This narrative and conceptual review examines the interface between implementation science and health policy research. Drawing on literature from both disciplines, we analyze key areas of convergence and divergence, explore how these distinctions contribute to the persistent ‘know-do gap,’ and discuss opportunities for integrated approaches connecting micro-level implementation processes with macro-level policy dynamics, with particular attention to LMICs. Implementation science and health policy research have evolved largely in parallel, despite sharing a common objective: improving population health by translating knowledge into action. Implementation science focuses on integrating evidence-based interventions into real-world settings, while policy research examines how policies are formulated, adopted, and sustained within complex political systems. This disciplinary separation has contributed to a persistent “know–do gap,” particularly in low-and middle-income countries (LMICs), where fragmented health systems, political economy constraints, and donor influence shape both policy trajectories and implementation realities. The disconnect between researchers and policymakers often stems from differing timing, priorities, and even language. Researchers may not address the immediate concerns of policymakers, and scientific evidence alone seldom drives policy changes. In LMICs, this divide is exacerbated by political cycles, vested interests, and the significant influence of international donors. In this review, we argue that bridging implementation science and policy research is not merely desirable but necessary. We examine how differences in scope, actors, settings, and theoretical frameworks have reinforced this divide, and we propose the development of integrated, hybrid approaches that connect micro-level implementation processes with macro-level policy dynamics. Bridging the gap between implementation research and policy change necessitates a context-sensitive approach that acknowledges the complexities of political, economic, and social systems, particularly in LMICs. By leveraging multi-level frameworks, engaging stakeholders, and fostering continuous learning, it is possible to enhance the translation of research into effective, sustainable health policies.

As the building sector shifts toward community-led energy efficiency (EE) initiatives to mitigate the climate crisis, scaling adoption requires understanding behavioral drivers. This study examines key drivers of EE measure adoption: perceived non-energy benefits, trusted advisors, and behavioral signaling. This research contributes to the energy policy and social science literature by empirically linking social trust with signaling preferences. It extends existing EE adoption decision theories by identifying distinct clusters of “expert” versus “close” advisors across demographic groups. Data from an experimental survey (n = 238) in Portland, Oregon, were analyzed using principal component analysis (PCA) and multivariate regression modeling. Results indicate that saving money and better indoor air quality are the most valued benefits with significant missing survey responses for other non-energy benefits, perhaps indicating a lack of respondent understanding. While family and contractors remain the most trusted advisors, findings highlight a clear split between expert actors and close social networks. Signaling via yard signs is the most preferred method for signaling EE behavior (31%), though “super-participators” prefer multi-channel signaling. These findings suggest that practitioners should thoughtfully leverage social networks and diverse signaling media to improve the salience and increase the adoption of residential energy efficiency programs.

This article outlines applications of the Nova food classification system in policies aimed at reducing the share of ultraprocessed food (UPF) in population diets. Such policies are lagging far behind the growing scientific and public attention to UPFs and the mounting evidence of their harms, reflecting industry opposition and institutional inertia but also valid scientific critiques and policy concerns. We examine how Nova and the UPF concept can inform and improve dietary guidance, dietary monitoring and target-setting, food regulation, and sustainable food system transitions. We position UPF-based policies as complementary to, rather than in competition with, existing policies aimed at addressing dietary nutrient imbalances, offering a constructive way forward to enhance the promotion of healthy diets and facilitate the transition to healthier and sustainable food systems. (Am J Public Health. 2026;116(7):932-939. https://doi.org/10.2105/AJPH.2026.308509).

Dissemination in diagnostic accuracy studies is not associated with methodological quality: a systematic review and meta-epidemiological analysis.

This article presents a review of the landscape of Open Science development and its relevance to crystallography. As open as possible as closed as necessary is the model for Open Science as commended by the UK and China. Mainland Europe requires compulsory release after 3 years since measurement of raw data. The USA policy situation has fluctuated considerably in recent years. Altogether, this article addresses the International Union of Crystallography's (IUCr) input into these activities. It considers its participation in international scientific and societal organizations, as well as its compliance with the policy recommendations. It considers the practical landscape in which crystallographers work, including their funding agencies, governments, central facilities and universities. This article sets the wider scene before describing how crystallography benchmarks against those under development. The IUCr's Teaching Pamphlets and the Online Dictionary of Crystallography are educational resources fully open to Global South and Global North readers and authors. Crystallography provides a mature, practice-based model of responsible openness that predates and can inform contemporary Open Science policy.

Understanding dynamics of agricultural runoff to Oslofjord: A causal loop modelling approach.

This study aims to analyze SARA-based horizontal conflicts from a national defense perspective and examine their potential as a strategic indicator in the early detection system of non-military threats. The research uses a qualitative approach with the library research method through the analysis of scientific literature, policy documents, and laws and regulations related to social conflicts, national security, and national defense. The results of the study show that SARA-based horizontal conflicts can be understood as non-military threats because they have the potential to undermine social cohesion, weaken state legitimacy, and disrupt national stability. In addition, identity-based conflicts have a developmental pattern that allows the emergence of early indicators such as social polarization, intolerance, and identity-based group mobilization. These findings are expected to enrich defense studies and support the strengthening of conflict prevention policies in order to maintain national security.

Understanding how domestic and foreign-invested firms across China's provinces participate in domestic and international production networks has become increasingly important in both scientific research and policy analysis. However, existing data infrastructures, most notably input-output (IO) datasets, does not adequately support this line of inquiry. Conventional inter-country IO (ICIO) databases treat China as a single, homogeneous economy and therefore overlook substantial variation at the provincial level. Meanwhile, China's inter-provincial IO tables provide no direct linkages between individual provinces and their foreign trading partners. Moreover, firm ownership heterogeneity is largely absent in most available IO datasets. To address these limitations, we construct a new ICIO dataset that integrates China's inter-provincial IO tables-disaggregated by domestic and foreign-invested firms-with the OECD's Activities of Multinational Enterprises-ICIO data and detailed Chinese customs statistics, all within a globally consistent accounting framework. The resulting database comprises 26 sectors, 107 regions, and 2 ownership categories for the benchmark years 2007, 2012, and 2017. These data offer a new foundation for empirical work in economics, environmental assessments, network analyses, and their interdisciplinary applications, particularly for studies examining how firms with different ownership structures across Chinese provinces engage in global supply chains.

ABSTRACT This study investigates how science, technology, and finance (STF) policies improve urban green transformation and competitiveness. Utilizing a multi-period difference-in-differences (DID) model, we analyse panel data from 285 Chinese cities spanning the period 2006–2022 to assess the effect and underlying mechanisms of STF policies on urban green competitiveness. Results show that the implementation of STF policies significantly enhances urban green competitiveness, with an average improvement ranging from 8.12% to 9.18%. Our analysis of the underlying mechanisms reveals that this improvement is driven by three key factors: increased financial incentives for research and development, a rise in green patent awards, and improved total factor productivity. These findings demonstrate that STF policies enhance urban green competitiveness both directly and indirectly through innovation and productivity gains. In addition, we highlight the heterogeneity of STF policy effects across regions, city sizes, resource endowments, and city clusters. Our study provides new empirical evidence and policy insights to guide regions in harnessing the combined power of science, technology, and finance to achieve green and sustainable urban development.

Effective pandemic response requires large-scale behavioural change. Despite efforts to integrate behavioural science advice in public health policy making, there is a shortage of empirical evidence on the challenges faced by countries that want to professionalise their behavioural science advisory capacity. As senior behavioural science advisers during the COVID-19 pandemic, we explored and compared how behavioural science organised itself across Northwestern Europe in order to draw lessons for integrating behavioural insights into policy during future crises. Using the evidence-based public health framework, we conducted interviews with 21 senior stakeholders from the Netherlands, Ireland, the UK and Finland, followed by a facilitated look-back meeting with a subset of participants. All participants were actively involved in their countries' pandemic responses. Data were analysed using qualitative analysis software (NVivo 15) and validated during the facilitated look-back meeting. Producing actionable and relevant behavioural science advice during crises benefits from: (1) drawing on a wide mix of research methodologies; (2) sustaining continuous dialogue with, and access to, policy and decision-makers; (3) ensuring clear packaging and delivery of advice, including additional efforts to support comprehension; (4) creating functional structures for interdisciplinarity; and (5) maintaining institutional memory of the competencies and relationships developed during crisis response. An improved science for policy practice in the health field involves the difficult tasks of motivating direct dialogue, interdisciplinarity and co-creation, and a functioning governance of behavioural science advice systems. The findings further illustrate the conceptual utility of the evidence-based public health framework.

Paratonia in Advanced Dementia: Deconstructing Scientific, Regulatory, and Health System Barriers to Botulinum Toxin A (BoNT-A) Treatment of Involuntary Muscle Resistance.

This study examines how the 1979 Iranian Revolution altered Iran’s long-term scientific development using publication and citation data for 1960–2024. Combining longitudinal scientometric analysis with counterfactual modeling, we identify a major structural break in Iran’s research trajectory after 1979. Iran experienced a substantial post-2000 recovery in publication volume, but this recovery was accompanied by a persistent quality gap, with Field-Weighted Citation Impact (FWCI) and top-cited-paper shares remaining below those of several comparator systems. To assess the long-run opportunity cost of the disruption, we use two complementary counterfactual strategies: a comparison-based Synthetic Control Method (SCM) and a set of developmental-growth proxy scenarios. Across specifications, the qualitative conclusion is stable: the post-1979 interruption is associated with a durable loss of scientific compounding capacity. At the same time, the quantitative magnitude of the cumulative shortfall is sensitive to counterfactual specification, indicating that no single benchmark should be treated as a definitive historical forecast. We therefore interpret the SCM results as comparison-based benchmarks and the developmental-growth proxies as illustrative upper- and mid-range scenarios rather than literal one-to-one counterfactuals. We interpret these findings through the lens of the Middle-Income Trap (MIT) and the political economy of the Rentier State. We argue that Iran’s post-revolutionary science policy contributed to a quantity–quality paradox in which expansion in scientific output was not matched by comparable gains in global scientific influence, while also recognizing that observed citation-impact measures are shaped by international collaboration constraints and bibliometric coverage limitations. The findings highlight the long-run developmental cost of political disruption while emphasizing that sustained scientific catch-up depends not only on output growth, but also on institutional continuity, international integration, and incentive structures that reward impact rather than volume alone.

BackgroundThe COVID-19 pandemic demanded evidence synthesis approaches that could keep pace with rapidly evolving science and urgent policy needs. Rapid reviews and living evidence syntheses (LES) emerged as critical methodological responses, yet limited guidance exists for teams undertaking such work, particularly regarding the operational challenges of sustained production, multi-team coordination, and the transition from rapid reviews to living formats under emergency conditions.ObjectivesThis paper aims to document and appraise methodological decisions made across each component of the synthesis process and their rationale; identify the contextual factors, coordination mechanisms, and infrastructure that enabled or constrained synthesis implementation under emergency conditions; and derive practical recommendations for evidence synthesis teams, funders and commissioners, and methodologists.MethodsWe present a structured reflective process evaluation of our experience producing three rapid LESs on COVID-19 vaccine effectiveness as part of COVID-END in Canada, addressing vaccine effectiveness in adults (short-term and long-term) and children/adolescents, collectively producing 79 versions over approximately two years. Drawing on a Context–Mechanism–Outcome (CMO) lens, we examine eight core methodological processes, each time highlighting: the methodological decision and its rationale; the challenges encountered; and the lessons learned.FindingsKey insights include: decision-maker engagement played a central role in methodological decisions, directly determining scope boundaries, update frequency, and presentation formats; the multi-team coordination structure was essential to quality under streamlined methods, with cross-team verification partially offsetting the validity risks of single-reviewer screening; choosing between narrative synthesis (compared to meta-analytic approaches) in two of three products reflected capacity constraints during emergency production, a distinction with implications for how such adaptations are evaluated against existing guidance; automation produced considerable efficiency gains but required added development capacity and resources, demonstrating that teams should plan and budget for automation from the outset rather than absorbing its demands reactively during production; and coverage was acceptable for decision timelines. External cross-checks identified five studies not captured by our searches across the entire project, of which only two were ultimately eligible for synthesis. Emergency production meant that key process records, including change logs, per-cycle search records, and pilot screening agreement data, were not systematically captured. Future teams should plan to assemble a minimal transparency package from the outset. A minimum viable LES framework is proposed comprising minimum infrastructure, minimum methods, and optional enhancers.ConclusionsThe successful implementation of rapid LES requires pre-existing or rapidly mobilisable infrastructure, sustained and explicitly resourced decision-maker engagement, transparent documentation of methodological adaptations across update cycles, effective coordination in multi-team contexts, and realistic sustainability planning. These lessons, derived from a specific emergency context, offer transferable guidance for evidence synthesis teams preparing for future health emergencies and for standards bodies considering how existing guidance accommodates emergency and living synthesis conditions.

Science and technology finance policy, green innovation and corporate ESG performance

There is a dearth of research that provides robust empirical studies of policy implementation in the context of open science. In the United States, a series of Presidential Memoranda issued by the White House Office of Science and Technology Policy (OSTP) lays much of the federal policy foundation for open science, including a requirement for equitable access to federally funded public research. Benefits from research results and scientific data are often constrained by barriers to access, creating inequities in knowledge sharing. Hence, equitable public access to research results is still lacking. This study provides an exploratory qualitative study of practical challenges, organizational tensions, and tradeoffs experienced by agency actors in U.S. federal agencies seeking to implement equitable public access. We contribute to further research towards advancing policy implementation in open science by offering insights into how U.S. federal agencies understand and implement equitable public access. Our study also contributes to the practice of implementing policy mandates regardless of political administrations by increasing government agency actors' awareness of four dimensions latent in the implementation process: striving with ambiguity, overcoming fragmentation, reducing disparity, and mitigating perplexity. • We explored advancing the policy implementation process in open science. • Policy implementation in open science necessitates an iterative, adaptive process. • We found four dimensions that diminish policy implementation for equitable public access.

From targets to real world impact: a review of models of care for chronic HBV and HCV in Europe.

Swift quantitative risk assessment and multi-criteria decision analysis on main food-borne pathogens in market food in Zhejiang Province, China.

The Indonesia Smart Card for College (KIP-K) program is a government policy aimed at expanding access to higher education for underprivileged communities while simultaneously supporting poverty alleviation efforts, in line with the Sustainable Development Goals (SDGs) point 1, namely No Poverty. This study aims to analyze the implementation of the KIP-K program using William N. Dunn's policy evaluation indicators, which include effectiveness, efficiency, adequacy, equity, responsiveness, and accuracy. The study uses a qualitative approach through a literature study method by reviewing scientific journals, government reports, policy documents, and various other relevant sources. The results show that the KIP-K program has made a positive contribution to increasing access to higher education and reducing economic barriers for students from underprivileged families. However, the implementation of the program still faces several obstacles, such as inaccurate targeting of aid recipients, delays in disbursement of funds, adequacy issues in certain regions, weak supervision, and less than optimal transparency in program management. These findings indicate that the success of the program is not only determined by the amount of educational assistance provided, but is also influenced by the quality of governance, recipient data validation, and the policy evaluation system implemented. Therefore, strengthening the evaluation mechanism and program governance is an important step in increasing the effectiveness of KIP-K to support sustainable poverty alleviation.

The evidence-policy-interface is a two-way street: policymakers and researchers both benefit from sharing insights about the problems that governments face and how they seek solutions. Yet, too often, the response is one-way activity: investment in a research-to-policy infrastructure to help academics send evidence to policymakers. This engagement is ineffective unless academics draw on research to understand the policy process in which they engage. Most engaged academics are not policy scientists; they rely on policy sciences for knowledge of policy processes. While there is an impressive body of policy research, and some is based on interviews with policymakers, there is no equivalent-sized infrastructure for policy process researchers. Our knowledge of policymaking is insufficient because most policy scientists are not well supported to conduct elite-interview research. Then, this lack of capacity makes most academics ill-prepared to engage effectively in the evidence-policy interface. While we cannot solve this problem overnight, we can stimulate reflection-driven improvement, including by revisiting guidance to policy scientists conducting elite research. To that end, we review current advice to help formalize this guidance and provoke reflection on the state of interview-informed knowledge. We argue that elite interviewing has become logistically easier but politically harder when elites are disincentivised to share perspectives with outsiders. Overcoming this trust barrier may depend on practices beyond those outlined in standard qualitative research methods and ethics processes. If so, access to policy insights and knowledge might remain limited. We need better ways to facilitate academic-policymaker exchange.

With the increasing demand for high-quality regional economic development, accurately extracting hidden features of economic data and achieving reliable development trend prediction has become an important prerequisite for formulating scientific economic policies. To optimize the feature extraction accuracy of regional economic data and the development prediction reliability, a model for feature extraction and development prediction of regional economic data is constructed by integrating an improved grey wolf optimization algorithm, a support vector machine, and a generative adversarial network. On the F1 unimodal function and F2 multi-modal function tests, the convergence speed was significantly better than that of the wind-driven optimization and sine cosine algorithm, demonstrating stronger adaptability in complex optimization problems. The comparative experiment on the steel wire rope dataset showed that the algorithm improved the recognition rate by 1.25% compared to the ‌Principal Component Analysis-Grey Wolf Optimizer-Support Vector Machine, reaching 98.75%, and had higher efficiency in high-dimensional feature processing, verifying its superiority in feature extraction and classification recognition. The model was applied to the economic data in Anhui Province, selecting 8 core indicators such as the GDP of the primary industry and the income of urban and rural residents from 2011 to 2022. In 2011, when the true value was 16,311, the predicted value of the research model was 16,200. In 2015, when the true value was 23,808, the model predicted a value of 23,600. The minimum absolute error from 2011 to 2020 was only 103, and the error rate was as low as 0.005, demonstrating outstanding stability in medium and long-term forecasting. The proposed model can effectively capture the characteristics of regional economic data, improve prediction accuracy, and provide a scientific basis for regional economic development decisions.