA model accounting for only 0.1% and 1.4% of the variance in choice to engage and level of engagement, respectively. Conclusions Our data suggest that the HAPA model did not predict engagement with DMH via a self-guided website. More research is needed to identify appropriate theoretical frameworks and practical strategies (eg, digital design) to optimize DMH tool engagement.

Background For approximately one in five children who have social, emotional, and behavioral (SEB) challenges, accessible evidence-based prevention practices (EBPPs) are critical. In the USA, schools are the primary setting for children’s SEB service delivery. Still, EBPPs are rarely adopted and implemented by front-line educators (e.g., teachers) with sufficient fidelity to see effects. Given that individual behavior change is ultimately required for successful implementation, focusing on individual-level processes holds promise as a parsimonious approach to enhance impact. Beliefs and Attitudes for Successful Implementation in Schools for Teachers (BASIS-T) is a pragmatic, multifaceted pre-implementation strategy targeting volitional and motivational mechanisms of educators’ behavior change to enhance implementation and student SEB outcomes. This study protocol describes a hybrid type 3 effectiveness-implementation trial designed to evaluate the main effects, mediators, and moderators of the BASIS-T implementation strategy as applied to Positive Greetings at the Door, a universal school-based EBPP previously demonstrated to reduce student disruptive behavior and increase academic engagement. Methods This project uses a blocked randomized cohort design with an active comparison control (ACC) condition. We will recruit and include approximately 276 teachers from 46 schools randomly assigned to BASIS-T or ACC conditions. Aim 1 will evaluate the main effects of BASIS-T on proximal implementation mechanisms (attitudes, subjective norms, self-efficacy, intentions to implement, and maintenance self-efficacy), implementation outcomes (adoption, reach, fidelity, and sustainment), and child outcomes (SEB, attendance, discipline, achievement). Aim 2 will examine how, for whom, under what conditions, and how efficiently BASIS-T works, specifically by testing whether the effects of BASIS-T on child outcomes are (a) mediated via its putative mechanisms of behavior change, (b) moderated by teacher factors or school contextual factors, and (c) cost-effective. Discussion This study will provide a rigorous test of BASIS-T—a pragmatic, theory-driven, and generalizable implementation strategy designed to target theoretically-derived motivational mechanisms—to increase the yield of standard EBPP training and support strategies. Trial registration ClinicalTrials.gov ID: NCT05989568. Registered on May 30, 2023.

Background:Intervention adaptation is often necessary to improve the fit between evidence-based practices/programs and implementation contexts. Existing frameworks describe intervention adaptation processes but do not provide detailed steps for prospectively designing adaptations, are designed for researchers, and require substantial time and resources to complete. A pragmatic approach to guide implementers through developing and assessing adaptations in local contexts is needed. The goal of this project was to develop Making Optimal Decisions for Intervention Flexibility during Implementation (MODIFI), a method for intervention adaptation that leverages human centered design methods and is tailored to the needs of intervention implementers working in applied settings with limited time and resources. Method:MODIFI was iteratively developed via a mixed-methods modified Delphi process. Feedback was collected from 43 implementation research and practice experts. Two rounds of data collection gathered quantitative ratings of acceptability (Round 1) and feasibility (Round 2), as well as qualitative feedback regarding MODIFI revisions analyzed using conventional content analysis. Results:In Round 1, most participants rated all proposed components as essential but identified important avenues for revision which were incorporated into MODIFI prior to Round 2. Round 2 emphasized feasibility, where ratings were generally high and fewer substantive revisions were recommended. Round 2 changes largely surrounded operationalization of terms/processes and sequencing of content. Results include a detailed presentation of the final version of the three-step MODIFI method (Step 1: Learn about the users, local context, and intervention; Step 2: Adapt the intervention; Step 3: Evaluate the adaptation) along with a case example of its application. Discussion:MODIFI is a pragmatic method that was developed to extend the contributions of other research-based adaptation theories, models, and frameworks while integrating methods that are tailored to the needs of intervention implementers. Guiding teams to tailor evidence-based interventions to their local context may extend for whom, where, and under what conditions an intervention can be effective.