Using Participatory Implementation Science to Advance Health Equity.

The purpose of this study was to complete a scoping review of implementation science (IS) research in communication sciences and disorders (CSD) over time and to determine characteristics of IS research in CSD. A scoping review was conducted of PubMed and Education Resources Information Center for sources published in English that (a) included CSD practitioners, (b) addressed IS research, and (c) identified a specific evidence-based practice. Resulting sources were systematically examined for study aim, patient populations, implementation framework utilized, setting of the study, implementation strategy examined, and implementation outcome measured. The majority of the 82 studies that underwent a full-text review (80.5%) were published in 2014 or later. One fourth of the studies were concept papers, and another one fourth focused on context assessment (25.6% of studies, each), 11% focused on designing implementation strategies, and 36.6% focused on testing implementation strategies. The patient population most frequently represented aphasia (21.3%), and most studies (34.4%) were conducted in inpatient medical settings. Nearly half (42.6%) of the nonconcept studies lacked an IS framework. Among implementation strategies identified, approximately one third of studies focused on education and/or training plus another strategy and one fourth focused on education and/or training alone. Implementation outcomes measured typically represented early stages of implementation. This scoping review of IS research in CSD described the landscape of IS studies in CSD. IS is intersecting with CSD at a rapid rate, especially since 2014. Future IS research in CSD should adopt an implementation framework a priori and consider the broad range of implementation strategies and outcomes to support the uptake of research into typical practice settings.

BackgroundDespite significant progress in HIV prevention and treatment, uptake of evidence-based interventions among adolescents and young adults (AYA), particularly in low- and middle-income countries (LMICs), remains low. Implementation research can optimize strategies to enhance reach, uptake, and equitable access to these innovations. The Prevention and Treatment through a Comprehensive Care Continuum for HIV-affected Adolescents in Resource-Constrained Settings Implementation Science Network (PATC3H-IN) leverages implementation science to strengthen the delivery and sustainability of evidence-based HIV prevention and care for AYA across six countries in sub-Saharan Africa. This paper outlines PATC3H-IN’s goals, summarizes the implementation science (IS) data that will be collected, and highlights the advantages of research networks in advancing science.MethodsThe PATC3H-IN builds on the existing PATC3H consortium to advance IS research targeting AYA in LMICs. The PATC3H-IN comprises eight Clinical Research Centers (CRCs) located in Nigeria, Uganda, Malawi, South Africa, Zambia, and Tanzania. Representatives from the CRCs were asked to provide information on the IS components of their proposed studies, including details on study populations, IS frameworks, outcomes, and strategies, mechanisms of change, effectiveness outcomes, and documentation of intervention adaptations. The reports from the CRCs were compared to identify opportunities for advancing science across study sites.ResultsThe PATC3H-IN studies will enroll AYA aged 12–24 years, with some emphasizing key subpopulations, namely AYA living with HIV, sexual and gender minorities, and adolescent girls and young women. All PATC3H-IN studies will be guided by one or more implementation science frameworks and theories, with the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework most frequently cited (n = 4/8). Across the CRCs, 54 unique implementation strategies will be used, with community engagement being the most common. Several studies will document intervention adaptations, and all studies will collect a set of common data elements to facilitate secondary data analyses across projects.ConclusionsThe PATC3H-IN represents a significant contribution to advancing HIV prevention and care research for AYA in resource-constrained settings. Findings from PATC3H-IN will extend our understanding of IS in sub-Saharan Africa, a region particularly burdened by HIV, and for AYA who are traditionally under-represented in IS research.Clinical trial numberNot Applicable.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12889-025-24995-0.

Integrating existing and novel methods to understand organizational context: A case study of an academic-public health department partnership

OBJECTIVES/GOALS: Clinical and Translational Science Award (CTSA) hubs are launching D&I Science cores to provide resources and services to promote the translation of clinical evidence into practice. We developed a D&I Science Core strategic plan reflecting our research community’s needs by assessing Implementation Science (IS) competencies. METHODS/STUDY POPULATION: The Tufts CTSI D&I Science Core was launched in early 2023. To design services that meet research community needs, we conducted a survey and key informant interviews based on Padek etal.’s list of Implementation Science (IS) competencies. The competencies are organized into four domains (Definition, Background, and Rationale; Theory and Approaches; Design & Analysis; and Practice-Based Considerations) and categorized by expertise level (beginner, intermediate, advanced). Participants who had attended or expressed interest in a D&I interest group were asked via an email survey to rate their level of confidence in completing selected IS-related research activities, about their experience with IS research or practice, and the types of resources, services and training they desired. RESULTS/ANTICIPATED RESULTS: Twenty researchers (20/65, 31%) submitted survey responses and six researchers participated in in-depth interviews. Survey respondents felt most confident in engaging stakeholders in IS research and least confident selecting a model or framework for a study. Results suggest that researcher capacity building is needed to: • Understand IS models and frameworks and their approaches, strengths, and limitations • Select and use models and frameworks in studies • Assemble IS teams and prepare grant proposals Suggestions for resources, services, and training, include: • Customized education to address diverse needs, knowledge levels, and learning styles • Promotion of D&I Core consultations and grant support services • Sharing of successful proposals to help researchers learn how to apply IS methods DISCUSSION/SIGNIFICANCE: A strategic workplan for the D&I Science Core was developed and implemented to address the findings. Initial emphasis is on developing easily accessible resources and timely consultations for investigators new to IS needing to apply these methods in current grant proposals, while also providing training resources for deeper skill building.

BackgroundThe impact of both implementation strategies (IS) and evidence-based interventions (EBI) can vary across contexts, and a better understanding of how and why this occurs presents fundamental but challenging questions that implementation science as a field will need to grapple with. We use causal epidemiologic methods to explore the mechanisms of why sharp distinctions between implementation strategies (IS) and efficacy of an evidence-based intervention (EBI) may fail to recognize that the effect of an EBI can be deeply intertwined and dependent on the context of the IS leading to its uptake.MethodsWe explore the use of instrumental variable (IV) analyses as a critical tool for implementation science methods to isolate three relevant quantities within the same intervention context when exposure to an implementation strategy is random: (1) the effect of an IS on implementation outcomes (e.g., uptake), (2) effect of EBI uptake on patient outcomes, and (3) overall effectiveness of the IS (i.e., ~ implementation*efficacy). We discuss the mechanisms by which an implementation strategy can alter the context, and therefore effect, of an EBI using the underlying IV assumptions. We illustrate these concepts using examples of the implementation of new ART initiation guidelines in Zambia and community-based masking programs in Bangladesh.ResultsCausal questions relevant to implementation science are answered at each stage of an IV analysis. The first stage assesses the effect of the IS (e.g., new guidelines) on EBI uptake (e.g., same-day treatment initiation). The second stage leverages the IS as an IV to estimate the complier average causal effect (CACE) of the EBI on patient outcomes (e.g., effect of same-day treatment initiation on viral suppression). The underlying assumptions of CACE formalize that the causal effect of EBI may differ in the context of a different IS because (1) the mechanisms by which individuals uptake an intervention may differ and (2) the subgroup of individuals who take up an EBI may differ. IV methods thus provide a conceptual framework for how IS and EBIs are linked and that the IS itself needs to be considered a critical contextual determinant. Moreover, it also provides rigorous methodologic tools to isolate the effect of an IS, EBI, and combined effect of the IS and EBI.DiscussionLeveraging IV methods when exposure to an implementation strategy is random helps to conceptualize the context-dependent nature of implementation strategies, EBIs, and patient outcomes. IV methods formalize that the causal effect of an EBI may be specific to the context of the implementation strategy used to promote uptake. This integration of implementation science concepts and theory with rigorous causal epidemiologic methods yields novel insights and provides important tools for exploring the next generation of questions related to mechanisms and context in implementation science.

Translational research: The ingredients are only the start of the recipe for better dietetic practice

The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant.Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.

Background: In recognition of the value and impact of holistic and patient-centred care, clinicians, patients, and healthcare organisations have advocated for practical changes to help integrate existing services. Technology is needed to enable the scale and speed of data organisation and sharing and communication needed to integrate clinical services and sites. Implementing complex or technology-supported interventions is difficult, and has high failure rates, as it requires changes to a number of embedded and interrelated processes, roles, and policies. Implementation Science (IS), which draws from a multidisciplinary base and has established theories, models, and frameworks (TMF) is increasingly used to improve research translation and efficiency. While there has been a widely reported rapid increase in the number of new TMF, including frameworks specific to either complex or technology-supported interventions, there is no single framework specific to the implementation of complex and technology-supported interventions (which are increasingly used). This paper describes the structured way that Realist Research, the Consolidated Framework for Implementation Science (CFIR), and the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework were combined, applied, and tested to plan, implement, and evaluate a complex technology-supported intervention to integrate existing services. Aim: The FIT in Integrated Care study aimed, as part of a multi-site Randomised Controlled Trial, to build on existing IS to co-design, test, and evaluate a structured, practical, and comprehensive way to plan, implement, and evaluate the implementation of a complex technology-supported intervention that integrates existing services. Methods: The FIT in Integrated Care was co-designed in three stages. The first stage defined the function that the new framework needed to fulfil and identified suitable IS theory and practice to inform its structure and content. The second stage involved developing the new framework (structure, content) and applying it to the GERONTE* intervention. Realist research, the CFIR, and the NASSS framework were chosen and combined because of their relevance (to IS and to complex and technology-supported interventions), evidence base, combined comprehensiveness, and compatibility. The new fr↔amework was refined based on end-user feedback. The third stage, which is ongoing, involves the broader and longer-term testing, evaluation, and reporting on the framework’s functionality and user-friendliness in implementing a complex technology-supported intervention to integrate existing care services. Results: The FIT in Integrated Care study resulted in the combination and application of existing IS theory and practice, into an evidenced-based, structured, practical, user-friendly multi-stakeholder framework to plan, implement, and evaluate the implementation of a complex technology-supported intervention that integrates existing services. This new framework focuses research and analysis on the ‘context, mechanism of action, and outcomes’ to enable understanding of ‘how, in what context and for who’ the intervention works (so as to support wider adoption and/ or adaptation). Conclusion: Combining Realist research, the CFIR, and NASSS framework provides an evidence-based, robust, user-friendly way to plan, implement, and evaluate a complex technology-supported intervention that integrates existing healthcare services.

BackgroundMany of society’s health problems require research-based knowledge acted on by healthcare practitioners together with implementation of political measures from governmental agencies. However, there has been limited knowledge exchange between implementation science and policy implementation research, which has been conducted since the early 1970s. Based on a narrative review of selective literature on implementation science and policy implementation research, the aim of this paper is to describe the characteristics of policy implementation research, analyze key similarities and differences between this field and implementation science, and discuss how knowledge assembled in policy implementation research could inform implementation science.DiscussionFollowing a brief overview of policy implementation research, several aspects of the two fields were described and compared: the purpose and origins of the research; the characteristics of the research; the development and use of theory; determinants of change (independent variables); and the impact of implementation (dependent variables). The comparative analysis showed that there are many similarities between the two fields, yet there are also profound differences. Still, important learning may be derived from several aspects of policy implementation research, including issues related to the influence of the context of implementation and the values and norms of the implementers (the healthcare practitioners) on implementation processes. Relevant research on various associated policy topics, including The Advocacy Coalition Framework, Governance Theory, and Institutional Theory, may also contribute to improved understanding of the difficulties of implementing evidence in healthcare. Implementation science is at a relatively early stage of development, and advancement of the field would benefit from accounting for knowledge beyond the parameters of the immediate implementation science literature.SummaryThere are many common issues in policy implementation research and implementation science. Research in both fields deals with the challenges of translating intentions into desired changes. Important learning may be derived from several aspects of policy implementation research.

BackgroundThe past decade of research has seen theoretical and methodological advances in both implementation science and health equity research, opening a window of opportunity for facilitating and accelerating cross-disciplinary exchanges across these fields that have largely operated in siloes. In 2019 and 2020, the National Cancer Institute’s Consortium for Cancer Implementation Science convened an action group focused on ‘health equity and context’ to identify opportunities to advance implementation science. In this paper, we present a narrative review and synthesis of the relevant literature at the intersection of health equity and implementation science, highlight identified opportunities (i.e., public goods) by the action group for advancing implementation science in cancer prevention and control, and integrate the two by providing key recommendations for future directions.DiscussionIn the review and synthesis of the literature, we highlight recent advances in implementation science, relevant to promoting health equity (e.g., theories/models/frameworks, adaptations, implementation strategies, study designs, implementation determinants, and outcomes). We acknowledge the contributions from the broader field of health equity research and discuss opportunities for integration and synergy with implementation science, which include (1) articulating an explicit focus on health equity for conducting and reviewing implementation science; (2) promoting an explicit focus on health equity in the theories, models, and frameworks guiding implementation science; and (3) identifying methods for understanding and documenting influences on the context of implementation that incorporate a focus on equity.SummaryTo advance the science of implementation with a focus on health equity, we reflect on the essential groundwork needed to promote bi-directional learning between the fields of implementation science and health equity research and recommend (1) building capacity among researchers and research institutions for health equity-focused and community-engaged implementation science; (2) incorporating health equity considerations across all key implementation focus areas (e.g., adaptations, implementation strategies, study design, determinants, and outcomes); and (3) continuing a focus on transdisciplinary opportunities in health equity research and implementation science. We believe that these recommendations can help advance implementation science by incorporating an explicit focus on health equity in the context of cancer prevention and control and beyond.

IntroductionHIV continues to devastate the adolescent population in sub‐Saharan Africa (SSA). The complex array of interpersonal, social, structural and system‐level obstacles specific to adolescents have slowed progress in prevention and treatment of HIV in this population. The field of implementation science holds promise for addressing these challenges.DiscussionThere is growing consensus that enhanced interactions between researchers and users of scientific evidence are important and necessary to tackle enduring barriers to implementation. In 2017, the Fogarty International Center launched the Adolescent HIV Prevention and Treatment Implementation Science Alliance (AHISA) to promote communication and catalyse collaboration among implementation scientists and implementers to enhance the cross‐fertilization of insights as research advances and the implementation environment evolves. This network has identified key implementation science questions for adolescent HIV, assessed how members’ research is addressing them, and is currently conducting a concept mapping exercise to more systematically identify implementation research priorities. In addition, AHSA pinpointed common challenges to addressing these questions and discussed their collective capacity to conduct implementation science using the shared learning approach of the network. Specifically, AHISA addresses challenges related to capacity building, developing mentorship, engaging stakeholders, and involving adolescents through support for training efforts and funding region‐/country‐specific networks that respond to local issues and increase implementation science capacity across SSA.ConclusionsInnovative platforms, like AHISA, that foster collaborations between implementation science researchers, policymakers and community participants to prioritizes research needs and identify and address implementation challenges can speed the translation of effective HIV interventions to benefit adolescent health.

Embedding implementation science (IS) research into health departments can foster relevant and robust research that is responsive to health department needs. However, few health department evaluators have formal IS training. Building internal health department capacity to conduct rigorous evaluation has the potential to catalyze improvements in Ending the HIV Epidemic (EHE) service delivery. In-person IS workshop for EHE evaluators held in Seattle, WA, May 2024. We conducted a needs assessment with EHE jurisdictions to identify their capacity and expertise in IS, and desire for additional training. Based on this assessment, we developed a 2.5-day workshop to equip health department epidemiologists/data managers/program coordinators with the background and tools needed to conduct evaluations of activities within their EHE programs. The workshop covered 9 core IS competencies. There were 14 workshop participants, representing 25% of the 57 EHE jurisdictions. Participants felt that the workshop increased their IS technical skills [IS knowledge rating (0 to 5 scale) increased from a mean of 2.5 (day 1) to 4.2 (day 3)] and that the workshop helped them understand how implementation outcomes can strengthen their evaluation. They also noted that the workshop provided them with a useful framework to contextualize and evaluate EHE activities, with 83% of participants strongly agreeing that the content learned could be applied to their health department work. IS short-courses are a promising strategy to increase health department evaluators' understanding of IS and to gain confidence in how to apply IS methods to their own EHE evaluations.

Lost in translation: priorities in the application of evidence to practice.

Purpose: The purpose of this study is to discern the prospects and challenges of integrating Internet of Things (IoT) into the public healthcare sector of Bangladesh. This is timely because the present public healthcare status of the country needs dire improvement. Hence, this study explores the future scope of IoT-based public healthcare systems to be introduced to the Directorate General of Health Services (DGHS) of Bangladesh henceforth recommends how the country can integrate the implementation of this technology to deliver better public healthcare services to its population. Methodology: The authors reviewed extensively literature from several reputed sources to identify the current public healthcare conditions, as well as the benefits and impediments of IoT-based public healthcare system in Bangladesh. Based on the searches pertaining to the data base, this study then outlines appropriate future scopes of IoT in the public healthcare of Bangladesh. Findings: This research identified major impediments such as lack of infrastructure, low motivation, awareness and acceptance as well as poor healthcare startups prevalent in the public health services of Bangladesh. To surmount the inefficiencies of the current services, the authors deliberated how IoT based public healthcare system could foster better monitoring; management and rehabilitation thereby improve the overall Bangladesh’s public healthcare system. Practical Implications: IoT healthcare organizations can also use technology to improve patient outcomes and increase efficiencies, which will lower the cost of treatment, reduce death rates and make it possible to give public healthcare to people who live in rural areas. Originality/Value: The researchers of this study are confident that the findings of this study will benefit other researchers, policymakers, healthcare service providers/designers and professionals to realize the potentials of IoT-based public healthcare system that could overcome the challenges faced and finally could deliver sound public healthcare facilities and services for Bangladeshis in the near future. Limitations: In the future, we plan to develop the Central Repository of patient database system using blockchain technology.

Disparities in health outcomes for heart, lung, blood, and sleep-related health conditions are pervasive in the United States, with an unequal burden experienced among structurally disadvantaged populations. One reason for this disparity is that despite the existence of effective interventions that promote health equity, few have been translated and implemented consistently in the healthcare system. To achieve health equity, there is a dire need to implement and disseminate effective evidence-based interventions that account for the complex and multilayered social determinants of health among marginalized groups across healthcare settings. To that end, the National Heart, Lung, and Blood Institute's Center for Translation Research and Implementation Science invited early stage investigators to participate in the inaugural Saunders-Watkins Leadership Workshop in May of 2018 at the National Institutes of Health. The goals of the workshop were to: (1) present an overview of health equity research, including areas which require ongoing investigation; (2) review how the fields of health equity and implementation science are related; (3) demonstrate how implementation science could be utilized to advance health equity; and (4) foster early stage investigator career success in heart, lung, blood, and sleep-related research. Herein, we highlight key themes from the 2-day workshop and offer recommendations for the future direction of health equity and implementation science research in the context of heart, lung, blood, and sleep-related health conditions.