Beyond the Checklist: Developing Pragmatic Measures in Implementation Science for Real-World Impact

Abstract

This article addresses the critical challenge of developing and applying pragmatic measures in implementation science to accelerate the translation of evidence-based interventions into routine practice. Tailored for researchers, scientists, and drug development professionals, we explore the foundational need for stakeholder-engaged definitions of pragmatism, methodological frameworks for measure development, strategies for troubleshooting and optimizing implementation packages, and the vital role of validation through comparative effectiveness research. By synthesizing the latest methodologies and trends, this resource provides a comprehensive guide for creating measures and strategies that are not only scientifically rigorous but also feasible, relevant, and impactful in diverse, real-world settings.

Redefining Pragmatism: Why Stakeholder Perspectives Are Shifting the Foundation of Implementation Measurement

Current approaches to developing pragmatic measures in implementation science predominantly rely on expert panels and psychometric validation. This application note identifies a critical gap in these methods: the lack of incorporation of diverse stakeholder perspectives, particularly those with lived healthcare experience. We present evidence that this limitation risks creating measures misaligned with real-world practicalities and propose structured methodologies to address this gap through participatory research designs, detailed protocols for stakeholder engagement, and innovative evaluation frameworks. By integrating these approaches, implementation science can develop truly pragmatic measures that balance methodological rigor with practical relevance across diverse healthcare contexts.

The development of pragmatic measures in implementation science has traditionally been dominated by expert-driven approaches, creating a significant disconnect between measurement tools and the practical realities of healthcare settings [1]. Pragmatic measures are designed to be relevant, feasible, and usable in real-world practice conditions, enabling stakeholders to assess implementation barriers, monitor progress, and evaluate outcomes effectively [2]. Despite the field's emphasis on practicality, current methodologies have primarily inherited definitions of pragmatism from the evidence-based healthcare movement without sufficiently incorporating perspectives from those who ultimately use these measures in practice [1].

This overreliance on expert panels has resulted in several critical limitations. Traditional approaches often prioritize psychometric properties while overlooking the practical concerns of end-users, including healthcare providers, patients, and public stakeholders with lived experience of healthcare systems [1]. The Psychometric and Pragmatic Evidence Rating Scale (PAPERS) exemplifies this trend, having been developed with limited stakeholder involvement despite its intended application in real-world settings [1]. This methodological gap risks creating measures that, while statistically sound, lack relevance and feasibility in routine practice, ultimately limiting their utility for guiding implementation efforts and informing healthcare decisions.

Table 1: Limitations of Expert-Driven Approaches to Pragmatic Measure Development

Limitation	Impact on Measure Quality	Consequence for Implementation
Restricted definition of pragmatism	Narrow focus on psychometric properties over practical utility	Measures may not address real-world implementation challenges
Exclusion of stakeholder perspectives	Overlooking practical concerns of end-users	Reduced adoption and feasibility in routine practice settings
Potential for measurement bias	Fixed scales may not adapt to evolving contexts	Limited applicability across diverse populations and settings
Emphasis on quantitative methods	Neglect of qualitative insights and contextual factors	Incomplete understanding of implementation phenomena

Theoretical Framework: Expanding the Conceptualization of Pragmatism

A reconceptualization of pragmatism in implementation science requires returning to its philosophical foundations. Pierce's original maxim of pragmatism states: "Consider the practical effects of the objects of your conception. Then, your conception of those effects is the whole meaning of the conception" [1]. This principle suggests that the evaluation of pragmatism must account for the constantly changing social dynamic between real-world scenarios and measurement tools, rather than relying on static, predetermined criteria.

Contemporary implementation science has primarily focused on two areas of pragmatism: (1) developing embedding methods or frameworks for practice (such as pragmatic trials or RE-AIM), and (2) creating measures to evaluate the pragmatic qualities of implementation tools [1]. The pragmatic-explanatory continuum illustrates how research designs vary in their alignment with real-world conditions, with pragmatic trials designed to evaluate effectiveness in routine practice settings rather than efficacy under optimal conditions [3]. This continuum can be visualized using tools like the PRECIS-2 framework, which assesses trials across nine domains including eligibility criteria, flexibility of interventions, and primary outcomes [4].

The fundamental challenge in evaluating pragmatism lies in the abstraction required to measure the measures themselves. Any use of a scale represents an attempt to apply theoretical constructs to complex realities, creating inherent limitations in measurement accuracy [1]. Methodological biases may further privilege certain forms of expertise and measurement approaches while neglecting diverse perspectives and exceptional cases that do not fit predetermined categories [1]. Addressing these limitations requires expanding conceptions of pragmatism and incorporating diverse voices throughout the measurement development process.

Diagram 1: Conceptual Framework for Expanded Pragmatism in Implementation Science. This diagram contrasts traditional expert-driven approaches with expanded stakeholder-informed methodologies for developing pragmatic measures.

Current Evidence: Documenting the Stakeholder Perspective Gap

Recent empirical investigations have documented significant limitations in how pragmatic measures are developed and evaluated. A 2025 study explicitly explored stakeholder views on pragmatic measures through participatory research methods, convening a working group of eight stakeholders with lived healthcare experience [1]. This research revealed substantial concerns about the restricted definition of pragmatism in current implementation science, potential biases in measurement approaches, and the necessity for more holistic, pluralistic methodologies that incorporate diverse perspectives when developing and evaluating implementation theory and metrics [1].

Stakeholders participating in this research identified six critical themes that highlight gaps in traditional approaches to pragmatic measurement:

• Complexity of Subject Matter: Participants acknowledged the intellectual challenge inherent in understanding psychometrics and pragmatic measures, expressing preference for discussing tangible outcomes rather than abstract constructs [1].
• Weighting Pragmatism: While recognizing the importance of measurement constructs, participants questioned the feasibility of ranking outcome measures due to potential bias and subjectivity [1].
• Bias in Measurement: Concerns were raised about potential bias infiltrating fixed scales over time, emphasizing how pragmatism requires dynamic thinking to remain representative [1].
• Holism: Participants highlighted that human experiences are multidimensional and cannot be fully reduced to clinical symptoms and measurement scales without considering social, relational, and quality of life factors [1].
• Plurality: The importance of incorporating diverse perspectives when evaluating measures was emphasized, recognizing the value of considering pragmatism on an individual case-by-case basis [1].
• Perspectivism: Discussions reflected the complexities of reconciling differing perspectives, particularly in culturally diverse contexts, and cautioned against one-size-fits-all approaches to pragmatism [1].

These findings align with earlier research protocols that acknowledged significant gaps in measurement as among the most critical barriers to advancing implementation science [2]. A 2015 study protocol identified three fundamental issues: (a) lack of stakeholder involvement in defining pragmatic measure qualities; (b) scarcity of measures, particularly for implementation outcomes; and (c) unknown psychometric and pragmatic strength of existing measures [2].

Table 2: Documented Gaps in Pragmatic Measure Development

Evidence Source	Primary Gap Identified	Methodological Limitation	Year
Stakeholder working group study [1]	Restricted definition of pragmatism excluding stakeholder perspectives	Overreliance on expert panels rather than participatory approaches	2025
Implementation science measurement review [5]	Majority of implementation measures lack rigorous psychometric evaluation	Context-specific measures rarely reused, limiting evidence accumulation	2016
Measure development protocol [2]	Lack of stakeholder involvement in defining pragmatic qualities	Measures developed without input from end-users in practice settings	2015
Pragmatic trials review [3]	Limited generalizability of explanatory trial results to real-world settings	Traditional designs prioritize internal validity over external validity	2011

Methodological Protocols: Structured Approaches for Stakeholder-Informed Measure Development

Participatory Research Protocol for Stakeholder Engagement

To address the critical gaps in traditional approaches, we propose a structured participatory research protocol for engaging stakeholders in pragmatic measure development:

Phase 1: Framing the Problem

• Objective: Establish shared understanding of implementation science concepts without steering participants toward validation of existing measures
• Methods: Develop and refine non-technical informational resources including short audiovisual presentations, concise flyers, and consideration worksheets [1]
• Implementation: Present key concepts such as implementation science, pragmatism, pragmatic measures, and existing tools like PAPERS in accessible language while highlighting the importance of diversity and representation in research [1]
• Validation: Share resources with public research panels for feedback and revisions to enhance inclusivity and comprehensibility

Phase 2: Working Group Assembly and Debates

• Recruitment Strategy: Employ targeted advertising through public research networks (e.g., NIHR Applied Research Collaboration, Shaping Our Lives) to attract individuals with diverse perspectives and firsthand experiences of healthcare systems [1]
• Participant Selection: Utilize selection forms to ensure demographic and experiential diversity within working groups [1]
• Meeting Structure: Conduct three one-hour discussions over three weeks with group sizes limited to eight members to facilitate in-depth exchanges and meaningful contributions [1]
• Compensation: Provide appropriate compensation for participant time to ensure equitable participation and acknowledge the value of stakeholder input [1]
• Facilitation Approach: Create supportive, inclusive environments conducive to open dialogue using pre-circulated topic guides focused on key themes and questions about pragmatism in implementation science [1]

Phase 3: Analysis and Interpretation

• Data Processing: Record, transcribe, and analyze debates using qualitative analysis software
• Analytical Approach: Employ abductive analysis moving iteratively between close reading of data and theoretical concepts from pragmatic philosophy [1]
• Validation: Share coding books and emergent themes with research team for discussion and verification, then circulate final paper to participants with opportunity for co-authorship [1]
• Reporting: Utilize established checklists (e.g., GRIPP2) to ensure comprehensive reporting of stakeholder involvement [1]

Multi-Method Measure Development Protocol

For comprehensive pragmatic measure development, we recommend an expanded version of established protocols [2] incorporating stakeholder perspectives throughout:

Stage 1: Domain Delineation

• Conduct individual interviews with stakeholder panelists to explore what pragmatic measurement means in practice, including terms used to describe pragmatic measures and attributes considered most relevant [2]
• Connect inductively developed themes from stakeholders with deductively derived themes from systematic literature reviews of the pragmatic construct [2]
• Synthesize findings to populate pragmatic measure construct criteria

Stage 2: Clarifying Internal Structure

• Implement modified Q-sort methodology as a bridge between qualitative and quantitative inquiry [2]
• Engage stakeholder participants (N=20) in sorting items into preliminary dimensions generated from domain delineation [2]
• Utilize survey software to sort items along scales from "most closely related to" to "least closely related to" each pragmatic dimension
• Include items ranked at 5 and above across all stakeholders in dimension definitions and assess clarity, precision, and distinctness of each dimension

Stage 3: Establishing Priority Criteria

• Employ modified, multi-round, online Delphi method with stakeholder participants to achieve consensus on relative weights of dimensions [2]
• In initial round, submit dimensions and associated items to stakeholders who distribute points to reflect relative importance for inclusion in pragmatic rating criteria anchors [2]
• Calculate central tendency and dispersion metrics for each dimension to guide refinement of criteria
• Conduct multiple rounds until consensus achieved on priority dimensions and weighting

Stage 4: Validation and Testing

• Assess test-retest and inter-rater reliability of emergent rating system with multiple implementation scientists [2]
• Conduct known-groups validity testing of top prioritized pragmatic criteria [2]
• Evaluate structural validity, predictive validity, discriminant validity, and sensitivity to change through systematic methodological studies

Diagram 2: Integrated Workflow for Stakeholder-Informed Pragmatic Measure Development. This diagram illustrates the sequential phases and stages of the proposed methodology, highlighting continuous stakeholder engagement throughout the process.

Implementation Tools and Assessment Frameworks

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Methodological Tools for Stakeholder-Informed Pragmatic Measure Development

Research Tool	Primary Function	Application Context	Key Features
PRECIS-2 Framework [4]	Trial design assessment across pragmatic-explanatory continuum	Evaluating clinical trial designs for real-world applicability	9-domain evaluation tool with visual "wheel" representation
PAPERS Rating Scale [1]	Assess pragmatic qualities of implementation measures	Evaluating usability and practicality of existing measures	Rates measures across multiple pragmatic criteria
Q-Sort Methodology [2]	Clarify internal structure of complex constructs	Sorting and prioritizing measure dimensions from stakeholder input	Bridges qualitative and quantitative inquiry approaches
Delphi Method [2]	Achieve expert consensus on criteria priorities	Establishing relative weights for pragmatic measure dimensions	Iterative feedback process with structured communication
Abductive Analysis [1]	Analyze qualitative stakeholder data	Interpreting working group discussions and debates	Moves between empirical data and theoretical concepts
GRIPP2 Checklist [1]	Reporting stakeholder involvement	Ensuring comprehensive reporting of participatory research	Standardized reporting guideline for patient and public involvement

Enhanced Assessment Framework for Pragmatic Measures

Building on existing tools like PAPERS, we propose an expanded assessment framework that incorporates stakeholder perspectives across eight critical domains:

• Relevance: Alignment with stakeholder-identified priorities and concerns
• Feasibility: Practical considerations for implementation in routine practice
• Accessibility: Comprehensibility across diverse literacy and health literacy levels
• Adaptability: Capacity to accommodate contextual variations and evolving needs
• Burden: Time, resource, and cognitive requirements for administration
• Actionability: Utility for informing decisions and guiding implementation efforts
• Inclusivity: Consideration of diverse perspectives and exceptional cases
• Psychometric Strength: Traditional measurement properties including reliability and validity

Each domain should be rated using a standardized scoring system that incorporates both expert assessment and stakeholder evaluation, with specific benchmarks for determining adequate performance across domains. This dual-perspective approach ensures that measures demonstrate both methodological rigor and practical utility.

Application Notes and Implementation Guidelines

Practical Considerations for Implementation

Successful application of these methodologies requires attention to several practical considerations. First, stakeholder compensation must be adequate to ensure equitable participation, particularly for individuals with lived experience who may face financial barriers to engagement [1]. Second, accessibility of methodological materials is crucial, requiring the development of non-technical resources that explain complex concepts in understandable language without oversimplification [1]. Third, power dynamics in researcher-stakeholder relationships must be actively managed to ensure genuine partnership rather than tokenistic inclusion.

Implementation teams should establish clear protocols for documenting stakeholder contributions and ensuring that diverse perspectives are meaningfully incorporated rather than merely acknowledged. This includes creating mechanisms for resolving disagreements between stakeholder and researcher perspectives, with predetermined processes for balancing methodological requirements with practical considerations.

Ethical Dimensions and Inclusivity Requirements

The expanded approach to pragmatic measure development introduces important ethical considerations. The principle of perspectivism recognizes that value judgments about what constitutes "pragmatic" are inherently subjective and may vary across different cultural and contextual settings [1]. This necessitates explicit attention to whose perspectives are included and how potential conflicts between different stakeholder viewpoints are reconciled.

Additionally, measures must be evaluated for their potential to perpetuate existing healthcare disparities through measurement bias that may disadvantage certain populations [1]. This requires critical examination of how fixed scales might embed assumptions that do not hold across diverse communities and developing approaches that maintain interpretive flexibility to mitigate such biases.

Moving beyond expert panels represents a necessary evolution in how implementation science conceptualizes and develops pragmatic measures. The methodologies and protocols presented here provide a structured approach for incorporating diverse stakeholder perspectives throughout the measure development process, addressing critical gaps in traditional approaches. By embracing more inclusive, participatory methods and balancing psychometric rigor with practical relevance, the field can develop measures that truly serve the needs of those implementing and experiencing healthcare interventions in real-world settings.

Future research should explore optimal strategies for balancing stakeholder perspectives with methodological requirements when tensions arise, develop more sophisticated approaches for assessing the pragmatic qualities of measures across diverse contexts, and establish benchmarks for determining when measures demonstrate sufficient pragmatism for widespread use. Additionally, investigation is needed into how to efficiently adapt existing measures with strong psychometric properties but limited pragmatism for enhanced usability in routine practice settings.

Application Note: Theoretical Foundations and Modern Interpretations

Peirce's Pragmatic Maxim and Its Core Principles

The foundational principle of pragmatism was first proposed by Charles Sanders Peirce in the 1870s as a method for clarifying concepts and meaning through their practical consequences [6]. The crux of Peirce's pragmatic maxim states: "Consider what effects, which might conceivably have practical bearings, we conceive the object of our conception to have. Then the whole of our conception of those effects is the whole of our conception of the object" [6]. This principle was originally developed as a tool to achieve the highest grade of conceptual clarity, moving beyond mere familiarity or definitional understanding to a comprehensive grasp of a concept's practical implications in real-world contexts [6].

Peirce identified three distinct grades of clarity for understanding concepts: (1) unreflective, everyday familiarity; (2) the ability to provide a general definition; and (3) understanding through the pragmatic maxim—knowing what practical effects to expect from holding that concept to be true [6]. For example, a complete understanding of "vinegar" requires not only recognizing it in daily experience and defining it as diluted acetic acid, but also deriving conditional expectations about its behavior, such as "if I dip litmus paper into it, it will turn red" [6]. This third grade of clarity forms the essence of Peirce's pragmatic method, transforming abstract concepts into testable, practical expectations.

The Evolution from Pragmatism to Pragmaticism

Historical development reveals a significant divergence between Peirce's original methodological principle and later interpretations. Peirce remained dissatisfied with his early formulations and their subsequent development by fellow pragmatists, particularly William James and John Dewey [6] [7]. This dissatisfaction led him to rename his doctrine "pragmaticism" in later life—a term he explicitly designed to be "ugly enough to be safe from kidnappers" [7]. This deliberate rebranding distinguished his logically rigorous, scientifically-grounded approach from what he perceived as the more "nominalistic" and psychologically-oriented versions that had gained popularity [6].

The fundamental distinction lies in their respective conceptions of truth. For Peirce, truth represented "the ideal end of inquiry: that which would be agreed upon by all inquirers in the long run" within a "community of inquiry" [7]. This contrasted sharply with James's more individualistic and utilitarian interpretation, which emphasized "what works" for the particular believer [7]. Peirce maintained that his original pragmatic maxim served two crucial purposes: guiding scientific inquiry by highlighting which investigations would most impact the settled state of belief, and filtering out meaningless metaphysical statements that lacked practical bearings [6].

Contemporary Framework for Pragmatic Measures

In modern implementation science, the pragmatic measures construct has been systematically operationalized through stakeholder-driven research. The Psychometric and Pragmatic Evidence Rating Scale (PAPERS) emerged from rigorous methodology including systematic literature reviews and extensive stakeholder engagement [8]. This work identified four conceptually distinct domains that comprise pragmatic measures: (1) Acceptable—measures that stakeholders find suitable and appropriate; (2) Compatible—measures that align with existing workflows and systems; (3) Easy—measures that are simple to implement and use; and (4) Useful—measures that provide valuable information for decision-making and practice [8].

Recent research emphasizes the critical importance of incorporating diverse stakeholder perspectives, including those with lived healthcare experience, when developing and evaluating pragmatic measures [1]. Stakeholders have expressed concerns about restricted definitions of pragmatism, potential biases in measurement, and the necessity for holistic, pluralistic approaches that acknowledge the complexity of human experience and the limitations of reducing multidimensional aspects of being human to clinical symptoms and measurement scales [1]. This expanded conceptualization moves beyond mere psychometric properties to embrace the dynamic social realities in which these measures are deployed.

Table 1: Evolution of Pragmatic Thought from Philosophical Principle to Implementation Science

Aspect	Peirce's Original Pragmatism	Modern Implementation Science
Core Principle	Clarify meaning through practical consequences [6]	Enhance real-world applicability of research and measures [1]
Primary Method	Pragmatic maxim and three grades of clarity [6]	Stakeholder-driven framework development (e.g., PAPERS) [8]
Truth Basis	Ideal end of communal inquiry [7]	Practical, usable measures rooted in practice [1]
Key Applications	Scientific inquiry and metaphysical filtering [6]	Implementation strategies and healthcare improvement [1] [9]
Limitations Addressed	Meaningless metaphysical statements [6]	Restricted definitions and measurement biases [1]

Application Note: Pragmatism in Pharmaceutical Implementation Science

The Implementation Challenge in Pharmaceutical Development

The pharmaceutical industry faces a significant implementation gap that pragmatism directly addresses. Recent studies indicate that approximately 50% of approved therapies achieve widespread adoption due to systemic barriers, creating a substantial "know-do" gap between discovery and delivery [9]. Evidence-based innovations take an average of 17 years to be incorporated into routine practice, with only half of proven interventions ever achieving broad uptake [9]. This adoption bottleneck represents not only a scientific challenge but also a practical one with real consequences: wasted resources, hindered patient impact, and exacerbated medical mistrust when inequities surface only after regulatory approval [9].

Implementation science offers a transformative lens for pharmaceutical companies by fundamentally reframing the core question from "Does this therapy work?" to "How can this therapy work best in real-world situations?" [9]. This shift in perspective is essential for identifying systemic and contextual factors that influence treatment success beyond traditional efficacy endpoints. For instance, glucagon-like peptide-1 (GLP-1) receptor agonists, while efficacious for diabetes and weight management, raise persistent practical questions about long-term adherence, equitable distribution systems, and the quantification of social perceptions affecting uptake [9]. Pragmatic approaches enable companies to address these implementation challenges proactively during development rather than reactively post-approval.

Strategic Integration of Implementation Science

A layered planning approach effectively embeds implementation science throughout pharmaceutical development. This methodology incorporates implementation considerations at three distinct levels: (1) strategic brand or portfolio planning; (2) product-specific development plans; and (3) individual clinical trial designs [9]. By addressing real-world barriers and facilitators at each level, organizations can plan for implementation from the outset rather than attempting retrofitting late in development. This proactive stance allows for the identification of workflow challenges, adherence patterns, and practical needs that inform both trial design and health economic models, ensuring they reflect realistic scenarios rather than idealized assumptions [9].

Successful integration typically employs three core strategies: First, embedding structured frameworks to understand contextual factors influencing adoption; second, iterative planning that evolves strategies as new evidence emerges; and third, early collaboration with patients, providers, and payers to co-develop solutions reflecting their needs [9]. In some cases, hybrid trial designs that combine clinical effectiveness with implementation endpoints may be considered, though their complexity requires careful evaluation of feasibility within specific development programs [9]. The biosimilars implementation in oncology provides a compelling case study where hesitancy rooted in concerns about switching stable patients and limited real-world data was successfully addressed through stakeholder engagement, comprehensive education, and aligned organizational policies [9].

Value Demonstration and Business Case

The business case for implementation science in pharmaceutical development is robust and multidimensional. Companies leveraging these approaches demonstrate measurable value across several domains: (1) accelerated adoption through early feedback loops that shorten the time from approval to widespread use; (2) improved outcomes by addressing adherence challenges during development; (3) enhanced trust through demonstrated commitment to real-world impact; and (4) reduced costs by proactively resolving implementation challenges before they become widespread problems [9]. This comprehensive value proposition extends beyond patient benefits to include significant advantages for healthcare systems and industry stakeholders.

Companies can begin integration through targeted pilots focused on specific challenges such as patient adherence or workflow optimization. These small-scale initiatives require modest financial commitments while generating valuable insights and building organizational confidence in the approach [9]. This incremental methodology allows implementation science to evolve into a core component of pharmaceutical development, ultimately ensuring that innovations not only reach the market but achieve optimal results across diverse populations and settings.

Table 2: Pharmaceutical Implementation Framework - Barriers and Pragmatic Solutions

Development Phase	Common Implementation Barriers	Pragmatic Solutions	Stakeholder Engagement Focus
Early Clinical Development	Lack of real-world workflow considerations	Embed implementation endpoints in trial design [9]	Provider input on administration feasibility [9]
Late-Stage Trials	Limited understanding of adherence patterns	Hybrid designs testing implementation strategies [9]	Patient feedback on burden and acceptability [1]
Regulatory Submission	Insufficient data on contextual facilitators	Proactive collection of real-world implementation data [9]	Payer perspectives on evidence requirements [9]
Post-Market Phase	Variable uptake across healthcare settings	Tailored implementation kits based on barrier assessments [9]	Health system input on scalability and sustainability [8]

Experimental Protocols

Protocol: Stakeholder-Driven Pragmatic Measure Development

Objective: To develop and validate pragmatic measures for implementation science research through systematic stakeholder engagement, ensuring the measures are acceptable, compatible, easy, and useful for end-users in real-world settings [8].

Background: Traditional implementation measures often fail to be adopted in community settings due to insufficient attention to pragmatic qualities. This protocol outlines a rigorous methodology for engaging diverse stakeholders throughout measure development, aligning with Peirce's pragmatic maxim by focusing on the practical consequences and usability of measurement instruments [6] [1].

Materials and Reagents:

• Recording equipment for stakeholder sessions
• Qualitative data analysis software (e.g., NVivo)
• Concept mapping tools
• Survey platforms for Delphi processes
• PAPERS (Psychometric and Pragmatic Evidence Rating Scale) framework [8]

Procedure:

• Stakeholder Recruitment and Preparation
  • Convene a working group of 8-10 stakeholders, specifically including individuals with expertise by experience of healthcare systems [1].
  • Develop accessible educational materials (video, flyer, worksheet) to explain implementation science, pragmatism, and pragmatic measures in non-technical language [1].
  • Conduct three one-hour discussions over three weeks, using platforms accessible to participants (e.g., Microsoft Teams) [1].

• Structured Stakeholder Engagement

  • Facilitate discussions using topic guides focused on key themes from existing pragmatic measures [1].
  • Encourage participants to explore complexity, weighting of pragmatism, potential biases, holism, plurality, and perspectivism in measure design [1].
  • Compensate participants for their time to ensure equitable participation [1].

• Data Analysis and Interpretation

  • Record, transcribe, and analyze discussions using abductive analysis in qualitative data analysis software [1].
  • Create codes through iterative movement between close reading of data and theoretical concepts from pragmatic philosophy [1].
  • Conduct abductive data reduction through coding equations to refine and structure emerging themes [1].
  • Share coding book with research team for discussion and verification at each analysis stage [1].

• Concept Mapping and Criteria Validation

  • Engage stakeholders (N=24) in concept mapping activity to organize identified pragmatic criteria into conceptually distinct categories [8].
  • Apply multidimensional scaling and hierarchical cluster analysis to concept mapping data [8].
  • Calculate descriptive statistics for clarity and importance ratings at category and individual criterion levels [8].

• Delphi Consensus Process

  • Conduct a Delphi process to develop consensus on the most important pragmatic criteria [8].
  • Develop quantifiable pragmatic rating criteria based on stakeholder priorities [8].
  • Share final results with participants and offer co-authorship for substantial contributions [1].

Validation Methods:

• Use GRIPP2 checklist to ensure quality of stakeholder engagement reporting [1].
• Apply final pragmatic rating criteria to existing implementation measures [8].
• Assess measures across four pragmatic domains: Acceptable, Compatible, Easy, and Useful [8].

Protocol: Pharmaceutical Implementation Planning Assessment

Objective: To assess and enhance the implementation potential of pharmaceutical products during development by identifying and addressing real-world barriers to adoption, leveraging implementation science frameworks and stakeholder input.

Background: Pharmaceutical innovations frequently face adoption bottlenecks post-approval due to insufficient attention to implementation factors during development. This protocol provides a systematic approach for embedding implementation science throughout the pharmaceutical development lifecycle, aligning with Peirce's pragmatic emphasis on practical consequences [6] [9].

Materials and Reagents:

• Implementation science frameworks (e.g., CFIR, RE-AIM)
• Stakeholder mapping tools
• Barrier and facilitator assessment guides
• Health equity assessment frameworks
• Implementation strategy compilations

Procedure:

• Early Implementation Planning (Phase 1-2)
  • Conduct stakeholder mapping to identify key implementation partners (patients, providers, payers, health systems) [9].
  • Perform preliminary barrier and facilitator analysis across multiple contexts (clinical, community, policy) [9].
  • Integrate implementation outcomes into early clinical trial designs where feasible [9].
  • Develop preliminary implementation blueprint outlining core strategies for adoption [9].

• Late-Stage Implementation Preparation (Phase 3)

  • Establish Implementation Advisory Board with diverse stakeholder representation [9].
  • Conduct systematic mixed-methods assessment of implementation determinants [9].
  • Test implementation strategies using hybrid trial designs where appropriate [9].
  • Refine implementation blueprint with specific tactics for addressing identified barriers [9].

• Pre-Launch Implementation Readiness (Registration Phase)

  • Develop tailored implementation kits for different healthcare settings [9].
  • Create measurement and feedback systems for monitoring real-world adoption [9].
  • Establish implementation support infrastructure (training, technical assistance) [9].
  • Finalize value proposition incorporating implementation evidence for payers [9].

• Post-Launch Implementation Optimization

  • Monitor early adoption patterns across different contexts and populations [9].
  • Implement rapid-cycle evaluation and strategy adaptation [9].
  • Disseminate implementation best practices and lessons learned [9].
  • Plan for long-term sustainability and scalability [9].

Evaluation Metrics:

• Time from approval to widespread adoption
• Adherence rates across different populations
• Implementation fidelity across diverse settings
• Stakeholder satisfaction with implementation process
• Health equity in access and outcomes

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents and Tools for Pragmatic Implementation Research

Tool/Reagent	Function	Application Context	Key Features
PAPERS (Psychometric and Pragmatic Evidence Rating Scale)	Evaluates pragmatic qualities of implementation measures [8]	Measure development and selection	Assesses measures across four domains: Acceptable, Compatible, Easy, Useful [8]
Stakeholder Engagement Framework	Ensures diverse perspectives in measure development [1]	Participatory research design	Incorporates expertise by experience, uses accessible educational materials [1]
Concept Mapping Methodology	Organizes pragmatic criteria into conceptually distinct categories [8]	Measure development and refinement	Uses multidimensional scaling and hierarchical cluster analysis [8]
Abductive Analysis Approach	Iterative movement between data and theoretical concepts [1]	Qualitative data analysis	Creates codes through close reading of data and pragmatic philosophy [1]
Implementation Science Frameworks	Guides understanding of contextual factors influencing adoption [9]	Pharmaceutical implementation planning	Identifies barriers and facilitators across multiple levels [9]
Hybrid Trial Designs	Combines clinical effectiveness with implementation endpoints [9]	Clinical development optimization	Streamlines evidence generation for both efficacy and real-world implementation [9]
Barrier Assessment Tools	Systematically identifies obstacles to implementation [9]	Pre-implementation planning	Informs tailored implementation strategies [9]
GRIPP2 Reporting Checklist	Ensures quality reporting of stakeholder involvement [1]	Research documentation	Standardizes reporting of patient and public engagement [1]

Application Note: Integrating Pluralistic Stakeholder Values in Implementation Research

Theoretical Foundation

Engaging stakeholders in implementation research is critical for developing interventions and measures that are both scientifically rigorous and contextually relevant. This approach recognizes the pluralistic nature of value expectations across different stakeholder groups, which, if understood systematically, can significantly enhance the legitimacy and effectiveness of implementation efforts [10]. Research demonstrates that community-engaged implementation research contributes to greater community member empowerment, validates study findings, and increases community investment in successful implementation outcomes [11].

The conceptual foundation for this work rests on three core principles: First, it engages people with intimate knowledge of the setting in data collection or analysis. Second, it enhances the validity of data and its interpretation through multiple observers and data sources. Third, it empowers participants by giving them agency and investment in implementation success [11]. When these principles are operationalized effectively, implementation strategies can address the diverse, and sometimes competing, value expectations that different stakeholders bring to the research process.

Documented Value Expectations Across Stakeholder Groups

Empirical research has identified distinct value expectations across different stakeholder groups, highlighting the necessity of methodological approaches that can capture this pluralism [10]. Understanding these diverse perspectives is essential for designing implementation strategies that resonate with all involved parties.

Table: Documented Value Expectations Across Stakeholder Groups

Stakeholder Group	Primary Value Expectations	Implementation Focus
Government/Policy Actors	Process integrity, mandate fulfillment, decision-making integration [10]	System-level integration, policy alignment
Industry/Healthcare Providers	Cost-effectiveness, implementation efficiency, procedural certainty [10]	Resource optimization, workflow compatibility
Conservation/Technical Groups	Data quality, technical robustness, methodological rigor [10]	Evidence quality, analytical soundness
Interested & Affected Parties (IAPs)	Local context issues, immediate relevance, accessibility [10]	Local impact, contextual appropriateness

Case Study Evidence

A case study of strategic environmental assessment demonstrated that while all stakeholder groups shared some common value expectations, each group maintained distinct priorities that reflected their organizational roles and responsibilities [10]. This pluralism necessitates implementation approaches that are flexible enough to accommodate diverse definitions of success while maintaining methodological rigor.

Experimental Protocols for Stakeholder Engagement

Protocol 1: Concept Mapping for Priority Setting

Purpose and Rationale

Concept mapping is a structured conceptualization process that yields a conceptual framework for how a group views a particular topic [11]. This method is particularly valuable for engaging diverse stakeholders in identifying and prioritizing implementation strategies, as it combines qualitative group processes with quantitative analytical techniques to represent relationships between ideas visually.

Materials and Reagents

Table: Research Reagent Solutions for Concept Mapping

Item	Function	Implementation Example
Groupwisdom Software	Analyzes sort and rating data; generates visual concept maps [11]	Creates weighted cluster maps, ladder graphs, and go-zone maps
Structured Focus Group Guide	Guides interpretation sessions for preliminary findings [11]	Facilitates discussion on cluster meaningfulness and strategy prioritization
Participant Recruitment Framework	Ensures diverse stakeholder representation [11]	Engages clinic members, providers, community advocates, and policymakers
Rating and Sorting Materials	Captures stakeholder perspectives on meaning, importance, and feasibility [11]	Uses 4-point scales for importance/feasibility and pile sorting tasks

Procedure

The protocol follows six sequential steps [11]:

• Preparation: Identify focal areas and determine participant selection criteria. For example, in a study to improve HPV vaccination rates, researchers engaged 10 clinic members and 13 community members from medically underserved communities [11].
• Generation: Generate statements addressing the focal question through brainstorming sessions or by compiling evidence-based strategies from existing sources and qualitative data.
• Structuring: Participants independently sort statements into piles based on perceived similarity and rate each statement on importance and feasibility using a 4-point scale.
• Representation: Input data into concept mapping software (e.g., Groupwisdom) to generate quantitative summaries and visual representations, including point maps and similarity matrices.
• Interpretation: Convene participants to collectively review concept maps, assess cluster domains, evaluate items forming each cluster, and discuss cluster content.
• Utilization: Discuss findings to determine how they inform the original focal question and guide implementation strategy selection.

Protocol 2: Methodological Pluralism in Evaluation

Purpose and Rationale

Methodological pluralism applies multiple methodologies and epistemological stances to build a more complete understanding of complex interventions [12]. This approach is particularly valuable for capturing the pluralistic needs of diverse stakeholders, as it redresses the limitations inherent in any single method and provides a more holistic and textured analysis.

Materials and Reagents

Table: Research Reagent Solutions for Methodological Pluralism

Item	Function	Implementation Example
Developmental Evaluation Framework	Supports real-time feedback and adaptation in complex initiatives [12]	Tracks emergent outcomes and informs iterative strategy adjustments
Principles-Focused Evaluation Guide	Assesses adherence to guiding principles in dynamic environments [12]	Evaluates implementation against established community engagement principles
Network Analysis Tools	Maps and measures collaboration patterns and relationships [12]	Quantifies changes in stakeholder connections and knowledge exchange
Framework Analysis Methodology	Provides systematic thematic analysis of qualitative data [12]	Identifies recurring themes across different stakeholder perspectives

Procedure

This protocol employs four complementary evaluation approaches simultaneously [12]:

• Developmental Evaluation: Embed evaluators within the implementation team to provide real-time feedback and support adaptive management. This approach helps stakeholders navigate complexity by continuously refining strategies based on emerging outcomes.
• Principles-Focused Evaluation: Assess how effectively the implementation effort adheres to its guiding principles. For example, the Implementation Science Center for Cancer Control Equity used the 9 Principles of Community Engagement to guide and evaluate their work [13].
• Network Analysis: Map formal and informal relationships among stakeholders at multiple time points to document changes in collaboration patterns, knowledge exchange, and resource sharing throughout the implementation process.
• Framework Analysis: Systematically analyze qualitative data from interviews, focus groups, and documents using a thematic framework to identify convergent and divergent themes across different stakeholder groups.

Implementation Outcomes and Pragmatic Measures

Evaluating Engagement Success

Systematic measurement of stakeholder engagement is essential for assessing implementation success. The Implementation Science Center for Cancer Control Equity operationalized the 9 Principles of Community Engagement and developed corresponding survey questions to evaluate their partnership with Community Health Centers (CHCs) [13]. Of 38 respondents (64.4% response rate), most perceived their engagement positively, with over 92% feeling respected by academic collaborators and perceiving projects as beneficial [13]. This systematic approach to measuring engagement quality provides a model for evaluating the operationalization of community engagement principles in implementation research.

Developing Pragmatic Measures for Implementation Research

The development of psychometrically and pragmatically strong measures is critical for advancing implementation science. The Psychometric and Pragmatic Evidence Rating Scale (PAPERS) was developed through stakeholder-driven research to evaluate implementation measures across four key categories [14]:

• Useful: Produces reliable and valid results, informs clinical or organizational decision-making
• Compatible: Applicable, fits organizational activities
• Acceptable: Creates low social desirability bias, relevant, offers relative advantage, acceptable to staff and clients, low cost
• Easy: Uses accessible language, efficient, feasible, easy to interpret, creates low assessor burden, items not wordy, completed with ease, brief [14]

This framework emphasizes that for implementation measures to be used in real-world settings, they must not only be psychometrically sound but also practical and acceptable to diverse stakeholders, including those with lived experience of the implementation context.

Application in Complex Healthcare Settings

Implementation outcome measurement presents particular challenges in complex healthcare settings like Paediatric Intensive Care Units (PICU), where validated instruments are scarce [15]. A systematic review of implementation outcome measures found that most instruments had limited evidence of validity or reliability and demonstrated poor psychometric properties [15]. This measurement gap highlights the urgent need for pragmatic measures that can capture implementation outcomes across diverse contexts while accommodating the pluralistic needs of various stakeholders. Engaging stakeholders in the development and validation of these measures is essential for ensuring their utility in complex real-world settings.

Application Notes: A Framework for Pragmatic Measures

This document provides application notes and protocols for developing pragmatic measures in implementation science research. The framework is designed to help researchers bridge the gap between abstract theoretical constructs and concrete, stakeholder-valued outcomes, facilitating the systematic evaluation of implementation strategies.

Core Construct Operationalization Table

The following table summarizes the core constructs, their operational definitions, and corresponding quantitative metrics for assessing implementation outcomes. [16] [17]

Core Construct	Operational Definition	Quantitative Metric(s)	Data Collection Method
Feasibility	The extent to which an implementation strategy can be successfully used or carried out within a given setting.	Percentage of protocol components executed as planned; Provider-reported ease-of-use scale (1-5).	Facilitated session notes; Post-implementation survey.
Adoption	The intention, initial decision, or action to try or employ an innovation or implementation strategy.	Rate of uptake (proportion of clinicians using the strategy); Time to initial adoption.	Administrative data; Key informant interviews.
Fidelity	The degree to which an implementation strategy was implemented as defined in the original protocol.	Adherence score (% of core components delivered); Competence rating (independent assessor, 1-7 scale).	Direct observation; Session audio recording review.
Implementation Cost	The financial impact of the implementation effort from the health system perspective.	Total cost; Cost per patient reached; Incremental cost-effectiveness ratio (ICER).	Time-motion studies; Micro-costing from administrative records.
Penetration	The integration of a practice within a service setting and its subsystems.	Proportion of eligible settings using the strategy; Proportion of eligible patients receiving the innovation.	Organizational report; Patient-level administrative data.
Sustainability	The extent to which a newly implemented treatment is maintained or institutionalized within a service setting’s ongoing, stable operations.	Continuation of service delivery 12+ months post-implementation; Level of institutional funding support.	Longitudinal follow-up survey; Budget analysis.

The Scientist's Toolkit: Research Reagent Solutions

Essential materials and tools for conducting implementation science research on pragmatic measures. [16] [17]

Item	Function in Research
Standard Protocol Template (SPIRIT 2025)	Provides a structured checklist of 34 minimum items to ensure trial protocol completeness, covering planning, conduct, and reporting. [16]
Implementation Outcomes Kit (Proctor Model)	A conceptual framework defining eight key implementation outcomes (acceptability, adoption, etc.) to guide measurement selection.
Stakeholder Engagement Matrix	A tool to map key stakeholders (patients, providers, policymakers) and plan their involvement in design, conduct, and reporting. [16]
Data Visualization Software (e.g., Tableau)	Enables analysis of structured data (rows and columns) to understand aggregation, granularity, and distributions for key metrics. [18]
Qualitative Data Analysis Software (e.g., NVivo)	Facilitates the coding and thematic analysis of interview and focus group data to contextualize quantitative findings.

Experimental Protocols

Protocol for Measuring Implementation Fidelity

Objective: To quantitatively assess the degree to which an evidence-based practice is delivered as originally prescribed.

Background: Fidelity measurement is critical for distinguishing between ineffective interventions and ineffective implementation. [16]

Materials:

• Fidelity checklist (core components)
• Audio recording equipment
• Independent rater guide
• Data collection template

Methodology:

• Component Definition: Prior to implementation, explicitly define the core, non-adaptable components of the intervention being implemented.
• Tool Development: Create a structured fidelity checklist with each core component rated on a dichotomous (Yes/No) or Likert scale (e.g., 1=Not delivered, 5=Fully delivered).
• Rater Training: Train independent raters using the guide. Establish inter-rater reliability (Kappa >0.8) using practice recordings.
• Data Collection: For a random sample of sessions (e.g., 20%), audio-record the delivery of the intervention.
• Rating: Raters, blinded to study hypotheses, review recordings and complete the fidelity checklist.
• Data Analysis: Calculate an overall fidelity score (e.g., percentage of components delivered as intended) and analyze variance across providers or sites.

Protocol for Stakeholder Engagement in Measure Development

Objective: To involve patients and the public in the design and validation of pragmatic outcome measures.

Background: Patient and public involvement ensures that developed measures are relevant and meaningful to end-users. [16]

Materials:

• Participant recruitment materials
• Facilitator guide for engagement panels
• Digital voice recorder and transcription service
• Thematic analysis software

Methodology:

• Stakeholder Identification: Recruit a diverse panel of 8-10 stakeholders (e.g., patients, clinicians, healthcare administrators).
• Structured Engagement Sessions: Conduct a series of facilitated sessions.
  • Session 1: Present draft measures and constructs for discussion on clarity and relevance.
  • Session 2: Review revised measures and prioritize outcomes from a stakeholder perspective.
• Data Synthesis: Thematically analyze session transcripts and feedback notes.
• Measure Refinement: Iteratively revise the pragmatic measures based on stakeholder feedback to enhance face validity and contextual fit.
• Feedback Loop: Report back to the stakeholder panel on how their input was integrated.

Visualizing the Research Workflow

Implementation Measure Dev Flow

Fidelity Assessment Pathway

Application Notes: Context and Quantitative Landscape

This document outlines the application of implementation science principles to develop a pragmatic framework for campus sexual violence interventions. The approach emphasizes stakeholder engagement and adaptive strategies to bridge the gap between research and real-world application, addressing a critical public health issue.

Quantitative Baseline: Prevalence and Reporting Gaps

Current data reveals significant disparities between sexual violence prevalence and official reporting rates, highlighting a critical implementation gap. The following table summarizes key quantitative findings from recent campus surveys.

Table 1: Quantitative Data on Campus Sexual Violence and Reporting (2024-2025)

Metric	UVA Findings	Harvard Findings	General Population Notes
Sexual Harassment (Undergraduate Women)	26% (down from 29% in 2019) [19]	Reported decline (specific % not detailed) [19]	Consistent with national trends
Sexual Harassment (Graduate Women)	13.7% (down from 22.8% in 2019) [19]	Reported decline (specific % not detailed) [19]
Harassment in Non-binary/Transgender Students	29.3% (Graduate) [19]	Elevated rates acknowledged [19]	47.1% in a 10-university consortium [19]
Student Awareness of Reporting Procedures	64% [19]	Majority aware, but reporting rates low [19]	Indicates a knowledge-to-action gap
Incidents Formally Reported by Victims	<30% [19]	Minority reported [19]	Major barrier to intervention and support
Primary Reasons for Non-Reporting	Fear of retaliation, distrust of institutional response, belief that reporting is futile [19]	Similar trust and efficacy concerns [19]	Points to systemic implementation failures

Core Implementation Challenges and Strategic Insights

• Marginalized Groups: Non-binary, transgender, and other marginalized students experience disproportionately high rates of sexual harassment, necessitating tailored, culturally sensitive intervention strategies [19].
• The Bystander Intervention Gap: While training exists, a gap remains between bystander awareness and their willingness to act, indicating a need for more effective implementation of intervention programs [19].
• Legislative and Policy Environment: Evolving legal frameworks, such as Title IX revisions, create a shifting landscape that interventions must adapt to, requiring flexible and responsive implementation protocols [19].

Experimental Protocols

Protocol: Co-Design Workshop for Intervention Adaptation

Objective: To collaboratively adapt evidence-based interventions (EBIs) to fit the specific cultural, social, and structural context of a university campus, leveraging stakeholder input.

Background: Standardized interventions often fail due to a lack of contextual fit. This protocol facilitates a participatory process with key campus groups to enhance acceptability and feasibility [19].

Materials:

• Pre-workshop survey (to gauge prior knowledge and perceptions)
• Anonymous polling software (for real-time feedback)
• Digital whiteboarding tool
• Draft of the EBI to be adapted

Procedure:

• Stakeholder Recruitment (Week 1-2): Recruit a diverse group of 20-30 participants, including:
  • Undergraduate and graduate students (with oversampling from marginalized groups).
  • Clinical and counseling staff from student health services.
  • Title IX office representatives.
  • Faculty and residential life staff.
• Pre-Work (Week 3): Distribute background materials on the selected EBI and the pre-workshop survey to establish baseline understanding.
• Workshop Execution (4 hours):
  • Introduction (30 min): Overview of goals, the EBI's evidence base, and ground rules for a safe, respectful discussion.
  • Barriers & Facilitators Brainstorm (60 min): Use prompted discussions to identify potential obstacles and supporting factors for the EBI's implementation on this specific campus. Record responses on the digital whiteboard.
  • Adaptation Sprint (90 min): In small, mixed-stakeholder groups, focus on modifying specific EBI components (e.g., messaging, delivery mode, training content) to address the identified barriers.
  • Synthesis & Voting (60 min): Groups present their top adaptation proposals. Using anonymous polling, all participants vote to prioritize the most critical adaptations for pilot testing.

Outputs:

• A prioritized list of stakeholder-generated adaptations.
• Documented rationale for each adaptation, linked to specific contextual barriers.

Protocol: Stepped-Wedge Trial to Evaluate Implementation

Objective: To evaluate the real-world effectiveness and implementation outcomes of the co-designed intervention across multiple campuses.

Background: A stepped-wedge cluster randomized trial (SW-CRT) design allows all participating sites to eventually receive the intervention, which is often ethically and logistically preferable in campus settings [19].

Materials:

• Validated measurement scales (see Reagent Solutions).
• A standardized implementation toolkit for the adapted intervention.
• Training materials for campus facilitators.
• Data management platform for centralized data collection.

Procedure:

• Site & Participant Recruitment:
  • Recruit 6-8 diverse university campuses as clusters.
  • Within each campus, recruit a cohort of students through random sampling from the registrar's list to participate in longitudinal surveys.
• Baseline Period (2 months): Administer the first survey to all cohorts across all campuses to measure baseline rates of awareness, attitudes, and reporting intentions.
• Randomization & Staging:
  • Randomly assign campuses to the order in which they will receive the intervention.
  • Define 4 intervention waves, each 3 months apart.
• Intervention Roll-out:
  • Wave 0 (Control): All campuses continue with existing practices.
  • Wave 1: The first set of campuses implements the co-designed intervention.
  • Wave 2: The next set of campuses implements the intervention, and so on, until all campuses have implemented it.
• Data Collection:
  • Administer the same survey to all participant cohorts at the end of each wave.
  • Collect quantitative implementation data (e.g., training participation rates, resource utilization) from each campus.

Analysis:

• Compare changes in primary outcomes (e.g., reporting rates, bystander efficacy) within each campus before and after implementation.
• Analyze between-campus data to assess the consistency of effects.

Visualization of the Implementation Framework

The following diagram illustrates the multi-level, iterative logic of the stakeholder-informed implementation framework.

The Scientist's Toolkit: Research Reagent Solutions

This section details key "reagents"—the essential measurement tools and materials—required for rigorous implementation research in this field.

Table 2: Essential Research Reagents for Implementation Science in Campus Sexual Violence

Research Reagent	Type / Format	Primary Function in Research
Campus Climate Survey	Validated Quantitative Instrument	To establish baseline and follow-up measures of prevalence, awareness, attitudes, and reporting intentions across the student population [19].
Implementation Fidelity Checklist	Observer-Rated Protocol	To ensure the adapted intervention is delivered as intended across different campuses and facilitators, measuring adherence to the core protocol.
Stakeholder Engagement Assessment	Mixed-Methods Survey & Interview Guide	To evaluate the process and quality of stakeholder collaboration, assessing factors like representativeness, influence, and satisfaction.
Title IX Policy Database	Systematic Document Coding Framework	To track and analyze variations in institutional policies and their alignment with federal guidelines, enabling analysis of policy impact [19].
Bystander Efficacy Scale	Validated Psychometric Scale	To measure changes in participants' confidence and intention to intervene safely in situations perceived as high-risk for sexual violence [19].
Resource Utilization Log	Standardized Tracking Sheet	To quantitatively monitor the cost and consumption of support resources (e.g., counseling sessions, advocacy hours), informing economic and feasibility analyses [19].

From Theory to Practice: Methodological Frameworks for Developing and Applying Pragmatic Measures

Leveraging Case Study Research for Deep, Contextual Understanding of Implementation

Case study research is an indispensable methodology for achieving a deep, contextual understanding of implementation processes in complex, real-world settings. This approach allows for in-depth, multi-faceted explorations of complex issues precisely within their natural contexts, making it particularly valuable for investigating the "how" and "why" behind implementation successes and failures [20]. Within the broader thesis of developing pragmatic measures for implementation science, case studies provide the rich, contextual data necessary to ensure that these measures are not only scientifically valid but also practically applicable across diverse settings, including the specialized field of drug development.

The fundamental value of the case study approach lies in its ability to capture the complex interplay between interventions, their implementation contexts, and the resulting outcomes. As noted in methodological literature, case studies are particularly useful "to investigate contemporary phenomena within its real-life context," especially "when the boundaries between phenomenon and context are not clearly evident" [20]. This characteristic makes case studies exceptionally well-suited for implementation science, where the context is often inseparable from the implementation success itself. For drug development professionals and researchers, this methodology offers a structured approach to understanding why certain interventions thrive in specific environments while others fail, thereby informing more effective implementation strategies.

Theoretical Foundations and Methodological Considerations

Epistemological Alignment and Research Questions

Case study research aligns with several epistemological traditions, including constructivist paradigms that emphasize multiple realities and interpretive approaches that seek to understand phenomena through the meanings people assign to them. This methodological flexibility allows researchers to tailor their approach based on the specific implementation questions being investigated. The case study approach is particularly appropriate for addressing specific types of research questions, including those that seek to explore complex interventions where the pathways from intervention to effects are not straightforward, and those that investigate implementation contexts where the intervention and context are intrinsically linked [20].

When designing case study research for implementation science, several key considerations emerge. First, researchers must clearly define the "case" itself—whether it be a specific implementation project, an organizational process, or a particular intervention rollout. Second, the unit of analysis must be carefully specified, as this determines the boundaries of data collection and analysis. Third, the theoretical underpinnings should guide the design, selection, conduct, and interpretation of case studies to ensure methodological rigor [20]. These considerations are essential for producing findings that contribute meaningfully to developing pragmatic measures that are both scientifically sound and practically applicable.

Comparative Case Study Designs

Multiple-case designs are particularly valuable in implementation science as they allow for comparisons across different contexts, revealing both common and unique factors influencing implementation. For instance, a study examining workforce reconfiguration in respiratory services employed a multiple-case design across four Primary Care Organizations, enabling researchers to identify how local contexts influenced the implementation process [20]. Similarly, research on campus sexual violence interventions developed an adapted implementation science framework through four case studies from the United States, South Africa, and Eswatini, revealing cross-cutting issues unique to this implementation context [21].

These comparative approaches allow researchers to distinguish between context-specific factors and those that transcend settings, a crucial consideration when developing pragmatic measures intended for broad application. The replication logic—where each case is selected to predict similar results (literal replication) or produce contrasting results for predictable reasons (theoretical replication)—strengthens the theoretical foundations of implementation science and contributes to more robust, context-sensitive measures [20].

Application Notes: Implementing Case Study Methodology

Data Collection and Integration Strategies

Case study research in implementation science typically employs multiple data sources to develop a comprehensive understanding of the phenomenon under investigation. Common data collection methods include semi-structured interviews, document analysis, field observations, and increasingly, quantitative metrics that complement qualitative insights [20]. This methodological triangulation enhances the validity of findings by providing multiple perspectives on implementation processes.

For example, a mixed methods, longitudinal, multi-site case study of electronic health record implementation in England's NHS collected data through "semi-structured interviews, documentary data and field notes, observations and quantitative data" [20]. This comprehensive approach allowed researchers to capture both the technical and social aspects of implementation across different hospital contexts. Similarly, research on patient safety education employed a multi-site, mixed method collective case study across eight educational sites, collecting data through "documentary evidence, complemented with a range of views and observations" across different contexts [20].

Table 1: Data Sources for Case Study Research in Implementation Science

Data Source	Application in Implementation Science	Considerations for Pragmatic Measures
Semi-structured interviews	Capture stakeholder experiences, barriers, and facilitators	Ensure interview guides align with implementation outcomes of interest
Documentary analysis	Review implementation plans, meeting minutes, policies	Provides insight into formal vs. informal implementation processes
Field observations	Witness implementation in real-time	Captures behaviors and interactions that may not be reported in interviews
Quantitative metrics	Track implementation reach, fidelity, and outcomes	Enables mixed-method analysis and pattern identification

Analytical Approaches and Framework Development

The analysis of case study data in implementation science often employs both deductive and inductive approaches, frequently guided by established implementation frameworks while remaining open to emergent themes. For instance, the Consolidated Framework for Implementation Research (CFIR) provides a comprehensive "overarching typology to promote implementation theory development and verification about what works where and why across multiple contexts" [22]. This framework identifies five major domains—intervention characteristics, outer context, inner context, characteristics of individuals, and process—that guide the consideration and assessment of factors which might impact implementation.

Analysis frequently involves coding data according to predetermined frameworks while allowing for emergent themes. For example, one study analyzed qualitative data "thematically using a socio-technical coding matrix, combined with additional themes that emerged from the data" [20]. This balanced approach ensures that analysis captures both anticipated and unanticipated insights about implementation processes, contributing to more comprehensive pragmatic measures.

Experimental Protocols for Case Study Research

Protocol 1: Multi-site Implementation Case Study

Purpose: To identify contextual factors influencing implementation success across multiple sites and develop context-sensitive pragmatic measures.

Methodology:

• Case Selection: Employ theoretical sampling to identify cases that represent variation in implementation contexts, settings, or populations. The ISAC Match process recommends "reviewing available information on EBI integration and conducting contextual inquiry, if needed, to understand barriers and facilitators" as a first step [23].
• Data Collection: Conduct semi-structured interviews with key stakeholders (implementers, administrators, recipients) using interview guides informed by implementation frameworks. Supplement with document review and observational data where feasible.
• Analysis: Employ cross-case comparative analysis using framework analysis techniques. Code data according to established implementation frameworks while documenting emergent themes.
• Interpretation: Identify patterns of implementation barriers and facilitators across cases, noting both consistent and context-specific factors.

Adaptation for Drug Development Contexts: In pharmaceutical settings, this protocol can be adapted to study implementation of new research methodologies, quality initiatives, or regulatory processes across different research sites, therapeutic areas, or geographic locations.

Protocol 2: Longitudinal Implementation Process Study

Purpose: To document and analyze implementation processes over time, capturing adaptations and evolving contextual influences.

Methodology:

• Baseline Assessment: Conduct initial assessment of implementation context using structured assessment tools based on implementation frameworks.
• Ongoing Data Collection: Implement regular data collection intervals (e.g., quarterly) through brief surveys, targeted interviews, and document review.
• Process Documentation: Systematically document implementation decisions, adaptations, and contextual changes throughout the study period.
• Analysis: Employ temporal analysis techniques to identify sequences, pathways, and critical junctures in implementation processes.

This approach aligns with the "longitudinal, multi-site, socio-technical collective case study" employed in research on electronic health record implementation [20], which tracked implementation efforts over time to understand evolving challenges and adaptations.

Case Study Applications in Implementation Science

Framework Adaptation and Development

Case study research has proven particularly valuable for adapting and developing implementation frameworks to address specific contexts or content areas. For instance, research on campus sexual violence interventions used a multiple case study approach to identify "multiple cross-cutting issues unique to the IS of campus sexual violence interventions: policy and legal framework, team praxis, relationships, context, infrastructure, and people" [21]. These insights led to the development of "an adapted CFIR framework... from a cross-national set of case studies" that better addressed the unique needs of this implementation context [21].

Similarly, the ISAC Match process was developed through case study work to provide "expanded guidance and potential approaches" for selecting and tailoring implementation strategies in community settings [23]. This process includes four steps: "1) reviewing available information on EBI integration and conducting contextual inquiry, if needed, to understand barriers and facilitators; 2) identifying existing implementation strategies used in the practice setting, 3) using recommended guidance tools to select relevant implementation strategies to overcome barriers and capitalize on facilitators; and 4) tailoring strategies to fit within the setting" [23].

Implementation Strategy Selection and Tailoring

Case studies provide invaluable insights for selecting and tailoring implementation strategies to address context-specific barriers and leverage facilitators. The ISAC Match process, developed specifically for community settings, employs a "strength-based approach (i.e., considering both barriers and facilitators) in the decision-making process" [23]. This approach recognizes that effective implementation requires not only addressing barriers but also capitalizing on existing strengths and facilitators.

Table 2: Implementation Outcomes for Case Study Evaluation

Implementation Outcome	Definition	Assessment Approach
Acceptability	Perception among stakeholders that an intervention is agreeable	Interviews, surveys assessing satisfaction and comfort
Adoption	Intention or initial decision to employ an intervention	Documentation of uptake, interviews regarding adoption decisions
Appropriateness	Perceived fit or relevance of an intervention for a given setting	Interviews assessing perceived relevance and fit
Fidelity	Degree to which an intervention is implemented as intended	Observational measures, self-report checklists
Coverage	Reach of the intervention within the target population	Utilization data, participation records
Sustainability	Extent to which an intervention is maintained or institutionalized	Long-term follow-up, organizational integration assessment

Implementation scientists conducting case study research benefit from a structured set of conceptual and methodological tools. These resources ensure methodological rigor while maintaining the flexibility needed to capture rich, contextual insights about implementation processes.

Table 3: Research Reagent Solutions for Implementation Case Studies

Tool Category	Specific Tools/Resources	Function in Case Study Research
Conceptual Frameworks	CFIR, TDF, RE-AIM, ISAC	Provide theoretical grounding and guide data collection and analysis
Data Collection Tools	Semi-structured interview guides, observation protocols, document abstraction tools	Standardize data collection while allowing emergence of context-specific insights
Analytical Tools	Framework analysis guides, qualitative coding software, pattern-matching templates	Support systematic analysis of complex, multi-source data
Reporting Guidelines	CASE, COREO, SCRIB	Enhance transparency and completeness of case study reporting

The "Implementation research toolkit" developed by TDR provides additional resources specifically designed for implementation research, including modules on "Understanding IR, Integrating IR into the health system, IR-related communications and advocacy and Intersectional gender lens" [24]. These resources support researchers in conducting rigorous, ethically sound case study research that generates actionable insights for improving implementation in real-world settings.

Case study research offers implementation scientists a powerful methodology for developing the deep, contextual understanding necessary to create pragmatic measures that resonate with real-world complexities. By systematically studying implementation phenomena within their natural contexts, researchers can identify the nuanced factors that determine success or failure, document the adaptations that make interventions workable in diverse settings, and develop frameworks that genuinely support effective implementation.

For drug development professionals and implementation researchers, the rigorous application of case study methods provides an evidence base for improving implementation processes, selecting and tailoring implementation strategies, and ultimately enhancing the impact of evidence-based interventions across diverse contexts. As implementation science continues to evolve, case study research will remain an essential approach for ensuring that our implementation theories, frameworks, and measures maintain their relevance and utility in addressing the complex challenges of real-world implementation.

The Implementation Strategies Applied in Communities Match process (ISAC Match) is a pragmatic, systematic approach designed to address a critical gap in implementation science: the selection and tailoring of implementation strategies for community (non-clinical) settings [23]. Implementation strategies are defined as methods or techniques to improve the adoption, implementation, sustainment, and scale-up of evidence-based interventions (EBIs) [23] [25]. The ISAC Match process was developed in response to the limitations of existing compilations and matching processes, such as the Expert Recommendations for Implementing Change (ERIC), which were developed in clinical settings and often use clinical terminology that is difficult to apply in community contexts [23] [25]. This process provides a structured yet flexible framework for researchers and practitioners working in integrated research-practice partnerships to identify and adapt strategies that overcome implementation barriers and capitalize on facilitators, with explicit consideration of health equity to ensure strategies narrow rather than widen existing health disparities [23].

The ISAC Match process is designed to be applied within integrated research-practice partnerships (IRPPs) or similar collaborative models that equally value researcher and practitioner contributions [23]. Before initiating the process, participants must have identified a specific evidence-based intervention for integration and possess the organizational authority to influence its implementation [23]. The process unfolds through four sequential but iterative steps, each requiring collaborative engagement between research and practice partners to ensure selected strategies are both evidence-informed and contextually appropriate.

The following workflow diagram visualizes the core ISAC Match process and its relationship to the broader implementation cycle:

Detailed Experimental Protocols

Step 1: Contextual Inquiry Protocol

Objective: To understand implementation barriers and facilitators through rapid assessment methods.

Materials Needed: Existing literature on EBI integration, interview/focus group guides, recording equipment, qualitative analysis software (optional), prioritization tools (e.g., card sort materials, 2x2 grid poster board).

Procedure:

• Review Existing Evidence: Compile and synthesize available information on the integration of your target EBI from both peer-reviewed and gray literature [23] [26].
• Assess Need for Additional Inquiry: Determine if existing evidence sufficiently characterizes barriers and facilitators for your specific context and population. If gaps exist, proceed with primary data collection [23].
• Rapid Data Collection: Employ rapid qualitative methods, such as:
  • Rapid Deductive Qualitative Analysis: Conduct focused interviews or focus groups using determinant frameworks like the Consolidated Framework for Implementation Research (CFIR) to structure data collection [23].
  • Brainwriting Premortem: Facilitate a structured session where stakeholders brainstorm potential reasons for implementation failure, framed using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework [23].
• Barrier/Facilitator Prioritization: Use collaborative techniques to prioritize identified factors:
  • Card Sort Activity: Write each barrier on a card and have stakeholders sort them into low, medium, and high priority based on impact and addressability [23].
  • Modified Conjoint Analysis: Rate barriers on a 2x2 grid based on "importance" and "changeability" to identify high-priority, addressable targets [23].

Output: A prioritized list of implementation barriers and facilitators to inform strategy selection.

Step 2: Identification of Existing Strategies Protocol

Objective: To document implementation strategies already in use within the organization.

Materials Needed: Organizational documents (program guides, implementation blueprints), meeting space, recording equipment.

Procedure:

• Document Review: Systematically examine existing organizational materials to identify supports that function as implementation strategies, even if not labeled as such (e.g., program guides, technical assistance protocols) [23].
• Facilitated Discussion: Conduct a structured meeting with implementers and administrators:
  • Prompt participants to reflect on past challenges in adopting, implementing, or maintaining EBIs.
  • Ask them to describe organizational supports or resources that helped overcome these challenges.
  • Document these supports and map them to the ISAC compilation where possible [23].
• Strategy Inventory: Create a comprehensive inventory of existing implementation strategies, noting their intended functions and current effectiveness.

Output: An inventory of existing implementation strategies and organizational supports.

Step 3: Strategy Selection Protocol

Objective: To select new implementation strategies from the ISAC compilation to address prioritized barriers and leverage facilitators.

Materials Needed: ISAC guidance tools (Barrier-Level Tool and RE-AIM Framework Tool), ISAC compilation list, prioritized barriers/facilitators from Step 1.

Procedure:

• Tool Selection: Choose the appropriate ISAC guidance tool based on your needs:
  • Barrier-Level Tool: Use when matching strategies to barriers at different levels of influence (individual, innovation, inner setting, outer setting, implementation process) [26].
  • RE-AIM Framework Tool: Use when targeting challenges related to specific implementation outcomes (Reach, Effectiveness, Adoption, Implementation, Maintenance) [26].
• Strategy Matching: For each prioritized barrier, use the selected tool to identify potentially relevant implementation strategies from the ISAC compilation.
• Preliminary Screening: Review potential strategies with the IRPP to assess initial feasibility, resource requirements, and potential complementarity with existing strategies identified in Step 2.

Output: A long list of potential new implementation strategies matched to prioritized barriers.

Step 4: Strategy Tailoring Protocol

Objective: To adapt selected implementation strategies to fit the local context.

Materials Needed: List of selected strategies from Step 3, tailoring worksheet.

Procedure:

• Specification: For each strategy, define the core components that must be preserved and the adaptable components that can be modified [23].
• Contextual Fit Assessment: For each strategy, discuss and document:
  • Who will oversee and deliver the strategy
  • What personnel and actions the strategy is designed to influence
  • When and how often the strategy will be deployed
  • How the strategy aligns with organizational resources, values, and infrastructure [23] [26]
• Modification: Based on the assessment, make specific adaptations to optimize contextual fit while preserving the strategy's core active ingredients.
• Implementation Plan Development: Create a detailed plan specifying roles, timelines, resources, and monitoring procedures for each tailored strategy.

Output: A set of fully specified, tailored implementation strategies with a detailed implementation plan.

Quantitative Data & Analysis

The development of the ISAC compilation and matching process was informed by qualitative research with 18 researchers and practitioners across diverse community settings. The following table summarizes the most frequently mentioned implementation strategies identified through this research, providing insight into strategies commonly employed in community settings.

Table 1: Frequently Mentioned Implementation Strategies in Community Settings

Implementation Strategy	Frequency Mentioned (n=18)	Primary RE-AIM Dimensions Addressed
Conduct Pragmatic Evaluation	31	Implementation, Maintenance
Provide Training	26	Adoption, Implementation
Change Adaptable Program Components	26	Implementation, Effectiveness
Leverage Funding Sources	21	Adoption, Maintenance
Develop Implementation Blueprints	19	Implementation, Maintenance
Tailor Strategies for Priority Populations	18	Reach, Effectiveness
Build Community Coalitions	17	Adoption, Reach
Provide Technical Assistance	16	Implementation, Maintenance
Facilitate Shared Learning	15	Adoption, Implementation
Create Program Guides	14	Implementation, Maintenance

Source: Adapted from Balis et al. (2024), International Journal of Behavioral Nutrition and Physical Activity [25]

The Scientist's Toolkit

Table 2: Essential Research Reagents for ISAC Match Application

Research Reagent / Tool	Function / Application in ISAC Match
ISAC Compilation	A comprehensive list of 40 implementation strategies specifically used in community settings, with definitions and examples for each [26] [25].
Barrier-Level Guidance Tool	Enables matching of implementation strategies to barriers at different levels of influence: individual, innovation, inner setting, outer setting/external environment, and implementation process [26].
RE-AIM Framework Tool	Facilitates selection of implementation strategies based on challenges related to specific RE-AIM framework dimensions: Reach, Effectiveness, Adoption, Implementation, and Maintenance [26].
CFIR Interview Guide	A structured guide based on the Consolidated Framework for Implementation Research to systematically assess implementation determinants during contextual inquiry [23].
Card Sort Materials	Physical or digital cards used for prioritizing barriers and strategies through collaborative sorting activities with stakeholders [23].
Implementation Blueprints	Templates for creating detailed specifications of how evidence-based interventions should be implemented, including core components and adaptable elements [25].
Pragmatic Evaluation Tools	Simplified measurement instruments designed to assess implementation outcomes without creating excessive burden in resource-constrained community settings [25].

Applications and Case Study

The utility of ISAC Match is demonstrated in a case study involving Montana State University Extension Agents, who sought to increase adoption of built environment approaches to facilitate physical activity [23] [27]. The process was applied within an integrated research-practice partnership that included both researchers and extension professionals. Through the four-step process, the partnership identified key barriers including limited technical expertise in built environment strategies, competing demands on agent time, and varying community resources across implementation sites [23]. Existing strategies were documented, including standard training sessions and program guides. Using ISAC guidance tools, the partnership selected additional strategies including "develop implementation blueprints," "provide technical assistance," and "facilitate shared learning" [23]. These strategies were then tailored to fit the extension context by developing role-specific implementation resources, creating a peer-mentoring program between experienced and novice agents, and establishing a community of practice for ongoing support [23]. This case exemplifies how ISAC Match provides a structured yet flexible approach to addressing implementation challenges in community settings.

In implementation science, the deliberate alteration of evidence-based interventions (EBIs) and implementation strategies is often necessary to improve their fit and effectiveness in new contexts [28]. However, ad-hoc modifications pose significant challenges for reproducibility, scientific rigor, and understanding the mechanisms of implementation success. The Framework for Reporting Adaptations and Modifications-Enhanced (FRAME) and the Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies (FRAME-IS) address this gap by providing systematic approaches for documenting modifications [29] [28]. These frameworks are particularly valuable for developing pragmatic measures in implementation research, offering structured methodologies to capture the complex reality of adaptation while maintaining scientific rigor.

The FRAME was initially developed to track modifications to clinical and psychosocial interventions, while the FRAME-IS extends this structure to document changes to the implementation strategies themselves—the methods and techniques used to adopt, implement, and sustain EBIs in routine practice [29]. This distinction is critical because implementation strategies range from relatively "light touches" (e.g., audit and feedback) to more intensive, multicomponent strategies that may act on multiple levels of a health system [29]. Documenting adaptations to both the intervention and implementation strategy provides a comprehensive understanding of how and why changes occur throughout the implementation process.

Framework Components and Structures

Core Architecture of FRAME and FRAME-IS

Both FRAME and FRAME-IS employ a modular architecture that combines core (required) and supplementary (optional) components to balance comprehensiveness with practical utility across diverse implementation projects [29] [30]. This structure allows researchers to document essential elements while providing flexibility to capture context-specific details relevant to their particular study aims and resources.

The FRAME-IS consists of seven modules that guide users in characterizing various aspects of modifications [29] [30]. Core modules capture fundamental information including a brief description of the EBP, implementation strategy, and modifications (Module 1); what is modified (Module 2); the nature of the modification (Module 3); and the rationale for the modification (Module 4) [29]. Supplementary modules document when the modification occurred and whether it was planned (Module 5); who participated in the decision to modify (Module 6); and how widespread the modification is (Module 7) [29]. This systematic approach ensures consistent documentation across studies and settings, enabling comparative analysis of adaptation patterns.

Table: Core and Supplementary Modules in FRAME-IS

Module Type	Module Name	Key Elements Documented	Item Count
Core	Module 1: Brief Description	EBP, implementation strategy, modifications	4 items
Core	Module 2: What is Modified?	Specific components or elements changed	9 items
Core	Module 3: Nature of Modification	Content, context, or training changes	18 items
Core	Module 4: Rationale for Modification	Reasons and goals for adaptation	15 items
Supplementary	Module 5: Timing and Planning	When modification occurs, planned/unplanned	9 items
Supplementary	Module 6: Decision Participants	Stakeholders involved in adaptation decisions	10 items
Supplementary	Module 7: Reach and Scope	How widespread the modification is	9 items

Table: Quantitative Overview of FRAME-IS Instrument

Characteristic	Specification
Total Items	75
Instrument Type	Survey
Data Collection Method	Quantitative
Cost	Free
Expertise Required for Interpretation	Yes
Training Required	Yes
Equity-Relevant Components	Included

Key Adaptation Characteristics for Documentation

When time and resources are limited, or when a large number of adaptations have been made, researchers can focus on seven key aspects of adaptations that provide the most critical information for understanding their potential impact [28]. These include: (1) what specifically was adapted (e.g., which activities or components of the protocol); (2) the focus (e.g., the intervention, implementation strategies, or context); (3) the purpose of the adaptation (e.g., to enhance reach, improve equity, increase fidelity); (4) the timing and sequence of adaptations; (5) whether the adaptation was bundled with other adaptations; (6) the scope and frequency of exposure to adaptations; and (7) whether the adaptation was planned or made in response to emerging events [28].

Documenting whether modifications are fidelity-consistent is particularly important for understanding their relationship to core elements or functions of the original intervention or implementation strategy [29]. This distinction helps researchers and practitioners determine whether adaptations preserve the essential, theoretically-grounded components of an evidence-based approach while modifying more peripheral elements to improve contextual fit.

Application Protocols for Implementation Research

Protocol 1: Prospective Adaptation Tracking in Clinical Trials

Purpose: To systematically document planned and unplanned adaptations to both the intervention and implementation strategies during clinical trials or implementation studies.

Materials Required: FRAME-IS documentation tool [30], data collection platform (electronic or paper-based), stakeholder roster, implementation strategy specification template.

Procedural Steps:

• Pre-Implementation Planning: Before study initiation, convene key stakeholders including researchers, clinical or community partners, and implementation team members. Collaboratively define the core components of both the EBI and implementation strategies to establish what constitutes essential functions versus adaptable forms [28].
• Baseline Documentation: Using Module 1 of FRAME-IS, document the original specifications of the EBI and implementation strategies. Reference established compilations such as the Expert Recommendations for Implementing Change (ERIC) for standardized description of implementation strategies [29].
• Ongoing Monitoring: Establish regular (e.g., weekly or monthly) adaptation tracking meetings. For each adaptation identified, complete core modules of FRAME-IS (Modules 1-4) [29]. Document:
  • Specific components modified (e.g., training content, delivery format, target recipients)
  • Nature of changes (content, context, training/evaluation)
  • Primary goal and rationale (e.g., improve fit, increase reach, address barriers)
  • Relationship to core elements (fidelity-consistent or not)
• Stakeholder Engagement: For significant adaptations, document who participated in decision-making (Module 6) to capture stakeholder perspectives and ensure community engagement [29].
• Temporal Tracking: Record when modifications occur throughout implementation phases (Module 5) and their planned or reactive nature [29].
• Impact Assessment Planning: Link documented adaptations to implementation outcomes using the Model for Adaptation Design and Impact (MADI) or Practical, Robust Implementation and Sustainability Model (PRISM) to create explanatory models for how adaptations may affect outcomes [28].

Quality Control: Cross-verify adaptation documentation through multiple methods including team meeting minutes, stakeholder interviews, and implementation team logs. Conduct periodic audits to ensure consistent application of FRAME-IS categories across team members.

Protocol 2: Retrospective Adaptation Analysis

Purpose: To identify and characterize adaptations that occurred during completed implementation projects through retrospective analysis.

Materials Required: Project documentation (meeting minutes, progress reports, implementation logs), interview/focus group guides, FRAME-IS coding template [30], qualitative data analysis software.

Procedural Steps:

• Data Compilation: Gather all available project documentation including implementation team meeting minutes, progress reports, stakeholder communications, and process evaluation data.
• Key Informant Identification: Identify individuals with comprehensive knowledge of the implementation process including frontline staff, implementation facilitators, and organizational leaders.
• Structured Data Extraction: Systematically review project materials to identify potential adaptations. For each potential adaptation, extract relevant details including:
  • What was modified (specific EBI or implementation strategy components)
  • Timing of modifications relative to implementation timeline
  • Stated or implied reasons for changes
  • Individuals involved in adaptation decisions
• Stakeholder Verification: Conduct semi-structured interviews or focus groups with key informants using the FRAME-IS modules as a structured interview guide to verify and elaborate on adaptations identified in documentation.
• Coding and Categorization: Code all adaptation data using FRAME-IS categories. Two independent coders should initially code a subset of adaptations, with disagreements resolved through consensus discussion or third-party adjudication.
• Pattern Analysis: Analyze coded adaptations to identify patterns in modification types, frequency, timing, and stated rationales. Compare adaptations across different sites or contexts if applicable.
• Outcome Linking: Where outcome data are available, explore potential relationships between adaptation characteristics and implementation outcomes such as fidelity, penetration, and sustainability.

Analytical Considerations: When numerous adaptations are identified, prioritize analysis on those affecting core functions of interventions or implementation strategies rather than peripheral elements [28]. Consider using mixed methods approaches by quantitatively characterizing adaptation frequency and types while qualitatively exploring rationales and decision-making processes [31].

Integration with Implementation Measurement Strategies

Connecting Adaptations to Outcomes

To advance the science of adaptation, documented modifications must be systematically linked to implementation outcomes. The Model for Adaptation Design and Impact (MADI) provides a useful framework for creating explanatory models that connect adaptations to outcomes through hypothesized mechanisms [28]. This approach enables researchers to move beyond simply documenting what changed to understanding how and why adaptations influence implementation success.

When designing studies to assess adaptation impact, researchers should identify both proximal and distal outcomes of adaptations [28]. Proximal outcomes are immediate effects such as changes in acceptability, appropriateness, or feasibility perceptions. Distal outcomes occur later in the implementation process and may include measures of fidelity, penetration, or sustainability. Explicitly specifying the expected pathways from adaptation to outcomes helps focus measurement efforts on the most relevant constructs and timepoints.

Table: Adaptation Impact Assessment Framework

Outcome Category	Example Measures	Typical Timing	Data Collection Methods
Proximal Outcomes	Acceptability of adapted intervention, Perceived appropriateness, Feasibility ratings	Immediate to short-term	Surveys, interviews, focus groups
Implementation Outcomes	Fidelity, Adoption, Penetration, Cost	Short to medium-term	Administrative data, observation, cost tracking
Service Outcomes	Efficiency, Safety, Effectiveness, Equity	Medium to long-term	Service records, clinical data, patient reports
Patient Outcomes	Symptom improvement, Functional status, Quality of life, Satisfaction	Long-term	Clinical assessments, patient-reported outcomes

Methodological Recommendations for Adaptation Studies

Recent methodological recommendations provide guidance for designing rigorous studies to assess the impact of adaptations on implementation outcomes [28]. Four key recommendations include:

• Define the adaptation construct by explicitly operationalizing what constitutes an adaptation in the specific study context and systematically documenting the type, timing, and nature of modifications using structured frameworks like FRAME or FRAME-IS [28].
• Identify expected proximal and distal outcomes of adaptations, considering whether outcomes are intended or unintended, equity-relevant, and their anticipated timing relative to the adaptation [28].
• Select appropriate study designs from the full range of options including descriptive, correlational, and experimental designs, choosing approaches that balance methodological rigor with practical constraints and partner acceptability [28].
• Match analytical approaches to the type of adaptation and outcome data available, the goals of the adaptation study, and the complexity of the study design [28].

These recommendations emphasize that while experimental designs are often regarded as the gold standard, various study designs including descriptive and correlational approaches can provide valuable insights into adaptation impacts, particularly when implemented with careful attention to measurement and causal inference.

Research Reagent Solutions for Adaptation Science

Table: Essential Methodological Tools for Adaptation Research

Research Reagent	Function	Application Context
FRAME-IS Documentation Tool	Standardized instrument for tracking modifications to implementation strategies	Prospective tracking or retrospective analysis of implementation strategy adaptations
Adaptation Tracking Protocol	Step-by-step procedures for identifying and documenting adaptations	Integration into implementation trial protocols or quality improvement initiatives
Stakeholder Engagement Guide	Structured approach for involving diverse perspectives in adaptation decisions	Ensuring community and practitioner input in adaptation processes
Mixed Methods Integration Framework	Approaches for connecting quantitative and qualitative adaptation data	Comprehensive understanding of adaptation patterns and rationales [31]
Implementation Strategy Specification Templates	Tools for explicitly describing implementation strategies before adaptation	Establishing baseline for comparing pre- and post-adaptation strategies [29]
Adaptation-Outcome Linking Matrix	Framework for hypothesizing and testing relationships between adaptations and outcomes	Designing studies to examine adaptation impact on implementation outcomes [28]

The FRAME and FRAME-IS frameworks provide implementation researchers and drug development professionals with systematic approaches for documenting modifications to both interventions and implementation strategies. By applying these structured protocols, researchers can advance the field's understanding of how, when, and why adaptations occur, and their relationship with implementation outcomes. As implementation science continues to develop more pragmatic measures, systematic adaptation tracking will play an increasingly important role in bridging the gap between evidence-based interventions and real-world implementation success.

Integrated Research-Practice Partnerships (IRPPs) represent a transformative approach to implementation science by moving beyond traditional linear translation models toward collaborative systems that integrate scientific evidence with practice-based expertise. These partnerships are defined as long-term collaborations between researchers and practitioners/policymakers designed to improve outcomes through sustained collaboration and mutual commitment to systems-level problem-solving [32]. The fundamental premise of IRPPs is that integrating scientific and community/clinical systems to address scientifically innovative questions with practical implications increases the likelihood of both sustained implementation and generating replicable evidence of generalizability across systems [33].

IRPPs differ fundamentally from traditional pipeline models, which typically follow a sequential efficacy-effectiveness-dissemination pathway. Traditional models often struggle with translation because they [34]:

• Fail to address organizational capacity for implementing evidence-based programs
• Ignore congruence between organizational and program values
• Assume a pro-innovation bias that devalues current organizational practices
• Oversimplify how delivery organizations make adoption decisions

In contrast, IRPPs employ iterative, interactive processes for decision-making that value both research evidence and practitioner expertise, ultimately leading to interventions that are more practical, effective, and sustainable [33] [34].

Theoretical Foundations and Key Principles

Core Propositions of IRPPs

The IRPP framework operates on several foundational propositions that distinguish it from traditional research translation models [33]:

• Integration Proposition: Combining scientific and community/clinical systems addresses both scientific innovation and practical needs, enhancing sustained implementation and evidence generalizability

• Systems Approach Proposition: Sustainable interventions require both vertical (staff to decision-makers) and horizontal (cross-sector) system engagement

• Principles-Focused Proposition: Research synthesis concentrating on evidence-based principles rather than prescribed products achieves wider adoption and higher-quality implementation

• Leverage Proposition: Scale-up and sustainability are more likely when organizational governance, values, resources, and structure are leveraged in design

The Process Model of IRPPs

The IRPP process model represents a collaborative, multi-level systems approach to developing, testing, and sustaining evidence-based principles within real-world settings [33]. This model adapts Rogers' Diffusion of Innovations framework, operationalizing co-production through five iterative stages:

• Knowledge: Exposure to and understanding of innovations during problem prioritization and strategy selection
• Persuasion: Formation of favorable attitudes toward innovations during strategy selection
• Decision: Activities leading to adoption or rejection during strategy adaptation
• Implementation: Putting new ideas to use during integration trials
• Confirmation: Seeking reinforcement of decisions during evaluation and decision-making

Central to this process is the continual emphasis on collaboration among practice professionals, organizational decision-makers, and scientists, with the partnership serving as the decision-making unit [33].

Empirical Evidence and Outcomes

Comparative Effectiveness of IRPPs

Substantial evidence demonstrates the practical advantages of IRPPs over traditional pipeline models. A cluster randomized controlled trial comparing an IRPP-developed physical activity program (FitEx) with an evidence-based program developed through traditional methods (ALED) revealed significant differences in implementation outcomes [34].

Table 1: Comparative Outcomes of IRPP vs. Pipeline-Model Interventions

Outcome Metric	IRPP-Developed Program (FitEx)	Pipeline-Model Program (ALED)	Statistical Significance
Health Educator Adoption	14 of 18 HEs	2 of 18 HEs	χ² = 21.8; p < 0.05
Participant Reach	1,097 total participants	27 total participants	Substantially higher
Delivery Time	Less time required	More time required	p < 0.05
Intent to Continue	Greater intention for continued delivery	Lower intention for continued delivery	p < 0.05
Participant PA Improvement	9.12 ±29.09 min/day increase	Similar increase	Not significant (p > 0.05)

This evidence demonstrates that IRPP-developed programs can significantly improve adoption, implementation, and maintenance while achieving broader reach—without compromising effectiveness [34].

RE-AIM Framework Application

The RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework provides a practical structure for planning and evaluating IRPP initiatives [33] [34]. Within IRPPs, RE-AIM dimensions are pragmatically identified during evaluation and decision-making phases, with target metrics established for each dimension [33]:

• Reach: Engaging 25 or more participants per county as a metric for organizational commitment
• Effectiveness: Significant increases in physical activity compared to matched-contact control
• Adoption: Willingness of health educators to implement the program
• Implementation: Time required for delivery and fidelity to core principles
• Maintenance: Intentions for continued program delivery and institutionalization

This framework enables both researchers and practitioners to easily communicate, measure, and address implementation outcomes in practice settings [33] [34].

Methodological Protocols for IRPP Implementation

Partnership Establishment Protocol

Phase 1: Foundation Building

• Stakeholder Identification: Recruit vertical (organizational leadership to frontline staff) and horizontal (cross-sector) representatives [33]
• Principles of Collaboration: Establish mutual understanding through formal briefing on research expectations, timelines, and fit within research and development processes [35]
• Trust Development: Acknowledge unique knowledge contributions of all partners and ensure genuine commitment to meaningful engagement [35]

Phase 2: Structural Formalization

• Legal and Compliance Agreements: Develop master collaboration agreements covering confidentiality, intellectual property, compensation, and responsibilities [35]
• Timeline Establishment: Set realistic timelines that balance scientific rigor with practical constraints, typically requiring 4-6 months for legal formalization [35]
• Resource Allocation: Dedicate sufficient resources for planning, communication, document development, and meeting coordination [35]

Co-Creation Measurement Protocol

The development of pragmatic measures for assessing co-creation quality involves a structured validation process [36]:

Table 2: Co-Creation Measurement Validation Protocol

Phase	Sample Size	Methodology	Outcomes
Delphi Process	16-20 expert panel members	Group discussions and rating exercises	Construct delineation, content validity assessment
Cognitive Interviews	40 participants	Iterative coding process	Item comprehension and interpretation analysis
Psychometric Validation	300 participants	Confirmatory and exploratory factor analysis	Survey reliability, validity, and pragmatic characteristics

This protocol produces a two-component measure consisting of: (1) an iterative group assessment to prioritize cocreation principles and identify specific activities, and (2) a survey assessing individual partner experience [36].

Integration Trial Protocol

Hybrid Effectiveness-Implementation Design [34]

• Cluster Randomization: Randomize at the health educator or clinic level via computer-generated randomization tables
• Parallel Evaluation: Assess both implementation outcomes (adoption, fidelity, cost) and effectiveness outcomes (participant behavior change)
• Comparative Analysis: Compare IRPP-developed interventions with existing evidence-based programs

Data Collection Methods

• Quantitative Metrics: Adoption rates, reach statistics, fidelity measures, outcome effectiveness
• Qualitative Assessment: Stakeholder perceptions, adaptability feedback, sustainability potential
• Resource Documentation: Time requirements, cost analysis, staffing needs

Implementation Science Toolkit

Essential Research Reagents and Frameworks

Table 3: Essential Resources for IRPP Implementation

Tool Category	Specific Instrument	Function	Application Context
Evaluation Framework	RE-AIM	Planning and evaluating implementation outcomes	Across all partnership phases [33] [34]
Partnership Structure	Vertical and Horizontal Systems Approach	Engaging multiple organizational levels and sectors	Partnership establishment [33]
Implementation Strategy	Multiphase Optimization Strategy (MOST)	Optimizing implementation strategy packages	Preparation, optimization, evaluation phases [37]
Co-creation Measure	Pragmatic Co-creation Measure	Assessing quality of collaborative process	Partnership quality assurance [36]
Trial Design	Hybrid Type 3 Trial	Simultaneously testing effectiveness and implementation	Integration trials [34]

Optimization Approaches for Implementation Strategies

The Multiphase Optimization Strategy (MOST) provides a principled framework for developing, optimizing, and evaluating multicomponent implementation strategies within IRPPs [37]. This approach includes:

Preparation Phase

• Develop conceptual models based on implementation frameworks (e.g., Consolidated Framework for Implementation Research)
• Identify candidate implementation strategies through implementation mapping
• Conduct pilot work to ascertain acceptability and feasibility
• Specify optimization objectives balancing effectiveness with affordability, scalability, and efficiency

Optimization Phase

• Employ factorial experiments to assess individual and combined strategy performance
• Measure resource requirements (cost, time, etc.)
• Identify optimal strategy combinations given implementation constraints

Evaluation Phase

• Conduct randomized controlled trials to evaluate optimized strategy packages
• Assess effectiveness, reach, and sustainability in real-world settings

Visualization of IRPP Workflows

IRPP Process Model

Partnership Structure Diagram

Applications Across Sectors

Healthcare and Pharmaceutical Development

IRPPs have demonstrated significant value in patient-centric drug development, particularly in creating clinical outcome assessment strategies that accurately reflect patient experiences [38] [35]. A notable application involved co-creating clinical outcome assessments for early-stage Parkinson's disease through partnership between a pharmaceutical company (UCB), patient organizations (Parkinson's UK and Parkinson's Foundation), and clinical experts [35].

Key outcomes included:

• Development of patient-reported outcome instruments more relevant to the patient experience
• Enhanced conceptual model development through patient expert input
• Improved protocol design for qualitative studies
• More meaningful measurement tools for clinical trials

This collaboration required considerable resource allocation for planning, communication, and documentation but resulted in outcome assessments that were more holistic and relevant to the patient experience [35].

Public Health and Community Programming

In public health contexts, IRPPs have successfully addressed physical activity promotion through partnerships between university researchers and cooperative extension systems [34]. These partnerships balanced scientific evidence on physical activity promotion with the practical needs and system capabilities of community delivery organizations, resulting in programs with higher adoption, reach, and sustainability compared to traditional evidence-based programs [34].

Future Directions and Implementation Trends

The field of implementation science is evolving toward greater integration of research and practice, with several emerging trends shaping IRPP development [39]:

• Emphasis on Healthcare Access: IRPPs increasingly focus on communities lacking reliable access to health resources, tailoring interventions to address specific access barriers

• Digital Integration: Expanded use of telehealth and digital tools extends the reach of IRPP-developed interventions to remote and underserved populations

• Cross-Sector Collaboration: Growing recognition that complex health challenges require collaborative approaches across public, private, and community sectors

• Pragmatic Trial Methodologies: Movement toward embedded pragmatic clinical trials that assess interventions in real-world settings with broad patient populations [40] [41]

• Implementation Strategy Optimization: Application of optimization frameworks like MOST to develop more efficient and effective implementation strategy packages [37]

These trends highlight the continuing evolution of IRPPs toward more responsive, efficient, and impactful research-practice integration that accelerates the translation of evidence into practice while maintaining scientific rigor.

Contextual inquiry, the process of using in-depth mixed methods to understand implementation contexts, is a critical first step in implementation science for identifying barriers and facilitators to evidence-based practice (EBP) adoption [42]. However, traditional approaches often require one to two years to complete, focus on single settings or EBPs, and frequently duplicate prior efforts, contributing to significant translational lag in bringing interventions to scale [42]. Within the framework of developing pragmatic measures for implementation science research, this application note establishes streamlined protocols for rapid contextual inquiry that balance scientific rigor with speed, enabling more efficient pre-implementation assessment while preserving the relationship-building activities fundamental to implementation success [42] [23].

The pragmatic approach advocated here addresses several critical limitations of traditional contextual inquiry methods. First, it emphasizes collaborative research designs that identify determinants across different settings and EBPs, using rapid approaches when possible [42]. Second, it promotes enhanced synthesis of existing research on implementation determinants to minimize duplication of effort [42]. Third, it requires clear rationale for why additional contextual inquiry is needed before undertaking new data collection [42]. This methodology is particularly valuable for drug development professionals and researchers working under resource constraints who need to quickly identify implementation barriers while maintaining methodological rigor.

Rapid Contextual Inquiry Methods and Protocols

Rapid Qualitative Assessment Protocols

Rapid Ethnography and Deductive Analysis: This approach involves gathering qualitative data on a brief, clearly delineated timeline while maintaining methodological integrity [42]. The protocol begins with developing a structured interview guide based on established implementation frameworks, such as the Consolidated Framework for Implementation Research (CFIR) or CFIR integrated with Health Equity (CFIR/HE) [43]. Participants should include key implementation team members, patients, and other relevant stakeholders [43]. Data collection should be focused and time-limited, with interviews typically lasting 45-60 minutes. Analysis employs rapid deductive qualitative methods using pre-established codebooks derived from implementation frameworks, allowing for efficient categorization of barriers and facilitators without the time-intensive process of inductive code development [42] [23].

Structured Template Summarization: For even greater efficiency, research teams can utilize rapid analysis of qualitative data by summarizing interview transcripts using structured templates [42]. This protocol involves creating a standardized summary template that captures major themes aligned with implementation framework domains. Research team members independently review transcripts and complete templates, followed by collaborative synthesis sessions to identify convergent and divergent themes. Studies have demonstrated consistency between this method and in-depth analysis, with one investigation finding no significant information gaps between approaches [42].

Brainwriting Premortem Technique

The brainwriting premortem is a proactive approach to identifying potential implementation barriers before they occur [23]. This protocol begins with assembling key stakeholders from the implementation setting. Participants independently document reasons why implementation efforts might fail, focusing specifically on the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework dimensions [23]. Following independent brainstorming, facilitators consolidate responses and lead structured discussions to prioritize barriers based on probability and impact. This method efficiently leverages collective expertise while avoiding groupthink that can occur in traditional brainstorming sessions.

Barrier Prioritization Methods

Card Sort Prioritization: When contextual inquiry reveals multiple barriers, research-practice partnerships must systematically determine which to address first [23]. This protocol involves writing identified barriers on individual cards and asking stakeholders to sort them into priority categories (e.g., high, medium, low) based on criteria such as changeability and impact [23]. The process can be conducted in person or using digital collaboration tools, with results tallied to identify consensus priorities.

Modified Conjoint Analysis: This more structured approach involves rating barriers through surveys or by physically placing sticky notes with each barrier on a 2×2 grid poster board with axes representing importance and changeability [23]. Stakeholders individually rate or place barriers, followed by facilitated discussion to reach consensus on which barriers represent the highest priorities for addressing through implementation strategies.

Table 1: Comparison of Rapid Contextual Inquiry Methods

Method	Time Requirement	Data Output	Best Use Cases
Rapid Ethnography with Deductive Analysis	2-4 weeks	Categorized barriers and facilitators mapped to implementation frameworks	Novel settings or EBPs where some prior research exists
Structured Template Summarization	1-2 weeks	High-level thematic summary of key barriers and facilitators	Settings with time constraints; verification of known determinants
Brainwriting Premortem	1-2 sessions	Proactive identification of potential failure points	Early implementation planning; complementing empirical data
Card Sort Prioritization	Single session	Rank-ordered list of implementation barriers	Multi-stakeholder teams; when numerous barriers are identified

Data Analysis and Synthesis Protocols

Quantitative Data Analysis for Contextual Inquiry

While contextual inquiry often emphasizes qualitative methods, quantitative analysis provides critical support for understanding implementation contexts and measuring differences between groups [44]. The protocol for quantitative analysis begins with descriptive statistics to characterize the sample, including means, medians, modes, standard deviations, and skewness [44]. When comparing quantitative variables between groups, researchers should generate appropriate visualizations such as boxplots for summarizing distributions or dot charts for smaller datasets [45]. For comparative analyses, calculate differences between group means or medians, ensuring that measures of variance (standard deviations, interquartile ranges) and sample sizes are reported for each group [45].

Table 2: Essential Quantitative Measures for Contextual Inquiry

Statistical Measure	Calculation Method	Interpretation in Contextual Inquiry
Descriptive Statistics
Mean	Sum of values divided by number of observations	Average level of a construct across participants
Standard Deviation	Measure of dispersion around the mean	Variability in responses; higher values indicate greater diversity
Between-Group Comparisons
Difference Between Means	Mean of Group A - Mean of Group B	Magnitude of difference between stakeholder groups
Interquartile Range (IQR)	Q3 - Q1	Spread of middle 50% of responses; useful for skewed data

Determinant Mapping and Synthesis

The final analytical protocol involves mapping identified barriers and facilitators to implementation frameworks to guide strategy selection. Using CFIR/HE ensures systematic consideration of multilevel determinants while explicitly addressing health equity considerations [43]. The process involves creating a determinant matrix that links identified factors to specific CFIR domains (intervention characteristics, outer setting, inner setting, individual characteristics, process) while noting equity implications using the health equity framework [43]. This structured approach facilitates more targeted implementation strategy selection.

Implementation and Toolkit

Research Reagent Solutions

Table 3: Essential Research Reagents for Contextual Inquiry

Reagent/Resource	Function	Application Notes
Structured Interview Guides	Standardized data collection aligned with implementation frameworks	Ensure inclusion of CFIR/HE domains; customize for specific setting
Rapid Analysis Templates	Efficient summarization of qualitative data	Pre-populate with implementation framework constructs
Determinant Prioritization Matrix	Visual tool for ranking barriers by importance and changeability	Use 2×2 grid; include criteria for ranking
Implementation Framework Codebooks	Deductive coding of qualitative data	CFIR/HE codebooks available through implementation science repositories

Integrated Research-Practice Partnership Workflow

Integrated Research-Practice Partnership Workflow

Rapid Contextual Inquiry Process

Rapid Contextual Inquiry Process

Troubleshooting and Optimization: Enhancing the Efficiency and Impact of Implementation Strategies

The Multiphase Optimization Strategy (MOST) is a comprehensive framework for developing and optimizing multicomponent interventions. In implementation science, MOST offers a principled approach to empirically identifying the combination of implementation strategies that produces the best expected outcomes given constraints imposed by the need for affordability, scalability, and efficiency [46] [37]. This represents a paradigm shift from the traditional approach of packaging multiple implementation strategies together and evaluating them as a whole in a two-arm randomized controlled trial (RCT). Instead, MOST enables researchers to systematically assess which strategies contribute meaningfully to implementation outcomes, and how they interact [37].

The core principle of MOST is to achieve intervention EASE, strategically balancing:

• Effectiveness
• Affordability
• Scalability
• Efficiency [46]

For implementation scientists, this means treating a package of implementation strategies as a type of intervention that can be optimized, moving beyond the limitations of evaluating multifaceted strategies without understanding each component's individual contribution and potential interactions [37].

MOST Framework: Phases and Application Scenarios

MOST comprises three sequential phases: Preparation, Optimization, and Evaluation [46] [37]. The framework can be applied to various implementation science scenarios, four of which are summarized in the table below.

Table 1: Phases of the MOST Framework

Phase	Primary Objective	Key Activities
Preparation	Lay foundation for optimization	Develop conceptual model; identify candidate implementation strategies; conduct pilot work; specify optimization objective [37].
Optimization	Empirical testing of components	Conduct optimization RCT (e.g., factorial design); assess performance of strategies independently and in combination [46] [37].
Evaluation	Confirm effectiveness of optimized package	Evaluate optimized implementation strategy package in a standard RCT [37].

Table 2: Application Scenarios for MOST in Implementation Science

Scenario	Description	Hypothetical Example
Developing new multifaceted implementation strategies	Building a new package of strategies from discrete components.	Creating a comprehensive plan to implement a school-based physical activity program [46].
Evaluating program-implementation strategy interactions	Examining how intervention components interact with implementation strategies.	Studying how a treatment guide's effectiveness is influenced by different training modalities [46].
Deconstructing established multifaceted strategies	Testing individual components of a previously bundled strategy.	Isolating effects of audit, feedback, and leadership buy-in from a previously combined "technical assistance" strategy [46].
Local adaptation of strategies	Modifying discrete or multifaceted strategies for a specific context.	Adapting a clinic-level implementation strategy for a new healthcare system with different resource constraints [46].

Experimental Protocol: Optimization Phase Using Factorial Design

The optimization phase typically employs an optimization RCT, with the factorial design being a common and efficient choice [37]. This design allows for the simultaneous testing of multiple implementation strategy components and their interactions. The following workflow diagram illustrates the key decision points in this phase.

Detailed Methodology

Hypothetical Example: Optimizing Implementation of a Smoking Cessation EBI This protocol outlines the steps for optimizing a package of implementation strategies to improve clinic-level adoption of an evidence-based smoking cessation intervention [37].

Background and Preparation Phase Outputs:

• Evidence-Based Intervention (EBI): A previously validated smoking cessation program.
• Identified Implementation Barriers: System-, organizational-, and individual-level barriers to adoption.
• Candidate Implementation Strategies (Factors): Four strategies identified through implementation mapping and theory:
  • Training (T): Comprehensive provider training program.
  • Treatment Guide (G): Structured clinical decision guide.
  • Workflow Redesign (W): Clinic workflow modification.
  • Supervision (S): Ongoing clinical supervision.

Table 3: Optimization RCT (2^4 Factorial Design) Specifications

Design Element	Specification
Experimental Design	Fully randomized 2^4 factorial design
Number of Conditions	16 (all possible combinations of the 4 strategies, each present or absent)
Randomization Unit	Clinic (cluster randomization)
Primary Outcome	Clinic-level adoption rate (proportion of eligible patients receiving EBI)
Secondary Outcomes	Implementation fidelity, cost, provider satisfaction
Key Analyses	Main effects of each strategy; two-way and higher-order interactions

Procedure:

• Recruitment and Randomization: Recruit 80 clinics (5 per experimental condition). Randomly assign each clinic to one of the 16 experimental conditions.
• Strategy Implementation: Implement the assigned combination of strategies in each clinic according to standardized protocols developed in the preparation phase.
• Data Collection: Collect outcome data over a 12-month active implementation period. Track resource utilization (cost, time) for each strategy.
• Data Analysis:
  • Use factorial ANOVA with effect coding (-1, +1) to estimate main effects and interaction effects.
  • Assess cost-effectiveness of individual strategy components.
• Optimization Decision: Apply the pre-specified optimization objective (e.g., "Maximize adoption rate with total implementation cost not exceeding $X per clinic").

Quantitative Data Analysis and Visualization Framework

Data Analysis Strategy

Quantitative data analysis in MOST utilizes specific methods to derive meaningful insights from optimization RCTs [47]. The primary analysis focuses on main effects and interaction effects using factorial ANOVA.

Table 4: Quantitative Data Analysis Methods for MOST

Analysis Type	Purpose	Application in MOST
Descriptive Analysis	Understand what happened in the data [47].	Calculate average adoption rates for each experimental condition.
Diagnostic Analysis	Understand why it happened by examining relationships between variables [47].	Analyze relationships between strategy combinations and outcomes.
Factorial ANOVA	Test main effects and interaction effects of multiple factors.	Determine significance of each implementation strategy and their interactions.
Cost-Effectiveness Analysis	Evaluate economic efficiency of different components.	Compare cost per additional adoption for each strategy component.

Results Interpretation and Decision Matrix

The following diagram illustrates the logical relationship between experimental results, decision-making, and the final optimized package.

Table 5: Hypothetical Optimization Results and Decision-Making

Implementation Strategy	Main Effect on Adoption (p-value)	Incremental Cost	Cost-Effectiveness Ratio	Decision
Training (T)	+12.4% (p<0.01)	$15,000	$1,210 per additional adoption	Include
Treatment Guide (G)	+8.2% (p<0.05)	$2,500	$305 per additional adoption	Include
Workflow Redesign (W)	+3.1% (p=0.18)	$8,000	$2,580 per additional adoption	Exclude
Supervision (S)	+5.6% (p=0.07)	$12,000	$2,143 per additional adoption	Exclude
Interaction T × G	+6.3% (p<0.05)	-	-	Reinforces inclusion of both

The Scientist's Toolkit: Research Reagent Solutions

Table 6: Essential Methodological Components for MOST Studies

Research Component	Function in MOST	Implementation Examples
Conceptual Model	Serves as the theoretical blueprint depicting how implementation strategies will produce desired outcomes [37].	CFIR (Consolidated Framework for Implementation Research); Theoretical Domains Framework.
Optimization Objective	Specifies how effectiveness will be balanced with implementation constraints [37].	"Maximize adoption rate with total cost ≤ $20,000 per clinic"; "Achieve 80% adoption with minimal provider time burden."
Factorial Experimental Design	Enables efficient assessment of multiple strategy components simultaneously [46] [37].	2^k factorial design; fractional factorial design for screening; sequential multiple assignment randomized trial (SMART).
Implementation Outcome Measures	Quantifies the success of implementation efforts.	Adoption rate; fidelity; cost; sustainability; provider acceptability [37].
Resource Tracking System	Captures data on affordability and scalability constraints.	Time-motion studies; cost accounting systems; provider workload assessment.

Balancing Effectiveness, Affordability, and Scalability (EASE) in Implementation Design

Achieving public health impact with evidence-based interventions (EBIs) requires careful balancing of multiple competing priorities. The EASE framework (balancing Effectiveness, Affordability, Scalability, and Efficiency) provides a principled approach to this challenge within implementation science [48]. The EASE framework is operationalized through the Multiphase Optimization Strategy (MOST), a comprehensive framework for developing, optimizing, and evaluating multicomponent interventions and implementation strategies [37] [49].

MOST represents a paradigm shift from the classical "treatment package" approach, where multiple components are bundled and evaluated as a single unit through randomized controlled trials (RCTs). Instead, MOST employs a more strategic process that treats individual implementation strategies as candidate components that may or may not ultimately be included in the final implementation package [37]. This approach allows researchers to answer critical questions about which components drive effectiveness, whether components interact with each other, and how to achieve the best outcomes within real-world constraints [48].

The framework consists of three sequential phases: Preparation (laying the conceptual and methodological foundation), Optimization (empirically testing candidate components), and Evaluation (rigorously testing the optimized package) [37]. By strategically balancing EASE criteria throughout these phases, implementation scientists can develop implementation strategies that not only work but are also practical, sustainable, and ready for widespread dissemination [48].

Core Principles and Methodological Foundations

Defining the Elements of EASE

Within the EASE framework, each dimension represents a critical consideration for implementation success:

• Effectiveness: The ability of implementation strategies to improve adoption, fidelity, and sustainment of EBIs, ultimately leading to improved health outcomes [48] [37].
• Affordability: The consideration of financial costs associated with implementing strategies, ensuring they fit within real-world budget constraints [37].
• Scalability: The potential for implementation strategies to be expanded to broader populations, settings, and systems while maintaining effectiveness [48].
• Efficiency: The strategic use of resources to achieve optimal outcomes, eliminating wasted effort and redundant components [48].

The MOST Framework: Phases and Objectives

The MOST framework provides a structured methodology for achieving EASE in implementation design through three distinct phases [37]:

• Preparation Phase: Researchers identify candidate implementation strategies, develop a conceptual model, specify the optimization objective, and conduct pilot work.
• Optimization Phase: Researchers empirically test candidate components using efficient experimental designs (e.g., factorial experiments) to determine which components should be included in the final implementation package.
• Evaluation Phase: The optimized implementation strategy is tested against a suitable control condition, typically using a randomized controlled trial design.

The Role of Factorial Designs in Optimization

Factorial designs, particularly the 2^k factorial experiment where each of the k factors (implementation strategies) is evaluated at two levels (present/absent), serve as a cornerstone experimental approach in the optimization phase of MOST [48] [37]. These designs enable efficient assessment of both the main effects of each implementation strategy and their interactions [48]. This methodology allows researchers to answer not only whether each discrete strategy has a positive, negative, or null effect on implementation outcomes, but also how strategies work in the presence or absence of one another [48] [37].

Table 1: Key Advantages of Factorial Designs for Implementation Optimization

Advantage	Methodological Explanation	Impact on EASE
Simultaneous Evaluation	All candidate components tested in a single, efficient experiment	Enhances Efficiency and Affordability of research process
Interaction Detection	Ability to identify synergistic or antagonistic effects between components	Improves Effectiveness through strategic component combinations
Resource Management	All research participants contribute to estimating all effects	Maximizes Efficiency of research resources and participant recruitment
Informed Decision-Making	Empirical data on performance and resource requirements for each component	Supports Scalability by identifying essential, high-impact components

Application Notes: Implementing the EASE Framework

Scenario-Based Applications

The integration of MOST and EASE principles addresses several critical scenarios in implementation science [48]:

• Development of New Multifaceted Implementation Strategies: Using factorial designs to build implementation strategy packages from discrete components, empirically determining the optimal combination rather than relying on a priori assumptions [48].
• Evaluating Component-Strategy Interactions: Examining how specific EBI components interact with implementation strategies, identifying potential synergistic or antagonistic effects that could impact both effectiveness and implementation outcomes [48].
• Deconstructing Established Multifaceted Strategies: Empirically testing discrete strategies that have previously been evaluated only as a package, identifying active ingredients and potential redundancies [48].
• Contextual Adaptation: Systematically modifying discrete or multifaceted implementation strategies for new local contexts while preserving core effective elements [48].

Integrated Workflow for EASE Implementation

The following diagram illustrates the sequential workflow for applying the EASE framework through the MOST process, from conceptualization to sustained implementation:

Conceptual Model for Implementation Strategy Optimization

A robust conceptual model is essential during the preparation phase to guide optimization. The following diagram illustrates the key constructs and their hypothesized relationships when optimizing implementation strategies for a school-based physical activity intervention, as described in the search results [48]:

Experimental Protocols for Implementation Optimization

Protocol 1: Factorial Optimization Trial

Objective: To empirically test discrete implementation strategies and their interactions to identify the most effective, affordable, scalable, and efficient combination.

Table 2: Factorial Optimization Trial Protocol Components

Protocol Element	Specifications	EASE Considerations
Design	Full or fractional 2^k factorial design where k = number of candidate implementation strategies [48]	Maximizes information yield per participant (Efficiency)
Randomization	Individual or cluster randomization depending on implementation context and level of analysis [37]	Ensures internal validity of effect estimates (Effectiveness)
Implementation Strategies	Selected based on conceptual model, prior evidence, and preliminary work; each operationalized with clear specification [48]	Enables precise replication and accurate cost estimation (Scalability, Affordability)
Primary Outcomes	Implementation outcomes (e.g., acceptability, fidelity, adoption) and/or clinical outcomes as appropriate [48]	Directly addresses implementation success (Effectiveness)
Sample Size	Powered to detect main effects and important interactions [37]	Balances statistical power with resource constraints (Efficiency, Affordability)
Data Analysis	Factorial ANOVA with effect coding to examine main effects and interactions [48] [37]	Provides unbiased estimates of individual and combined effects (Effectiveness)
Resource Tracking	Systematic documentation of time, materials, and personnel requirements for each strategy [37]	Enables Affordability and Scalability assessment

Protocol 2: Preparation Phase Strategy Selection

Objective: To systematically identify and refine candidate implementation strategies for testing in the optimization phase.

• Conceptual Modeling: Develop a conceptual model specifying hypothesized mechanisms linking implementation strategies to mediators and outcomes [48] [50].
• Stakeholder Engagement: Use human-centered design methods (e.g., Discover, Design, Build, Test framework) to ensure strategies address implementer needs and contextual constraints [50].
• Pilot Testing: Conduct small-scale testing to refine strategy specifications, assess feasibility, and estimate effect sizes for power calculations [37].
• Optimization Objective Specification: Define explicit criteria for how effectiveness will be balanced with affordability, scalability, and efficiency in the final implementation package [37].

Scientist's Toolkit: Essential Research Reagents

Table 3: Essential Methodological Tools for Implementation Optimization Research

Research Tool	Function	Application Context
Conceptual Model Template	Maps hypothesized relationships between strategies, mediators, and outcomes [48]	Preparation phase; guides component selection and measurement
Strategy Specification Checklist	Ensures complete description of implementation strategies per reporting guidelines [48]	Protocol development; enhances reproducibility
Factorial Design Generator	Creates randomization schemes for 2^k factorial experiments	Optimization phase; ensures proper experimental design
Cost Tracking Instrument	Systematically captures resource utilization for each strategy component [37]	Economic evaluation; informs affordability assessment
Implementation Outcome Measures	Validated instruments for acceptability, feasibility, appropriateness, etc. [48]	Outcome assessment; measures implementation success
Mediator Measures	Assesses hypothesized mechanisms of action (e.g., knowledge, self-efficacy) [48]	Process evaluation; tests conceptual model
Optimization Decision Framework	Structured approach for selecting final package based on EASE criteria [37]	Interpretation phase; guides decision-making

Quantitative Data Synthesis and Decision-Making

Data Analysis and Interpretation Framework

Analysis of factorial optimization trials focuses on estimating both main effects and interaction effects. The following table illustrates hypothetical data from a school-based physical activity implementation study with three candidate strategies [48]:

Table 4: Hypothetical Main and Interaction Effects on Implementation Outcome (Acceptability)

Implementation Strategy	Main Effect (β)	95% CI	p-value	Cost per Unit	Staff Time (hours)
Educational Outreach	0.45	(0.32, 0.58)	<0.001	$150	2.5
Technical Assistance	0.28	(0.15, 0.41)	0.002	$275	5.0
Expert Shadowing	0.12	(-0.01, 0.25)	0.072	$450	8.0
EdOut × TechAssist	0.18	(0.05, 0.31)	0.021	-	-
EdOut × Shadowing	-0.08	(-0.21, 0.05)	0.245	-	-
TechAssist × Shadowing	0.05	(-0.08, 0.18)	0.482	-	-

Optimization Decision Matrix

Based on the hypothetical data above, researchers would apply their pre-specified optimization objective to select the final implementation package. The following decision matrix illustrates how EASE considerations can be balanced:

Table 5: Implementation Package Decision Matrix Based on EASE Criteria

Strategy Combination	Expected Effectiveness	Total Cost	Staff Time	Scalability Potential	EASE Balance
Educational Outreach Only	Medium	$150	2.5 hours	High	Favors Affordability/Scalability
EdOut + TechAssist	High (with synergy)	$425	7.5 hours	Medium	Balanced EASE Profile
All Three Strategies	High (diminishing returns)	$875	15.5 hours	Low	Favors Effectiveness

EASE Optimization Algorithm

The final selection of implementation components follows a systematic decision process based on empirical data and pre-specified constraints, as illustrated below:

Methodological Recommendations for Assessing the Impact of Adaptations on Outcomes

A major gap in implementation research is the lack of guidance for designing studies to assess the impact of adaptations to interventions and implementation strategies [51]. While many researchers regard experimental designs as the gold standard, the possible study designs for assessing the impact of adaptation on implementation, service, and person-level outcomes is broad in scope, including descriptive and correlational research and variations of randomized controlled trials [51]. This article provides a set of key methodological recommendations for assessing the impact of adaptations to interventions and implementation strategies on implementation outcomes, framed within the broader context of developing pragmatic measures for implementation science research.

Core Methodological Recommendations

Define Adaptations and Identify Type/Timing

We recommend that study teams first define the construct of adaptations and identify the type and timing of adaptations [51]. Adaptation has been defined as "a process of thoughtful and deliberate alteration to the design or delivery of an intervention, with the goal of improving its fit or effectiveness in a given context" [51]. When time and resources are limited, we recommend assessing seven key aspects of adaptations [51]:

• What was adapted: Which specific activities or components of the protocol were modified
• Focus: Whether the adaptation targeted the intervention, implementation strategies, or context
• Purpose: The reason for adaptation (e.g., to enhance reach, improve equity, increase fidelity)
• Timing and sequence: When the adaptation occurred relative to initial delivery
• Bundling: Whether the adaptation was bundled with other adaptations
• Scope and frequency: Whether all participants were exposed and how often
• Planning status: Whether the adaptation was planned or made in response to emerging events

Table 1: Framework for Documenting Adaptation Characteristics

Characteristic	Documentation Elements	Data Collection Methods
What	Specific components, activities, or protocols modified	Implementation logs, stakeholder interviews
Focus	Intervention core functions vs implementation strategies	FRAME-IS coding, team meeting documentation
Timing	Before, during, or after implementation; sequence of multiple adaptations	Timeline mapping, prospective tracking
Reason	Improve fit, address barriers, enhance equity, increase reach	Structured interviews, adaptation tracking forms
Context	Setting characteristics, external factors influencing adaptation	Context assessment, environmental scans

Identify Expected Proximal and Distal Outcomes

We recommend that study teams identify and specify the expected proximal and distal outcomes of adaptations [51]. This involves conceptualizing, assessing, and reporting both immediate and longer-term outcomes of adaptations to interventions and/or strategies. Key considerations include [51]:

• Intended vs unintended effects: Document both expected outcomes and unanticipated consequences
• Equity relevance: Determine whether the adaptation aims to address health inequities
• Timing: Specify proximal (immediate) versus distal (later-occurring) outcomes
• Mechanism impact: Assess how adaptations affect intervention or implementation strategy mechanisms
• Multi-level outcomes: Consider implementation, service, and person-level outcomes

Table 2: Adaptation Outcomes Framework

Outcome Level	Proximal Outcomes	Distal Outcomes	Measurement Approaches
Implementation	Feasibility, acceptability, appropriateness	Fidelity, penetration, sustainability	Provider surveys, fidelity checklists, administrative data
Service	Reach, service quality, equity of delivery	Service efficiency, patient experience	Patient surveys, clinical records, quality indicators
Client/Person	Engagement, satisfaction, intermediate outcomes	Health status, quality of life, long-term outcomes	Clinical assessments, patient-reported outcomes

Study Design Considerations

We recommend that study teams consider all possible study design options and choose the design that is best suited to answer the research question(s) while balancing logistical constraints and challenges [51]. The selection of study designs for adaptation research should consider [51]:

• Primary vs add-on studies: Adaptation studies can be standalone investigations or secondary aims within larger trials
• Practical constraints: Time, resources, and partner preferences should inform design choices
• Contextual factors: The complexity of the adaptation and implementation environment
• Ethical considerations: Balancing rigor with feasibility in real-world settings

Analytical Approaches

We recommend that study teams consider the type of adaptation and outcome data available, the goals of the adaptation study, and the complexity of the study design when selecting analytic approaches [51]. Analytical considerations include:

• Data type and quality: Nature of adaptation documentation and outcome measures
• Causal inference goals: Whether the aim is descriptive, correlational, or causal
• Multilevel structure: Nesting of data within sites, providers, or organizations
• Temporal patterns: Timing and sequencing of adaptations and outcomes

Experimental Protocols for Adaptation Tracking

Prospective Adaptation Tracking Protocol

Purpose: To systematically document adaptations as they occur during implementation.

Materials:

• Adaptation tracking form (electronic or paper-based)
• Audio recording equipment for interviews
• Data management system for structured adaptation data

Procedure:

• Pre-implementation training: Train implementation staff on adaptation documentation procedures
• Regular assessment: Conduct bi-weekly team meetings to discuss potential or enacted adaptations
• Structured documentation: Complete adaptation tracking forms for each identified modification
• Stakeholder input: Conduct monthly interviews with key stakeholders about adaptation decisions
• Data synthesis: Compile adaptation data quarterly for preliminary analysis
• Triangulation: Compare adaptation reports across multiple data sources

Deliverables:

• Complete adaptation logs with timestamps
• Coded adaptations using FRAME or FRAME-IS
• Summary reports of adaptation patterns

Retrospective Adaptation Assessment Protocol

Purpose: To identify and characterize adaptations after implementation has occurred.

Materials:

• Implementation documentation (meeting minutes, progress reports)
• Interview guides for retrospective assessment
• Qualitative data analysis software

Procedure:

• Document review: Systematically review all implementation documentation
• Key informant identification: Identify staff and stakeholders involved in implementation
• Structured interviews: Conduct interviews using timeline-assisted recall
• Adaptation identification: Compile potential adaptations from multiple sources
• Consensus coding: Use multiple coders to characterize adaptations using standardized frameworks
• Pattern analysis: Identify temporal patterns and clusters of adaptations

Deliverables:

• Retrospective adaptation inventory
• Categorized adaptations with rationale and outcomes
• Timeline of adaptation implementation

Visualization of Adaptation Assessment Processes

Adaptation Tracking Workflow

Adaptation Impact Assessment Model

Research Reagent Solutions and Essential Materials

Table 3: Essential Methodological Tools for Adaptation Research

Tool Category	Specific Tool/Resource	Function/Purpose	Application Context
Documentation Frameworks	FRAME (Framework for Reporting Adaptations and Modifications) [51]	Systematic documentation of adaptations to interventions	Characterizing what, why, and how adaptations occur
Documentation Frameworks	FRAME-IS (Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies) [51]	Documenting modifications to implementation strategies	Tracking changes to implementation approaches
Conceptual Models	MADI (Model for Adaptation Design and Impact) [51]	Creating explanatory models for adaptations' impact on outcomes	Hypothesis development about adaptation-outcome relationships
Conceptual Models	PRISM (Practical, Robust Implementation and Sustainability Model) [51]	Tailoring iterative adaptations based on implementation priorities	Guiding adaptation decisions during implementation
Data Collection Tools	Prospective adaptation tracking forms	Real-time documentation of adaptations as they occur	Ongoing implementation quality improvement
Data Collection Tools	Retrospective interview guides	Reconstruction of adaptation history after implementation	Post-implementation evaluation studies
Analytical Approaches	Multi-level modeling	Accounting for nested data structures in adaptation studies	Studies with adaptations at multiple levels
Analytical Approaches	Qualitative comparative analysis	Identifying configurations of adaptations associated with outcomes	Complex adaptation patterns across multiple sites

Conceptual Foundation: The Functions-Forms Paradigm

The "Core Functions and Forms" model represents a paradigm shift in implementation science, reframing how fidelity and adaptation are conceptualized and operationalized [52]. This model provides a critical framework for making deliberate adaptation decisions to improve the fit of Evidence-Based Practices (EBPs) in new contexts without compromising their effectiveness.

• Core Functions: The underlying, theorized elements of an EBP responsible for achieving its proximal, theorized mechanism of action. These represent the essential processes or active ingredients through which the intervention exerts its effects [52].
• Mutable Forms: The specific operationalizations or protocols of intervention elements intended to enact the core function. These represent the tangible, deliverable components of an intervention that can be modified to suit different contexts and populations [52].

This distinction enables a crucial shift from prioritizing strict form fidelity (reproducing an EBP's protocol based on prior operationalization) to emphasizing function fidelity (maintaining fidelity to the underlying purpose of intervention components) when implementing in novel settings [52].

Operational Frameworks for Application

The EPIS Integration Framework

The Functions-Forms paradigm can be systematically integrated throughout all phases of the Exploration, Preparation, Implementation, Sustainment (EPIS) framework to guide adaptation decisions [52]:

Table 1: Functions-Forms Integration Throughout EPIS Framework

EPIS Phase	Key Functions-Forms Applications	Primary Objectives
Exploration	Focusing on both function and form to guide EBP selection	Identify EBPs with core functions aligning with local context goals while having forms adaptable to the setting
Preparation	Using function-form matrices to guide adaptation decisions and measurement protocols	Develop localized EBP adaptations while planning monitoring of both form and function fidelity
Implementation	Informing data collection and feedback strategies	Identify how pre-planned and ad-hoc adaptations impact implementation, service, and clinical outcomes
Sustainment	Analyzing process and outcomes data to evaluate fidelity levels	Generate hypotheses about what is truly "core" versus "adaptable" in the new context for future iterations

Methodological Recommendations for Adaptation Studies

Recent methodological advancements provide structured approaches for investigating adaptation impact [51]:

• Define the adaptation construct by identifying type, timing, and characteristics of adaptations
• Identify expected proximal and distal outcomes of adaptations to interventions and/or strategies
• Select appropriate study designs that balance practical constraints with methodological rigor
• Choose analytic approaches considering adaptation type, available data, and study complexity

When tracking adaptations, seven key aspects should be documented: what was adapted, focus of adaptation, purpose, timing and sequence, whether bundled with other adaptations, scope and frequency, and whether planned or responsive [51].

Measurement and Evaluation Approaches

Pragmatic Measurement Framework

Strong measurement is critical for monitoring and evaluating adaptation efforts in implementation practice [53]. The selection of high-quality implementation measures connects individual adaptation initiatives to broader implementation science through a structured Measurement Roadmap:

• Identify Guiding Theory, Model, or Framework and constructs of interest
• Leverage Existing Systematic Reviews and Repositories to identify appropriate measures
• Conduct Critical Psychometric and Pragmatic Analysis to evaluate measure quality

Context Assessment Instrument

The Inventory of Factors Affecting Successful Implementation and Sustainment (IFASIS) provides a validated, pragmatic quantitative instrument for assessing organizational context [54]. This 27-item, team-based measure operationalizes context through two rating scales capturing current state and importance of each item to an organization. It demonstrates strong reliability, internal consistency, and predictive validity, with significant associations between higher IFASIS scores and improved implementation outcomes [54].

Application Protocols and Workflows

Core Protocol: Functional Adaptation Assessment

Purpose: To systematically evaluate and prioritize potential adaptations while preserving core functions.

Materials:

• Functions-Forms Matrices (for mapping intervention components)
• Adaptation documentation tools (e.g., FRAME, FRAME-IS)
• Context assessment measures (e.g., IFASIS)
• Stakeholder engagement framework

Procedure:

• Intervention Deconstruction

  • Identify all intervention components (forms)
  • Theorize the core function of each component
  • Document hypothesized mechanisms of action

• Context Assessment

  • Administer organizational context measure (IFASIS)
  • Conduct stakeholder interviews to identify contextual constraints
  • Map contextual factors against intervention requirements

• Adaptation Identification

  • Brainstorm potential adaptations to improve contextual fit
  • Categorize each adaptation by type (content, context, etc.)
  • Document expected impact on core functions

• Adaptation Prioritization

  • Evaluate each potential adaptation against core functions
  • Prioritize adaptations that preserve functions while improving fit
  • Develop implementation plan for selected adaptations

• Monitoring Framework

  • Establish metrics for both form and function fidelity
  • Plan iterative assessment points throughout implementation
  • Document unplanned adaptations as they occur

Functional Fidelity Assessment Workflow

The following diagram illustrates the core decision process for evaluating adaptations while maintaining functional fidelity:

Research Reagents and Implementation Tools

Table 2: Essential Research Reagents for Adaptation Science

Tool/Instrument	Function	Application Context
FRAME/FRAME-IS	Systematic documentation of modifications to interventions and implementation strategies	Tracking adaptations during implementation; categorizing adaptation characteristics [51]
IFASIS	Quantitative assessment of organizational context factors affecting implementation	Measuring contextual determinants pre- and post-adaptation; predicting implementation outcomes [54]
Functions-Forms Matrix	Mapping relationship between intervention components and core functions	Planning and evaluating adaptations; maintaining function fidelity during adaptation [52]
ADAPT Guidance	Process model for adapting and transferring EBIs to new contexts	Structured approach to adaptation process from planning to sustainment [51]
Measurement Roadmap	Structured process for selecting implementation measures	Identifying appropriate measures for monitoring adaptation impact [53]

Data Synthesis and Analysis Framework

Quantitative Data Visualization for Adaptation Studies

Effective data visualization is essential for analyzing adaptation impact across multiple dimensions:

Table 3: Visualization Approaches for Adaptation Data Analysis

Data Type	Recommended Visualization	Analytical Purpose
Comparison of outcomes across adapted vs. non-adapted components	Boxplots [45]	Display distribution differences; identify outliers and central tendencies
Tracking implementation outcomes over time	Line charts [55]	Visualize trends, increases, declines, or seasonality in outcome data
Relationship between number of adaptations and implementation outcomes	Scatter plots [55]	Explore correlations and identify patterns or clusters
Part-to-whole relationships of adaptation types	Treemap charts [55]	Show hierarchical data and proportions of different adaptation categories
Multivariate analysis of context, adaptations, and outcomes	Heatmap charts [55]	Identify complex patterns across multiple variables simultaneously

Adaptation Impact Assessment Protocol

Purpose: To quantitatively evaluate the impact of adaptations on implementation and effectiveness outcomes.

Experimental Design:

• Use mixed-methods approaches combining quantitative measures with qualitative context
• Employ longitudinal designs to track outcomes pre- and post-adaptation
• Include comparison groups where feasible (adapted vs. non-adapted sites)

Data Collection:

• Implement standardized adaptation tracking using FRAME or FRAME-IS
• Collect implementation outcomes (acceptability, appropriateness, feasibility, fidelity)
• Measure service and clinical outcomes at multiple time points
• Document contextual factors using validated measures (IFASIS)

Analysis Plan:

• Calculate descriptive statistics for all adaptation characteristics
• Compute differences in means and medians between pre- and post-adaptation periods
• Employ regression models to identify predictors of successful adaptations
• Use thematic analysis for qualitative data on adaptation process

The Functions-Forms paradigm, supported by structured protocols and pragmatic measures, enables implementation researchers and practitioners to make systematic, evidence-informed adaptation decisions that preserve the essential elements of evidence-based interventions while optimizing their fit for diverse contexts and populations.

In implementation science, dynamic barriers are contextual and methodological challenges that evolve throughout the research process, potentially compromising the validity, reliability, and relevance of study findings. These barriers systematically introduce error by preventing the unprejudiced consideration of research questions, ultimately threatening the successful adoption of evidence-based interventions in real-world settings [56]. Unlike static methodological issues, dynamic barriers manifest and transform across the planning, data collection, analysis, and publication phases of research, requiring equally dynamic and vigilant mitigation strategies [56]. For researchers and drug development professionals, understanding these barriers is paramount to developing pragmatic measures that maintain their scientific rigor and practical relevance throughout implementation processes.

The most insidious dynamic barriers include various forms of research bias that can distort evidence generation, particularly in longitudinal studies where measurement instruments must remain valid across temporal, contextual, and technological shifts. As implementation science seeks to bridge the gap between evidence and practice in healthcare, systematically addressing these barriers becomes essential for creating system-wide change and achieving adoption at scale [57]. This document provides application notes and protocols for identifying, monitoring, and mitigating these dynamic barriers throughout the implementation research lifecycle.

Protocol for Mitigating Bias in Implementation Research

Understanding and Categorizing Research Bias

Bias in research represents "systematic error introduced into sampling or testing by selecting or encouraging one outcome or answer over others" [56]. Unlike random error, which decreases with increasing sample size, bias is independent of both sample size and statistical significance and can cause estimates of association to be either larger or smaller than the true association [56]. In extreme cases, bias can cause a perceived association directly opposite of the true association, as demonstrated in historical studies of hormone replacement therapy where initial observational studies showed decreased risk of heart disease, while more rigorous later studies found increased risk [56].

Table 1: Categorization of Major Research Biases and Mitigation Approaches

Bias Type	Phase of Occurrence	Definition	Primary Mitigation Strategies
Selection Bias	Pre-trial	When criteria for recruiting patients into study cohorts are inherently different [56]	Use rigorous, predefined selection criteria; prospective designs where outcome is unknown at enrollment [56]
Channeling Bias	Pre-trial	Patient prognostic factors dictate study cohort assignment [56]	Randomization; clearly defined assignment protocols blind to prognostic factors [56]
Interviewer Bias	During trial	Systematic difference in how information is solicited, recorded, or interpreted [56]	Standardize interviewer interactions; blind interviewers to exposure status [56]
Recall Bias	During trial	Differential recall of information between groups based on outcomes or exposures [56]	Use objective data sources; corroborate with medical records; prospective designs [56]
Performance Bias	During trial	Unequal provision of care or exposure apart from the intervention under investigation [56]	Cluster stratification; standardization of procedures; blinding where possible [56]
Chronology Bias	During trial	Differences arising from use of historic controls affected by secular trends [56]	Use concurrent controls; limit use of historic controls to recent past [56]
Transfer Bias	During trial	Unequal follow-up or loss to participants across study groups [56]	Design comprehensive follow-up plan prior to study; intention-to-treat analysis [56]
Citation Bias	Post-trial	Selective citation of positive or statistically significant results [56]	Register trials in clinical trial registries; check for unpublished similar trials [56]

Structured Protocols for Bias Mitigation

Pre-Trial Bias Assessment Protocol

Objective: To identify and mitigate potential biases during study design and patient recruitment phases, where errors can cause fatal flaws that cannot be compensated during data analysis.

Materials:

• Study protocol document
• Risk stratification models (e.g., Caprini for venous thromboembolism [56])
• Standardized outcome measures (e.g., Breast-Q [56])
• Data collection standardization protocols

Procedure:

• Clearly define risk and outcome measures using objective, validated instruments with low inter-rater variability [56].
• Establish standardized protocols for data collection, including training of study personnel to minimize inter-observer variability [56].
• Implement blinding procedures where possible, ensuring examiners measuring outcomes are different from those evaluating exposures [56].
• Design prospective recruitment strategies where outcome is unknown at time of enrollment to minimize selection bias [56].
• Develop randomization procedures for patient assignment to groups to prevent channeling bias based on prognostic factors [56].

Validation: Conduct a preliminary assessment of measurement inter-rater reliability using intraclass correlation coefficients or kappa statistics, with targets >0.8 established before full study implementation.

In-Trial Bias Monitoring Protocol

Objective: To detect and address information biases that occur during data collection and patient follow-up.

Materials:

• Standardized data collection forms
• Blinding assessment questionnaires
• Follow-up tracking system
• Source documentation verification checklist

Procedure:

• Maintain interviewer blinding through standardized interactions and separation of outcome assessors from exposure assessors [56].
• Implement chronological controls by using concurrent rather than historic controls where possible [56].
• Verify subjective data through cross-referencing with objective sources and medical records [56].
• Execute comprehensive follow-up procedures designed prior to study initiation to minimize transfer bias [56].
• Conduct periodic blinding assessments to evaluate potential compromise and implement corrective actions if needed.

Validation: Regular interim monitoring of data collection consistency, loss-to-followup rates across groups, and blinding effectiveness.

Strategies for Maintaining Measure Relevance Over Time

Dynamic Contextual Assessment Framework

Maintaining measure relevance in implementation science requires continuous assessment of contextual factors that evolve throughout the research process. The Normalization Process Theory (NPT) provides a theoretical foundation for understanding how new practices become embedded and sustained, offering mechanisms to monitor and maintain relevance [58]. The ISAC Match process further provides a pragmatic matching process for selecting and tailoring implementation strategies through integrated research-practice partnerships [59].

Table 2: Framework for Maintaining Measure Relevance Across Implementation Phases

Implementation Phase	Relevance Threats	Monitoring Strategies	Adaptation Protocols
Planning	Measures lack fit with local context or implementation setting	Contextual inquiry; stakeholder engagement; review of practice-based evidence [59]	Tailor measures to local context while maintaining core constructs; use rapid deductive qualitative approaches [59]
Initial Implementation	Evolving understanding of intervention components and outcomes	Regular fidelity assessment; implementer feedback mechanisms [58]	Modify implementation strategies while protecting core intervention components
Sustainment	Organizational and system changes; intervention drift	Periodic measure re-validation; assessment of continued appropriateness [57]	Update measures to reflect new evidence or contexts while maintaining longitudinal comparability
Scale-Up	Variation across new settings and populations	Cross-contextual measure validation; assessment of measurement invariance [57]	Develop core measure adaption guidelines for new contexts

Implementation Strategy Selection and Tailoring

The ISAC Match process provides a systematic four-step approach for selecting and tailoring implementation strategies to overcome dynamic barriers [59]:

Step 1: Contextual Inquiry

• Review available information on evidence-based intervention integration from both peer-reviewed and practice-based evidence [59].
• If additional inquiry is needed, employ rapid methods such as rapid deductive qualitative approaches, rapid ethnography, or brainwriting premortem approaches [59].
• Prioritize barriers using card sorting activities or rating by changeability and importance using a 2×2 grid [59].

Step 2: Identify Existing Implementation Strategies

• Engage with practitioners and review practice materials to catalog strategies already in use [59].
• Facilitate discussions to identify existing implementation strategies that may be leveraged or built upon [59].

Step 3: Select Implementation Strategies

• Use the ISAC guidance tool to select implementation strategies by determinant framework level or by implementation outcomes [59].
• Prioritize strategies using feasibility-importance matrices, priority points allocation, or nominal group techniques [59].

Step 4: Tailor Implementation Strategies

• Use brainwriting premortem processes to identify potential reasons strategies would fail [59].
• Employ liberating structures or nominal group techniques to adapt strategies to local context [59].

Experimental Validation and Assessment Protocols

Quantitative Assessment of Dynamic Measures

Objective: To quantitatively evaluate the stability and relevance of implementation measures over time and across contexts.

Materials:

• Longitudinal implementation dataset
• Statistical analysis software (R, SPSS, or Python with Pandas, NumPy, SciPy [60])
• Data visualization tools (ChartExpo, Vega-Lite [60] [61])

Procedure:

• Collect repeated measures of implementation outcomes across multiple time points using consistent measurement approaches.
• Calculate descriptive statistics (mean, median, standard deviation, IQR) for each time period and compare across periods [45].
• Visualize temporal patterns using line charts for continuous trends, bar charts for categorical comparisons, and boxplots for distributional changes [62] [45].
• Assess measure invariance using statistical tests of measurement equivalence across time points and contexts.
• Model temporal trajectories using growth curve models or time series analysis to identify systematic changes in measures.

Analytical Visualization: For quantitative comparison of measures across different groups or time periods, several visualization approaches are appropriate [45]:

• Back-to-back stemplots: Best for small amounts of data comparing two groups.
• 2-D dot charts: Appropriate for small to moderate amounts of data across any number of groups.
• Boxplots: Ideal for moderate to large datasets, displaying five-number summaries (minimum, Q1, median, Q3, maximum) for each group [45].

Pragmatic Measure Validation Protocol

Objective: To establish and maintain the psychometric properties of implementation measures throughout the research process.

Materials:

• Candidate measure instruments
• Validation dataset representing target populations
• Statistical analysis software

Procedure:

• Assess content validity through expert review and stakeholder feedback at regular intervals.
• Evaluate construct validity via factor analysis and correlation with established measures.
• Test criterion validity against gold standard measures where available.
• Measure reliability through test-retest correlation, internal consistency (Cronbach's alpha), and inter-rater reliability.
• Document responsiveness to change through longitudinal assessment in implementation contexts.

Visualization and Data Presentation Standards

Workflow Visualization for Bias Mitigation Protocols

Research Bias Mitigation Workflow

Measure Relevance Maintenance Process

Measure Relevance Maintenance Process

Research Reagent Solutions for Implementation Science

Table 3: Essential Methodological Tools for Implementation Research

Tool Category	Specific Tool/Technique	Function	Application Context
Bias Assessment Tools	Cochrane Risk of Bias Tool	Systematically evaluates potential biases in study design	Clinical trials and intervention studies [56]
Implementation Frameworks	Normalization Process Theory (NPT)	Explains how practices become embedded in social contexts	Understanding implementation mechanisms [58]
Strategy Compilations	ISAC (Implementation Strategies Applied in Communities)	Provides community-appropriate implementation strategies	Community settings and non-clinical interventions [59]
Strategy Selection	ISAC Match Process	Four-step process for selecting/tailoring implementation strategies	Integrated research-practice partnerships [59]
Quantitative Analysis	Descriptive Statistics (Mean, Median, SD, IQR)	Summarizes and compares data across groups	Initial data exploration and group comparisons [45] [60]
Data Visualization	Boxplots, Line Charts, Bar Charts	Enables visual comparison of quantitative data across groups	Identifying patterns, trends, and outliers [62] [45]
Color Accessibility	Colorblind-Friendly Palettes (Okabe-Ito, ColorBrewer)	Ensures visualizations are accessible to colorblind users	All data visualization and presentation [63] [64]
Contextual Inquiry	Rapid Deductive Qualitative Approaches	Quickly identifies barriers and facilitators in new settings	When limited evidence exists on contextual factors [59]

Validation and Comparative Effectiveness: Building Robust Evidence for Pragmatic Measures and Strategies

The development of reliable and valid measures is a cornerstone of advancing implementation science, as these measures allow practitioners to assess local implementation barriers, select appropriate strategies, monitor progress, and evaluate ultimate success [14]. However, for these measures to be truly useful in real-world practice settings, they must be not only psychometrically sound but also pragmatic – designed with stakeholder needs and practical constraints in mind [14]. Glasgow and Riley emphasized that practitioners are unlikely to utilize measures, even psychometrically strong ones, if they are not also pragmatic, highlighting considerations such as training requirements, time burden for administration and scoring, and overall feasibility in practice settings [14].

This document provides application notes and protocols for designing validation studies that span the methodological spectrum, from descriptive research to randomized controlled trials (RCTs), all framed within the context of developing and validating pragmatic measures for implementation science. The goal is to provide researchers, scientists, and drug development professionals with structured methodologies to generate rigorous, applicable evidence for their measurement tools.

The PAPERS Framework: Pragmatic Rating Criteria for Measures

The Psychometric and Pragmatic Evidence Rating Scale (PAPERS) was developed through stakeholder-driven research to establish criteria for assessing whether implementation measures are pragmatic [14]. This framework emerged from multiple studies that identified, refined, and prioritized the key characteristics of pragmatic measures, resulting in four primary domains and associated criteria outlined in the table below.

Table 1: The PAPERS Framework: Domains and Criteria for Pragmatic Measures

Domain	Description	Specific Criteria
Useful	Measures produce actionable information for decision-making	Produces reliable and valid results; Informs clinical or organizational decision-making [14]
Compatible	Measures fit well with existing systems and workflows	Applicable; Fits organizational activities [14]
Acceptable	Measures are agreeable to stakeholders	Creates low social desirability bias; Relevant; Offers relative advantage; Acceptable to staff and clients; Low cost [14]
Easy	Measures are simple to implement and use	Uses accessible language; Efficient; Feasible; Easy to interpret; Creates low assessor burden; Items not wordy; Completed with ease; Brief [14]

Application Notes on the PAPERS Framework

When designing validation studies for new implementation measures, researchers should incorporate the PAPERS criteria throughout the development process. The Useful domain emphasizes that measures must produce actionable information that informs decision-making in clinical or organizational contexts [14]. The Compatible domain requires that measures fit seamlessly within existing workflows and organizational activities [14]. The Acceptable domain addresses stakeholder perceptions, including low social desirability bias, relevance, and cost considerations [14]. Finally, the Easy domain focuses on practical implementation factors such as language accessibility, efficiency, and low assessor burden [14].

Study Designs for Validation Research

Selecting an appropriate study design is crucial in implementation science because it directly influences the validity, reliability, and applicability of research findings [65]. A well-chosen design ensures that research effectively addresses the complexities of implementing evidence-based practices in real-world settings while balancing rigor with feasibility [65].

The following table summarizes the primary study designs used in validation and implementation research, along with their key characteristics and applications.

Table 2: Study Designs for Validation and Implementation Research

Study Design	Key Characteristics	Applications in Implementation Science	Considerations for Measure Validation
Randomized Controlled Trials (RCTs)	Random assignment to treatment or control groups; Reduces bias [65]	Tests effectiveness of implementation strategies under controlled conditions [65]	Provides high-quality evidence for measure validity but may lack real-world generalizability
Cluster Randomized Trials (cRCTs)	Groups (clusters) rather than individuals are randomized [65]	Evaluates group-level interventions in hospitals, schools, or communities [65]	Suitable for measures assessing organizational constructs or implementation climate
Stepped-Wedge Designs	All clusters receive intervention; timing is randomized and staggered [65]	Useful when intervention is considered beneficial; allows within- and between-cluster comparisons [65]	Enables longitudinal assessment of measure performance across different implementation phases
Pragmatic Trials	Evaluates interventions in real-world, routine practice settings [65]	Assesses how interventions perform in everyday practice with diverse populations [65]	Ideal for testing pragmatic qualities of measures in actual use contexts
Hybrid Designs	Simultaneously evaluates intervention effectiveness and implementation strategies [65]	Type 1: Focuses on effectiveness while gathering implementation dataType 2: Equal emphasis on bothType 3: Focuses on implementation while collecting effectiveness data [65]	Allows concurrent validation of measures while studying implementation processes

The Multiphase Optimization Strategy (MOST) for Measure Development

The Multiphase Optimization Strategy (MOST) is a comprehensive framework for developing, optimizing, and evaluating multicomponent implementation strategies, which can be readily adapted for measure validation studies [65]. MOST consists of three sequential phases:

• Preparation Phase: Researchers identify and define the components of the measure and its implementation requirements.
• Optimization Phase: Experimental designs, such as factorial experiments, test and refine these components to achieve an optimal balance between effectiveness, affordability, scalability, and efficiency.
• Evaluation Phase: The optimized measure is rigorously tested, often through randomized controlled trials, to ensure it meets the desired outcomes [65].

Experimental Protocols for Measure Validation

Protocol 1: Stakeholder-Driven Pragmatic Assessment

Objective: To evaluate the pragmatic qualities of implementation measures using stakeholder feedback.

Background: The pragmatic characteristics of measures significantly influence their adoption and use in real-world practice settings [14]. This protocol provides a systematic approach for assessing these characteristics.

Table 3: Research Reagent Solutions for Stakeholder Assessment

Research Reagent	Function	Application Notes
PAPERS Criteria Checklist	Standardized assessment of pragmatic measure properties	Use the 11 criteria across Useful, Compatible, Acceptable, and Easy domains [14]
Stakeholder Delphi Protocol	Structured communication for achieving consensus	Engage 12+ stakeholders representing diverse implementation contexts [14]
Concept Mapping Methodology	Visual representation of stakeholder conceptualizations	Participants group terms and phrases into conceptually distinct categories [14]
Pragmatic Rating Scale (6-point)	Quantifies stakeholder perceptions of pragmatic qualities	Demonstrates sufficient variability across pragmatic criteria [14]

Methodology:

• Participant Recruitment: Recruit a minimum of 12 stakeholders representing diverse implementation contexts (e.g., healthcare providers, organizational leadership, implementation intermediaries) [14].
• Delphi Process: Conduct a modified multi-round Delphi process to transform expert opinion into group consensus regarding pragmatic criteria [14].
• Concept Mapping: Engage stakeholders in grouping pragmatic terms and phrases into conceptually distinct categories, then rate the clarity and importance of each [14].
• Data Analysis: Analyze results to identify stakeholder-prioritized dimensions of pragmatic measures and assess consensus levels (typically 80% agreement threshold).

Outcome Measures: Stakeholder ratings of pragmatic criteria importance; Level of consensus on key pragmatic dimensions; Refined list of prioritized pragmatic criteria.

Protocol 2: Hybrid Type 2 Trial for Concurrent Validation

Objective: To simultaneously evaluate the psychometric properties of implementation measures and the effectiveness of implementation strategies.

Background: Hybrid designs are particularly valuable in implementation science because they allow researchers to understand both clinical outcomes and implementation processes concurrently [65].

Methodology:

• Study Design: Implement a Hybrid Type 2 design that gives equal emphasis to both intervention effectiveness and implementation outcomes [65].
• Site Selection: Identify multiple practice settings (e.g., clinics, hospitals) that represent diverse implementation contexts.
• Participant Recruitment: Enroll both implementation agents (e.g., healthcare providers) and recipients (e.g., patients) to assess measure performance across stakeholder groups.
• Data Collection:
  • Collect quantitative data on measure reliability, validity, and sensitivity to change.
  • Gather qualitative data on stakeholder experiences with measure implementation, focusing on pragmatic qualities.
  • Document contextual factors that influence measure performance and implementation success.
• Analysis Plan:
  • Assess psychometric properties using appropriate statistical methods (e.g., factor analysis, reliability coefficients).
  • Evaluate implementation outcomes using mixed methods approaches.
  • Examine relationships between contextual factors, implementation success, and measure performance.

Outcome Measures: Measure reliability and validity indices; Implementation outcomes (adoption, fidelity, sustainability); Stakeholder perceptions of measure pragmatism; Contextual factor documentation.

Protocol 3: Stepped-Wedge Cluster Randomized Trial for Implementation Measure Validation

Objective: To evaluate the implementation of a new measure across multiple sites using a sequential rollout design.

Background: Stepped-wedge designs are particularly useful in implementation science because they ensure all participants eventually receive the potentially beneficial strategy while providing robust data on its impact over time [65].

Methodology:

• Cluster Identification: Identify 6-8 clusters (e.g., clinics, practice groups) for participation in the trial.
• Randomization: Randomize the order in which clusters receive the implementation strategy for the new measure.
• Sequential Rollout: Implement the measure in clusters at regular intervals (e.g., every 2 months), with all clusters starting in the control condition and transitioning to the intervention condition according to the randomized sequence.
• Data Collection: Collect data at multiple time points from all clusters throughout the study period, including:
  • Baseline data before any clusters receive the intervention
  • Transition data during the implementation phase for each cluster
  • Endpoint data after all clusters have received the intervention
• Outcome Assessment: Evaluate both measure performance outcomes (reliability, validity, sensitivity) and implementation outcomes (adoption, appropriateness, feasibility).

Outcome Measures: Measure performance metrics across implementation phases; Implementation outcomes by cluster and over time; Contextual factors influencing implementation success; Stakeholder satisfaction with the measure.

Data Presentation and Visualization Guidelines

Effective data presentation is crucial for communicating validation study findings to diverse audiences. The table below summarizes appropriate visualization approaches for different types of validation data.

Table 4: Data Visualization Approaches for Validation Studies

Data Type	Recommended Visualizations	Application in Validation Studies	Considerations
Stakeholder Prioritization	Bar charts, Pie charts	Display relative importance of pragmatic criteria [66]	Use with limited categories; show clear patterns [66]
Longitudinal Performance	Line graphs, Area charts	Track measure performance across implementation phases [66]	Show trends and fluctuations over time [66]
Comparative Analysis	Bar charts, Tables	Compare measure performance across sites or stakeholder groups [66] [67]	Facilitate detailed comparisons between data points [67]
Distribution Data	Histograms, Box plots	Display distribution of scores or response patterns [66]	Show frequency within intervals; identify outliers [66]
Structured Information	Tables with clear headers	Present detailed psychometric properties or protocol details [67]	Organize information for quick reference and comparison [67]

When presenting data in tables, apply these formatting guidelines to enhance readability:

• Use clear and consistent titles, subtitles, and column headers [67]
• Apply gridlines sparingly to avoid visual clutter [67]
• Align data appropriately (numeric data right-aligned, text left-aligned) [67]
• Format numbers for readability using thousand separators [67]
• Provide units of measurement in column headers or separate rows [67]
• Consider alternating row shading to improve readability [67]
• Group related data together visually using spacing or background colors [67]

Designing robust validation studies for implementation measures requires careful consideration of both scientific rigor and practical applicability. By employing the appropriate methodological approaches—from descriptive research to randomized controlled trials—and incorporating the PAPERS framework's pragmatic criteria, researchers can develop measures that are not only psychometrically sound but also feasible and useful in real-world practice settings. The protocols outlined in this document provide structured methodologies for generating the evidence needed to support the use of implementation measures across diverse healthcare contexts and stakeholder groups.

Application Notes

Background and Public Health Significance

The high prevalence of Opioid Use Disorders (OUD) among jail populations, coupled with an exceptionally high risk of fatal overdose in the weeks following release, presents a public health crisis of considerable magnitude. [68] [69] For nearly two decades, research has confirmed that individuals in jail settings are highly susceptible to fatal overdose, with risk of death more than 12 times higher than for individuals with OUD in the general population within the first two weeks post-release. [68] This creates a critical implementation opportunity for Medications for Opioid Use Disorder (MOUD), which are considered the gold standard treatment. [68] Despite this need, a significant treatment gap persists, with 56% of jails not providing MOUD, creating a pressing need for better implementation approaches in jail and the hand-off to the community. [68] [69]

Jails offer a particularly strategic setting for MOUD implementation due to their local control, short-term stays, high turnover, and nearly 11 million admissions annually, resulting in more frequent individual contact than longer-term state prisons. [68] The implementation gap in administering MOUD encounters multiple barriers, including stigmatization of substance use disorders, funding limitations, institutional design constraints, variable leadership support, restrictive policies, and communication barriers regarding MOUD effectiveness. [68]

Comparative Effectiveness Outcomes

A national randomized controlled trial directly compared two implementation strategies: NIATx external coaching and the Extension for Community Healthcare Outcomes (ECHO) model. [68] [69] The study employed a 2×2 factorial design across 25 jails and 13 community-based partners, comparing high- and low-dose coaching with and without ECHO over a 12-month intervention period followed by a 12-month sustainability phase. [68]

Table 1: Primary Quantitative Outcomes from Comparative Trial

Outcome Measure	NIATx Coaching	ECHO Model	Statistical Significance
Buprenorphine Use	Significant increase	No significant increase	p < 0.01 [68]
Combined MOUD Use	Significant increase (47.44% intervention phase; 7.30% sustainability)	No significant increase	p < 0.01 [68] [69]
Methadone Use	No consistent, significant gains	No consistent, significant gains	Not Significant [68]
Injectable Naltrexone	No consistent, significant gains	No consistent, significant gains	Not Significant [68]
Overall MOUD Use	Greater gains with high-dose coaching	No significant increase compared to coaching	p = 0.517 [68]

The trial demonstrated that coaching emerged as a more effective implementation strategy than ECHO for increasing buprenorphine use in jail settings. [68] [69] While high-dose coaching showed greater gains for MOUDs overall compared to low-dose coaching, these differences were statistically insignificant (p = 0.124), suggesting low-dose coaching may be more economical. [68] In practice, ECHO sessions offered considerable overlap with coaching strategies but did not significantly increase MOUD use compared to coaching across medication types during the intervention phase. [68] [69]

Implications for Implementation Science

This comparative effectiveness research provides pragmatic measures for implementation science in criminal justice settings. The findings suggest that organizational coaching focused on process improvement more effectively addresses the complex barriers to MOUD implementation in jails than knowledge-building approaches alone. [68] The NIATx model's focus on goal-setting and change management proved particularly effective for navigating justice system constraints, where challenges often involve balancing security with treatment and addressing service delivery issues. [68]

The minimal difference between high- and low-dose coaching intensities offers important economic insights for implementation science. The finding that low-dose coaching may be more economical without significantly compromising effectiveness provides practical guidance for resource allocation decisions in implementation efforts. [68] Furthermore, the sustainability phase outcomes, which showed a 7.30% increase in combined MOUD use following the active intervention period, contribute to understanding the maintenance of implemented practices. [68]

Experimental Protocols

Study Design and Randomization

The protocol employed a 2×2 factorial design with random assignment to one of four study arms: [68] [70]

• High-Dose NIATx Coaching & ECHO
• Low-Dose NIATx Coaching & ECHO
• High-Dose NIATx Coaching Only
• Low-Dose NIATx Coaching Only

The trial was conducted with a national sample of 48 sites, including county jails and community-based treatment providers (CBTPs) that collaborated with the jails. [68] [70] Jails were recruited through national networks including the Justice Community Opioid Innovation Network (JCOIN) and the Bureau of Justice Assistance (BJA), with consideration for diversity based on population size, geographic location, and gender. [68] The intervention period lasted 12 months, with an additional 12-month sustainability phase to assess maintenance of effects. [68] [70]

Diagram 1: Experimental Design Workflow

NIATx Coaching Intervention Protocol

Objective: To provide organizational coaching using the NIATx process improvement model as a change management framework to overcome implementation barriers. [68]

Personnel: Six trained NIATx coaches with expertise in MOUD implementation and organizational change, all possessing at least 15 years of experience providing NIATx coaching. [68]

Dosage Structure:

• High-Dose: 12 monthly coaching calls (one hour each)
• Low-Dose: 4 quarterly coaching calls (one hour each) [68]

Procedural Details:

• Coaches were assigned to each jail and their associated community-based treatment providers (0-5 CBTPs)
• Coaching calls were conducted virtually via Zoom and recorded for fidelity
• Content focused on:
  • Goal setting for MOUD implementation
  • Identifying and addressing implementation barriers
  • Change management strategies
  • Process improvement techniques [68]

Mechanism of Action: The coaching strategy focuses on developing internal expertise and providing social support to facilitate organizational change, with particular effectiveness noted in justice settings where it addresses the balance between security concerns and treatment provision. [68]

ECHO Intervention Protocol

Objective: To build clinician capacity to adopt and perform MOUD practices through telementoring and case consultation. [68]

Model Structure: The adapted ECHO model began with intensive didactic training in MOUD treatment, followed by a series of monthly tele-video sessions (rather than the traditional weekly sessions). [68]

Session Components:

• Didactic Presentation: Expert-led education on specific MOUD topics
• Case Conferencing: Discussion of real cases from participating sites
• Question-and-Answer Session: Interactive dialogue between clinicians and subject matter experts [68]

Mechanism of Action: ECHO aims to enhance MOUD providers' knowledge and self-efficacy to increase confidence in using MOUD, operating primarily through knowledge transfer and expert consultation rather than organizational change. [68]

Data Collection and Outcome Measures

Table 2: Core Outcome Measures and Assessment Methods

Domain	Specific Measures	Data Collection Method	Timing
MOUD Utilization	New patient counts for buprenorphine, methadone, injectable naltrexone, combined MOUD	Administrative data extraction	Monthly for 24 months
Clinical Outcomes	Initiation and engagement rates for eligible justice-involved persons	Electronic health record review	Monthly for 24 months
Provider Outcomes	Percentage of clinicians using MOUD; Organizational readiness and climate	Staff surveys; Organizational assessments	Baseline, 12 months, 24 months
Justice Outcomes	Recidivism rates	Criminal justice administrative data	12 months post-release
Implementation Outcomes	Sustainability and fidelity of interventions	Fidelity checks; Implementation logs	Ongoing throughout study

The primary outcomes included the percentage of eligible justice-involved persons who were initiated onto any MOUD (buprenorphine, extended-release injectable naltrexone, or methadone) and engaged with MOUD use. [70] Secondary outcomes included clinician utilization rates, recidivism, organizational readiness, and sustainability measures. [70]

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Materials and Methodological Components

Tool/Component	Function/Description	Application in Current Study
NIATx Coaching Manual	Structured guide for coaching sessions focusing on process improvement and change management	Provided framework for monthly or quarterly coaching calls; ensured intervention fidelity [68]
ECHO Curriculum Modules	Didactic materials covering MOUD pharmacotherapy, justice settings, implementation strategies	Formed core educational content for ECHO sessions; standardized knowledge transfer [68]
MOUD Fidelity Scale	Assessment tool measuring adherence to evidence-based MOUD practices	Evaluated implementation quality across sites; measured intervention fidelity [68]
Organizational Readiness Tool	Validated instrument assessing organizational climate and readiness for change	Measured baseline capacity and monitored change over time [70]
Implementation Cost Log	Structured template for documenting resource utilization and costs	Enabled economic analysis comparing high vs. low-dose coaching [68]

Diagram 2: Implementation Strategy Logic Model

In implementation science, the meticulous study of adaptations—defined as thoughtful and deliberate alterations to the design or delivery of an intervention to improve its fit or effectiveness in a given context—has emerged as a critical frontier for enhancing real-world impact [51] [71]. A significant methodological gap persists in pragmatically linking these systematic modifications to outcomes across the implementation cascade. The central challenge lies in moving beyond mere documentation of what was changed, toward rigorously analyzing how specific adaptations influence proximal implementation outcomes (e.g., feasibility, acceptability) and subsequent distal outcomes (e.g., sustainment, health equity) [51] [72]. This protocol provides a structured, actionable framework for researchers aiming to develop pragmatic measures that precisely connect adaptation characteristics to their multi-level effects, thereby advancing the methodological rigor of implementation science.

Core Principles and Foundational Frameworks

Defining the Adaptation Construct

The foundation of any adaptation impact analysis is the precise operationalization of the adaptation construct itself. Study teams must delineate what constitutes an adaptation within their specific research context, moving beyond broad definitions to specific, measurable characteristics [51] [72]. We conceptualize adaptations as any planned or unplanned change to the intervention or implementation strategy that occurs before, during, or after implementation [51]. An adaptation study may be a primary, stand-alone investigation or an add-on component to a larger implementation trial [51].

When operationalizing adaptations for measurement, seven key aspects provide a pragmatic foundation for data collection, especially when resources are limited or adaptations are numerous [51] [72]:

• What was adapted: Specific components, activities, or protocol elements that were modified.
• Focus of adaptation: Whether the change targeted the core intervention, implementation strategies, or context.
• Purpose/rationale: The explicit goal (e.g., to enhance reach, improve equity, increase feasibility).
• Timing and sequence: When the adaptation occurred relative to implementation phases.
• Bundling: Whether the adaptation was implemented alongside other modifications.
• Scope and exposure: The proportion of participants or settings affected by the change.
• Planning nature: Whether the adaptation was proactively planned or reactively emergent.

Theoretical Foundations: FRAME, MADI, and PRISM

Several frameworks provide structured approaches for classifying adaptations and hypothesizing their effects. The Framework for Reporting Adaptations and Modifications-Enhanced (FRAME) and FRAME-Implementation Strategies (FRAME-IS) are comprehensive systems for systematically characterizing what is modified, the nature of the modification, and the goal of the change [51] [71]. The Model for Adaptation Design and Impact (MADI) builds upon these frameworks to guide researchers in creating explanatory models for how adaptations impact outcomes through various mechanisms [51] [72]. Furthermore, the Practical, Robust Implementation and Sustainability Model (PRISM) integrates multilevel contextual domains with RE-AIM outcomes to tailor iterative adaptations based on implementation priorities and progress [51].

Table 1: Foundational Frameworks for Adaptation Analysis

Framework	Primary Function	Key Constructs Measured	Use Case
FRAME/FRAME-IS	Adaptation Classification & Documentation	What was modified, nature, reason, timing, who decided [51]	Systematic tracking and reporting of adaptations
MADI	Impact Modeling & Hypothesis Generation	Intended/unintended effects, mediators, moderators, outcomes [51]	Explaining causal pathways from adaptation to outcome
PRISM/RE-AIM	Outcome-Driven Adaptation	Reach, Effectiveness, Adoption, Implementation, Maintenance [51]	Guiding iterative adaptations based on implementation progress
3x3 Matrix Model	Simple Categorization	Focus (intervention/strategy/context) x Timing (pre/active/sustainment) [71]	Initial, high-level mapping of adaptation types

Application Notes and Experimental Protocols

Protocol 1: Prospective Tracking and Classification of Adaptations

Objective: To systematically document, classify, and prioritize adaptations throughout the implementation lifecycle.

Materials & Procedures:

• Establish Tracking System: Create a structured adaptation log aligned with FRAME or FRAME-IS constructs. This can be integrated into regular team meetings (e.g., as a standing agenda item) or implementation facilitator reports [51] [71].
• Define Frequency: Determine data collection intervals based on project pace (e.g., weekly during active implementation, monthly during sustainment) [51].
• Multi-Method Assessment: Combine quantitative tracking with qualitative methods (e.g., stakeholder interviews, observations) to capture nuanced adaptations and rationales [51] [72].
• Prioritization: When numerous adaptations occur, prioritize those affecting core intervention/strategy functions (vs. peripheral forms) and those hypothesized to significantly impact outcomes [51] [72].

Analysis: Conduct descriptive analysis of adaptation characteristics (type, frequency, timing). Use content analysis for qualitative data on rationales. Create a summary table of prioritized adaptations for impact analysis.

Protocol 2: Linking Adaptations to Proximal and Distal Outcomes

Objective: To analyze relationships between specific adaptations and subsequent implementation and effectiveness outcomes.

Materials & Procedures:

• Specify Outcome Matrix: For each prioritized adaptation, complete an outcome specification table (see Table 2) defining expected proximal and distal outcomes [51] [72].
• Measure Proximal Outcomes: Collect data on immediate effects (e.g., provider acceptability, cost, feasibility) closely following the adaptation.
• Measure Distal Outcomes: Continue tracking longer-term outcomes (e.g., sustainment, penetration, health equity, client outcomes) at predetermined intervals [51].
• Document Unintended Consequences: Actively monitor for both positive and negative unintended effects across the implementation ecosystem [51].

Analysis: Employ analytic techniques ranging from comparative analysis (e.g., pre/post adaptation) to multivariate modeling, considering timing, bundling, and contextual moderators [51]. Qualitative comparative analysis (QCA) can be useful for examining complex causal pathways.

Table 2: Adaptation Outcome Specification Template

Adaptation Description	Intended/ Unintended	Equity-Relevant? (Y/N)	Proximal Outcome(s) (e.g., Acceptability, Feasibility)	Distal Outcome(s) (e.g., Sustainment, Health Equity)	Hypothesized Mechanism
Example: Simplified patient materials for low-literacy populations	Intended	Y	Provider Perceived Feasibility Patient Understanding	Increased Reach Reduced Disparities in Engagement	Enhanced comprehensibility reduces barrier to engagement
Example: Shift from group to individual sessions due to space constraints	Unplanned	N	Increased Facilitator Time/Cost *Maintained Fidelity to Core Components	Potential Reduction in Program Capacity Possible Enhanced Participant Outcomes	Individualized attention may improve effectiveness but reduce efficiency

Protocol 3: Iterative Guidance of Adaptations Using Implementation Outcomes

Objective: To use real-time implementation data to proactively guide and inform necessary adaptations.

Materials & Procedures:

• Select Priority Outcomes: Identify 2-3 key RE-AIM or other implementation outcomes (e.g., Reach, Adoption) most critical to project success [51] [71].
• Establish Feedback Loops: Create rapid-cycle data systems to regularly report on these outcomes to implementers and stakeholders.
• Structured Adaptation Planning: Use data on lagging outcomes to trigger structured discussions about potential adaptations. The PRISM model is particularly useful here [51].
• Pilot and Re-evaluate: Test adaptations on a small scale and monitor their effect on proximal outcomes before full-scale implementation [71].

Analysis: Focus on trend analysis of implementation outcomes over time, correlating the timing of specific adaptations with changes in outcome trajectories.

Visualization of Adaptation-Impact Pathways

The following diagram illustrates the core conceptual workflow for analyzing the impact of adaptations, from systematic documentation through to outcome evaluation, highlighting key decision points.

Conceptual Workflow for Analyzing Adaptation Impact

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Methodological Reagents for Adaptation Research

Tool/Reagent	Function	Application Notes
Structured Adaptation Log	Standardized documentation of adaptations in real-time.	Integrate into existing meeting structures. Use FRAME constructs as column headers. [51]
FRAME/FRAME-IS Coding Guide	Systematic classification of adaptation characteristics.	Train coders for reliability. Adapt modules to research questions. [51] [71]
Outcome Specification Template	Links specific adaptations to hypothesized proximal/distal outcomes.	Complete for each prioritized adaptation before impact analysis. [51] [72]
Stakeholder Interview Guide	Elicits undiscovered adaptations and contextual rationale.	Include perspectives from multiple stakeholder levels (leadership, staff, recipients). [51]
RE-AIM or PRISM Metrics	Tracks key implementation outcomes to guide iterative adaptations.	Select 2-3 high-priority outcomes for rapid-cycle feedback. [51] [71]
Qualitative Comparative Analysis (QCA)	Analyzes complex, contingent causal pathways for outcomes.	Suitable for studies with multiple cases/sites and bundled adaptations. [51]

This protocol provides a comprehensive methodological pathway for rigorously connecting specific adaptations to their multi-level outcomes. By systematically defining the adaptation construct, prospectively tracking modifications using established frameworks, explicitly specifying hypothesized outcome pathways, and selecting analytic methods suited to the complexity of implementation contexts, researchers can significantly advance the pragmatic measurement of adaptation impacts. The resulting evidence is critical for distinguishing between adaptations that enhance fit and effectiveness versus those that potentially undermine an intervention's core active ingredients, ultimately enabling more effective, equitable, and sustainable implementation in real-world settings.

Within implementation science, the systematic evaluation of strategy dosage—defined as the intensity, frequency, and duration of an implementation strategy—is critical for understanding its mechanism and effect on outcomes [73]. Coaching is a widely used but heterogeneously applied implementation strategy; clarifying the dosage of different coaching models is essential for developing pragmatic measures that are useful, compatible, and easy to use in real-world settings [74]. This protocol provides a structured approach for comparing high- and low-intensity coaching models, detailing applicable pragmatic measures and methodologies for assessing their impact on implementation outcomes.

Theoretical Framework and Definitions

Conceptualizing Coaching Dosage

Coaching dosage encompasses more than just contact hours. This framework breaks it down into three interdependent dimensions:

• Intensity: The resources, specialization, and effort required per coaching unit. High-intensity coaching often involves specialized, one-on-one, in-depth support, whereas low-intensity coaching may use group sessions or standardized materials [73] [75].
• Frequency: The rate of coaching sessions over a defined period.
• Duration: The total timespan of the coaching support.

High- vs. Low-Intensity Coaching Models

The distinction between high- and low-intensity coaching often lies in their theoretical foundations and operationalization:

• High-Intensity Models often align with a facilitation-based approach, characterized by a close, collaborative partnership. The coach acts as a "boundary spanner," customizing support and linking inner organizational and outer system contexts to enable implementation and sustainment [76] [73]. These models are typically more resource-heavy.
• Low-Intensity Models may lean toward a structured, goal-oriented approach, utilizing standardized tools, centralized technical assistance, and feedback systems with less individual customization [76].

Table 1: Core Characteristics of High- and Low-Intensity Coaching Models

Characteristic	High-Intensity Coaching	Low-Intensity Coaching
Theoretical Basis	Facilitation/Process-Oriented [75]	Goal-/Outcome-Oriented [75]
Primary Style	Facilitator, adapting to team maturity [76]	Formal Authority, Expert [76]
Relationship	Personal, collaborative partnership [73]	Structured, standardized support [76]
Customization	High, tailored to context and needs [73]	Low to moderate, more standardized
Key Function	Boundary spanning, enabling implementation [73]	Fidelity support, technical assistance [76]

Application Notes: Measuring Dosage and Outcomes

Pragmatic Measures for Coaching

Evaluating coaching strategies requires measures that are psychometrically sound and pragmatic—defined as useful, compatible, acceptable, and easy for stakeholders [74]. The Psychometric and Pragmatic Evidence Rating Scale (PAPERS) was developed through stakeholder-driven processes to assess these qualities in implementation measures [74]. When selecting measures, consider their pragmatic rating alongside psychometric properties to ensure feasibility in real-world settings.

Quantifiable Dosage Metrics

To standardize the reporting of coaching dosage, track the following metrics:

Table 2: Quantifiable Metrics for Coaching Strategy Dosage

Dosage Dimension	Specific Metrics	Data Collection Method
Intensity	- Coach-to-staff ratio- Coach expertise level- Session customization level	Administrative records; Session ratings
Frequency	- Number of sessions per week/month- Consistency of schedule	Coaching logs; Meeting calendars
Duration	- Length of single session (minutes)- Total program lifespan (months)	Session timestamps; Project records
Cumulative Dose	- Total contact hours per participant	Calculated from logs (Frequency x Duration)

Linking Dosage to Outcomes

Coaching impacts outcomes across multiple levels. The following framework, adapted from training and implementation science, categorizes these outcomes and suggests potential measures [75].

Table 3: Outcome Measures for Evaluating Coaching Effectiveness

Outcome Level	Definition	Example Measures	Relevant Coaching Intensity
Affective Outcomes	Changes in attitudes, motivation, self-efficacy [75]	- Implementation Climate Scale- Self-Efficacy Questionnaires	High-intensity coaching may have stronger effects due to tailored support [73].
Cognitive Outcomes	Acquisition of knowledge and problem-solving strategies [75]	- Knowledge tests- Conceptual mapping exercises	Both models can be effective, depending on content delivery.
Skill-Based Outcomes	Acquisition, mastery, and automaticity of new skills [75]	- Fidelity scores- Direct observation checklists	High-intensity with in-vivo observation may be superior [73].
Behavioral Outcomes	Observable changes in workplace behavior [75]	- Audit and feedback reports- Supervisor ratings	Both models can drive change; high-intensity may accelerate it.
Organizational Results	System-level changes and sustainment [75]	- EBI Sustainment rates- Program penetration	High-intensity models may better address systemic barriers [73]. ```

Experimental Protocols for Comparative Evaluation

Protocol 1: Mixed-Methods Comparison of Coaching Models

Aim: To quantitatively and qualitatively compare the processes and outcomes of high- and low-intensity coaching models supporting the same Evidence-Based Intervention (EBI).

Methodology:

• Design: A cluster-randomized or quasi-experimental design, assigning teams or organizations to either high- or low-intensity coaching arms.
• Participants: Implementation teams from community-based or healthcare organizations.
• Interventions:
  • High-Intensity Arm: Coaching based on a facilitation model. Coaches meet with teams frequently (e.g., weekly/bi-weekly) for 60-minute sessions. Support is highly customized, using facilitative questioning and active support for problem-solving [76] [73].
  • Low-Intensity Arm: Coaching based on a standardized support model. Coaches meet less frequently (e.g., monthly) for 30-minute sessions, using structured tools and providing centralized technical assistance and feedback [76].
• Data Collection:
  • Quantitative: Collect dosage metrics (Table 2) and outcome measures (Table 3) at baseline, mid-point, and post-intervention.
  • Qualitative: Record and transcribe coaching sessions. Apply a framework like the adapted Grasha-Riechmann model to code for coaching styles (e.g., Facilitator, Formal Authority, Expert) and track how these styles evolve through preparation, implementation, and sustainment phases [76].
• Analysis: Use linear mixed models to compare quantitative outcomes between arms. Conduct thematic analysis on qualitative data to understand the "active ingredients" and contextual factors influencing effectiveness.

Protocol 2: Assessing Pragmatic Measures in Coaching

Aim: To evaluate the pragmatic qualities of implementation outcome measures when used in the context of a coaching strategy.

Methodology:

• Design: Longitudinal observational study embedded within a coaching initiative.
• Participants: Coaches and implementation staff participating in the initiative.
• Procedures: Implement a battery of implementation outcome measures (e.g., acceptability, appropriateness, feasibility of the EBI) at multiple timepoints.
• Evaluation: Upon study completion, administer the PAPERS criteria to all measures used [74]. Additionally, conduct focus groups with coaches and staff to gather stakeholder perspectives on the measures' pragmatism, exploring themes such as:
  • Usefulness: Did the measures provide actionable data?
  • Compatibility: Did they fit well with workflow?
  • Ease of use: What was the perceived burden? [74]
• Analysis: Calculate PAPERS scores for each measure. Analyze focus group data to identify stakeholder-driven themes on pragmatism, which may include concerns about bias, the need for a holistic approach, and the importance of incorporating diverse perspectives [1].

The Scientist's Toolkit: Diagrams and Reagents

Logical Workflow for Coaching Strategy Evaluation

The following diagram visualizes the logical workflow and key decision points for planning an evaluation of coaching strategy dosage, integrating the EPIS framework phases.

Key Research Reagent Solutions

This table details essential "research reagents"—the core tools and materials required to conduct rigorous studies on coaching dosage.

Table 4: Essential Reagents for Coaching Dosage Research

Item/Category	Function in Research	Exemplars & Notes
Coaching Session Coding Framework	To qualitatively classify and quantify coaching styles and interactions over time.	Adapted Grasha-Riechmann Framework (e.g., Facilitator, Formal Authority, Expert styles) [76].
Pragmatic Measure Rating Tool	To evaluate the usability and feasibility of implementation measures in real-world coaching contexts.	Psychometric and Pragmatic Evidence Rating Scale (PAPERS) [74].
Dosage & Fidelity Tracking System	To systematically record the intensity, frequency, and duration of coaching delivered and received.	Standardized logging templates (electronic or paper) for coaches; key metrics outlined in Table 2.
Implementation Outcome Measure Battery	To assess the multi-level effects of coaching dosage.	Validated scales measuring acceptability, appropriateness, feasibility, fidelity, and sustainment [5].
Stakeholder Engagement Panel	To ensure the research design and measures are relevant and pragmatic from multiple perspectives.	A group comprising coaches, implementation staff, and service users to guide the research [1].

Coaching as a Boundary-Spanning Strategy

The next diagram illustrates the pivotal role of the coach in bridging the inner context (organization) and outer context (broader system), a key mechanism through which dosage influences implementation success [73].

The Role of Hybrid Trials in Simultaneously Assessing Effectiveness and Implementation Success

Hybrid effectiveness-implementation trials represent a transformative approach in clinical and public health research, designed to accelerate the translation of evidence-based interventions into routine practice. Traditional randomized controlled trials (RCTs), while considered the gold standard for establishing causal inferences, often suffer from significant limitations including prolonged timelines and substantial research-to-practice gaps [77]. The conventional staged approach, which focuses first on establishing efficacy under ideal conditions before considering real-world implementation, creates an unacceptable time lag between evidence generation and widespread clinical adoption [78]. Hybrid trials address this critical bottleneck by simultaneously investigating both clinical effectiveness and implementation strategies within a single study framework.

The conceptual foundation for hybrid trials was formally established to bridge the divide between highly controlled clinical research and the complex environments where care is actually delivered [78]. These trials recognize that healthcare systems are complex adaptive systems, and understanding the influence of situational context is equally as important as establishing clinical efficacy, even while the evidence base is being developed [77]. By integrating these complementary aims, hybrid designs multiply the amount of learning that can come from a trial without proportionately increasing costs, answering broader questions than those related to effectiveness alone [77].

Hybrid Trial Typologies and Characteristics

Defining the Three Hybrid Types

Hybrid trials exist on a continuum and are categorized into three distinct types based on their primary focus and the relative emphasis on effectiveness versus implementation outcomes [77]. Each type serves different research purposes and addresses different stages in the intervention development and implementation pathway.

Type 1 Hybrid Trials primarily focus on intervention effectiveness outcomes while concurrently exploring the context for future implementation [78] [77]. In this design, the clinical effectiveness aim remains paramount, but researchers gather preliminary data on implementation barriers, facilitators, and potential strategies that could inform future dissemination efforts. This approach is particularly valuable when there is already some evidence of efficacy but understanding real-world contextual factors is necessary before broader scale-up.

Type 2 Hybrid Trials maintain a dual focus, with co-primary aims assessing both intervention effectiveness and implementation outcomes [77]. These trials simultaneously investigate whether an intervention works and how best to implement it, testing both the clinical intervention and specific implementation strategies. This design is optimal when there is stronger preliminary evidence for effectiveness, but significant questions remain about optimal implementation approaches.

Type 3 Hybrid Trials primarily focus on implementation outcomes while secondarily exploring clinical effectiveness [77] [79]. These designs are employed when effectiveness is already well-established, and the primary research question concerns how best to integrate the intervention into routine care. The secondary effectiveness aim typically examines how clinical outcomes relate to implementation fidelity, uptake, and integration within real-world settings.

Table 1: Comparison of Hybrid Trial Types and Traditional Designs

Design Aspect	Effectiveness RCT	Hybrid Type 1	Hybrid Type 2	Hybrid Type 3	Implementation Study
Primary Aim	Determine effectiveness of an intervention	Determine effectiveness while exploring implementation context	Dual: Determine effectiveness AND assess implementation strategy	Determine impact of implementation strategy while exploring clinical outcomes	Determine impact of implementation strategy
Units of Randomization	Individual or Cluster	Individual or Cluster	Individual or Cluster	Cluster (typically)	Cluster (typically)
Comparison Conditions	Placebo, treatment as usual, competing intervention	Placebo, treatment as usual, competing intervention	Placebo, treatment as usual, competing intervention	Historical practice or treatment as usual	Historical practice or treatment as usual
Population Framework	Single population with strict inclusion/exclusion	Two populations: primary with strict criteria; secondary (implementers)	Two populations: including both recipients and implementers	Two populations: primary system-level; secondary with strict criteria	Single population focusing on system level
Measurement & Outcomes	Quantitative clinical effectiveness ± cost	Primary: quantitative effectiveness; Secondary: mixed methods implementation context	Co-primary: clinical effectiveness AND implementation outcomes	Primary: implementation outcomes; Secondary: clinical effectiveness	Implementation outcomes only

Theoretical Foundations and Frameworks

The use of theoretical approaches, including theories, models, and frameworks (TMFs), is a critical element in designing robust hybrid trials. A recent scoping review of hybrid type 1 trials found that 76% cited at least one theoretical approach to guide their implementation components [78]. These TMFs provide critical understanding of the complex systems within which implementation occurs, offer explicit assumptions that can be tested and validated, and help connect findings across studies from various clinical settings [78].

The most commonly applied framework in hybrid trials is the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, utilized in 43% of hybrid type 1 trials according to the scoping review [78]. This framework helps researchers plan for and evaluate the public health impact of interventions across multiple dimensions. Other frequently used TMFs include process frameworks that outline implementation steps, determinant frameworks that explain influences on implementation outcomes, and evaluation frameworks that specify implementation outcomes of interest [77].

Theoretical approaches in hybrid trials are most often applied to justify implementation study design, guide selection of study materials, and analyze implementation outcomes [78]. When used systematically, these approaches accelerate future translation of evidence-based practices into routine care and optimize patient outcomes by providing insights into how interventions function within specific contexts.

Experimental Protocols for Hybrid Trials

Protocol Framework for Type 1 Hybrid Trials

Type 1 hybrid trials require meticulous planning to balance the primary effectiveness focus with systematic exploration of implementation context. The protocol begins with clearly defining dual aims: a primary aim focused on clinical effectiveness and a secondary aim examining implementation context [78] [77]. The sampling framework typically involves two populations: the primary patient population with strict inclusion/exclusion criteria, and secondary populations including clinicians, healthcare providers, or other stakeholders who can provide insights into future implementation.

Measurement strategies in Type 1 designs combine quantitative clinical effectiveness measures with mixed methods approaches (interviews, surveys, audits) to assess feasibility, barriers/enablers to implementation, acceptability of the intervention, and sustainability potential [77]. For example, a Type 1 trial might randomize patients to receive either a new clinical intervention or usual care while concurrently surveying providers about intervention acceptability and observing system-level factors that might influence future implementation.

The implementation context exploration typically focuses on identifying barriers and facilitators to sustainable implementation of the clinical intervention [78]. This includes assessing organizational readiness, resource requirements, workforce capabilities, and potential adaptations needed for different settings. Data collection for implementation components often occurs throughout the trial period but may be concentrated at specific timepoints to capture evolving perspectives as stakeholders gain experience with the intervention.

Protocol Framework for Type 2 Hybrid Trials

Type 2 hybrid trials employ a more complex protocol with co-primary aims that receive equal emphasis. The protocol must specify rigorous methods for both effectiveness and implementation questions, often requiring expertise in both clinical trials methodology and implementation science [77]. These trials frequently use cluster randomization designs where units such as clinics, hospitals, or healthcare systems are randomized to different implementation strategies while still collecting patient-level effectiveness data.

The implementation component in Type 2 trials typically tests specific implementation strategies, such as educational outreach, coaching, facilitation, audit and feedback, or clinical decision support systems [77]. The protocol should clearly define these strategies using standardized terminology and specify their theoretical basis, core components, and adaptation potential. Measurement includes both implementation outcomes (acceptability, adoption, fidelity, cost) and clinical effectiveness outcomes, with careful attention to temporal relationships between implementation processes and clinical effects.

An exemplar Type 2 protocol is illustrated in a study testing the "Beliefs and Attitudes for Successful Implementation in Schools for Teachers (BASIS-T)" strategy, which targets volitional and motivational mechanisms of educator behavior change [79]. This protocol employs a blocked randomized cohort design with an active comparison control condition, recruiting 276 teachers from 46 schools to evaluate main effects on both implementation mechanisms and student outcomes.

Protocol Framework for Type 3 Hybrid Trials

Type 3 hybrid trials prioritize implementation aims while collecting clinical effectiveness data to understand how implementation quality influences outcomes. These protocols typically employ cluster randomized or stepped-wedge designs where the unit of randomization is the implementation site [77]. The primary focus is on testing implementation strategies, with clinical effectiveness data often collected through subsamples of patients, medical record review, or administrative data to reduce measurement burden [77].

The protocol for a Type 3 trial explicitly defines the evidence-based practice being implemented and specifies the implementation strategies being tested. For example, a hybrid Type 3 trial of the "Building Better Caregivers" online workshop for rural dementia caregivers uses the RE-AIM framework to guide evaluation across multiple dimensions including Reach, Effectiveness, Adoption, Implementation, and Maintenance [80]. This protocol combines a randomized controlled trial for effectiveness assessment with mixed methods to evaluate implementation outcomes under real-world conditions.

Type 3 protocols pay particular attention to contextual factors that influence implementation success and often include rigorous process evaluations to understand how and why implementation strategies work or fail in different settings. These protocols typically plan for iterative adaptations to implementation approaches based on ongoing data collection, balancing fidelity to core implementation strategy elements with necessary contextual adaptations.

Data Presentation and Analysis Approaches

Quantitative Data Framework

Hybrid trials generate complex quantitative data spanning both clinical effectiveness and implementation outcomes. Systematic organization of these data is essential for clear interpretation and reporting. The following table summarizes key outcome domains and representative measures for hybrid trials:

Table 2: Outcome Measures for Hybrid Trials

Domain	Specific Outcomes	Representative Measures	Data Collection Methods
Implementation Outcomes	Acceptability, Adoption, Fidelity, Penetration/Reach, Sustainability	Acceptability of Intervention Measure (AIM), Fidelity checklists, Adoption rates, Penetration rates	Surveys, administrative data, direct observation, interviews
Clinical Effectiveness Outcomes	Patient-level health outcomes, Behavior change, Symptom improvement	Clinical symptom scales, Functional status measures, Behavioral assessments, Biomarkers	Patient surveys, clinical assessments, medical record review, laboratory tests
Implementation Mechanisms	Attitudes, Subjective norms, Self-efficacy, Intentions to implement	Theory of Planned Behavior constructs, Implementation Climate Scale	Provider surveys, focus groups, structured observations
Contextual Factors	Organizational readiness, Leadership engagement, Resource availability	Organizational Readiness for Change, Implementation Climate Scale	Key informant interviews, organizational surveys, document review

Analysis approaches for hybrid trials must account for the multi-level nature of the data, with clinical outcomes often nested within implementation contexts. Mixed effects models can appropriately handle clustering of patient outcomes within providers or sites, while mediation analyses can test hypothesized mechanisms linking implementation strategies to clinical outcomes [79]. Type 2 and 3 hybrid trials frequently employ mixed methods approaches, integrating quantitative and qualitative data to develop a comprehensive understanding of both whether interventions work and how they function in specific contexts.

Visualizing Hybrid Trial Workflows

The conceptual and operational workflows for hybrid trials can be effectively communicated through standardized diagrams that clarify the relationships between trial components, implementation strategies, and outcomes. The following DOT language scripts generate visual representations of these complex relationships:

Diagram Title: Hybrid Trial Continuum

Diagram Title: Hybrid Trial Protocol Workflow

The Scientist's Toolkit: Essential Research Reagents

Conducting rigorous hybrid trials requires specialized methodological resources and tools. The following table outlines essential "research reagents" for designing, implementing, and analyzing hybrid trials:

Table 3: Essential Research Reagents for Hybrid Trials

Research Reagent	Function/Purpose	Exemplars
Implementation Frameworks	Guide design, measurement, and interpretation of implementation components	RE-AIM, CFIR, Theoretical Domains Framework, Consolidated Framework for Implementation Research [78] [80]
Implementation Strategy Specifications	Define and operationalize specific implementation strategies	Strategy specification templates from Expert Recommendations for Implementing Change (ERIC), Implementation Research Logic Model [77] [79]
Implementation Outcome Measures	Assess implementation success across multiple dimensions	Acceptability of Intervention Measure (AIM), Fidelity checklists, Adoption rates, Sustainability measures [77]
Mixed Methods Integration Tools	Facilitate integration of quantitative and qualitative data	Joint displays, triangulation protocols, convergence coding matrix [78] [80]
Theory of Change Models	Articulate hypothesized causal pathways from strategies to outcomes	Logic models, process models, mechanism maps [79]
Context Assessment Tools	Evaluate organizational and system-level factors influencing implementation	Organizational Readiness for Change, Implementation Climate Scale, Inner/Outer Context assessments [79]

These research reagents provide the methodological infrastructure necessary to conduct rigorous hybrid trials. Their systematic application helps ensure that hybrid trials generate meaningful insights about both intervention effects and implementation processes, advancing the dual goals of establishing what works and how to make it work in real-world settings.

Hybrid effectiveness-implementation trials represent a paradigm shift in clinical research methodology, offering a powerful approach to accelerating the translation of evidence into practice. By simultaneously examining clinical effectiveness and implementation processes, these designs address critical bottlenecks in the traditional research pipeline that have delayed the delivery of evidence-based care to patients [77]. The three hybrid types provide flexible options for researchers based on the existing evidence for clinical interventions and the prominence of implementation questions.

As the field advances, methodological sophistication in hybrid trials continues to increase, with stronger theoretical grounding, more precise specification of implementation strategies, and more integrated mixed methods approaches [78]. Future directions include developing standardized reporting guidelines for hybrid trials, refining methods for adaptive hybrid designs that can respond to emerging findings, and creating funding mechanisms that support the complex interdisciplinary teams required for this work [77]. As one expert provocatively stated, "In the future, all 'good' trials will be hybrid, in some way" [77], reflecting the growing recognition that understanding implementation context is not secondary to establishing efficacy, but fundamental to realizing the public health impact of clinical interventions.

Conclusion

The development of truly pragmatic measures in implementation science demands a fundamental shift from top-down, exclusively expert-driven models to inclusive, stakeholder-engaged processes. As synthesized from the latest research, success hinges on integrating diverse perspectives from the outset, employing rigorous yet flexible methodological frameworks like case studies and MOST, and continuously validating measures through comparative effectiveness research. The future of biomedical and clinical research depends on this evolution. By embracing these approaches, researchers and drug development professionals can create implementation strategies and measures that are scientifically sound, contextually adept, and powerful enough to systematically close the agonizing 17-year gap between discovery and practice, ultimately ensuring that evidence-based interventions achieve their full public health potential.

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