Australian Rapid Evidence Support System Assessment: mapping Australia’s Health Evidence Ecosystem

Australian Rapid Evidence Support System Assessment: mapping Australia’s Health Evidence Ecosystem | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Australian Rapid Evidence Support System Assessment: mapping Australia’s Health Evidence Ecosystem Zoe Louise Jordan, Dook Shepherd, Peter Bragge, Bianca Pilla This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9205700/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective: To map and analyse Australia’s health evidence ecosystem as part of a Rapid Evidence Support System Assessment (RESSA), identifying key structures, gaps and opportunities to strengthen national evidence-support capacity. Methods: Publicly available online data were used to identify organisations active in evidence generation, translation and use. Organisations were functionally classified according to the Global Evidence Commission’s taxonomy and mapped using Kumu software. Network metrics (degree, betweenness, closeness, eigenvector and reach) were calculated to assess structure, influence and connectivity. Results: A total of 1382 organisations performing 1,653 functional roles were identified across five domains: decision-makers (28%), hybrid decision-maker intermediaries (19%), evidence intermediaries (11%), hybrid intermediary–producers (32%) and evidence producers (10%). The network displayed a decentralised, polycentric structure anchored by several “super-connectors” bridging supply and demand, notably the Sax Institute, Australian Living Evidence Collaboration, Centre for Evidence and Implementation, Paul Ramsay Foundation, NHMRC HEAL National Research Network and Monash University. Geographic analysis revealed strong concentration in New South Wales, Victoria and the Australian Capital Territory, with limited representation elsewhere. Results implications: Australia’s evidence ecosystem demonstrates strong translational capacity but fragmented coordination and regional inequities. Conclusions: This phase-one RESSA provides the first national baseline of Australia’s health evidence ecosystem. Maintaining a living, updatable network model could support coordination, reduce duplication and enhance evidence-informed public health decision-making. Australia evidence ecosystem network mapping network analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Key points • This study provides the first systematic mapping of Australia’s health evidence ecosystem using the Global Evidence Commission’s taxonomy. • Findings reveal strong translational capacity but fragmented coordination and regional inequities. • A small number of “super-connector” organisations bridge evidence supply and demand. • Maintaining a living, updatable network model could support national collaboration, reduce duplication, and strengthen Australia’s evidence-support system for public health decision-making. Introduction The Global Commission on Evidence to Address Societal Challenges (hereafter the Global Evidence Commission)( 1 ) has called for coordinated national efforts to strengthen evidence-support systems that ensure decision-making is informed by trustworthy, timely, and relevant evidence. To support this global agenda, Rapid Evidence Support System Assessments (RESSAs) have been developed to identify existing capacities, gaps, and opportunities for improved coordination within national evidence ecosystems. These assessments provide a structured means of understanding how countries generate, translate, and use evidence to inform health and social policy( 1 ). Within this context, Australia’s health evidence infrastructure has evolved into a complex network of evidence producers, intermediaries, and users of evidence, encompassing universities, government agencies, research collaborations, think tanks, and non-government organisations( 2 , 3 ). However, despite considerable national investment in evidence generation and translation, questions remain about the coherence, efficiency, and equity of the system as a whole( 4 ). Mapping this ecosystem provides an opportunity to identify where strengths and gaps exist, and to inform future policy and infrastructure development aimed at improving public health outcomes. The impetus for this work also aligns with Australia’s commitment to the United Nations Sustainable Development Goals (SDGs)( 5 , 6 ). Strengthening the national evidence ecosystem contributes directly to these global priorities by promoting transparent, accountable, and effective institutions, advancing innovation in evidence synthesis and translation, and ensuring that the best available knowledge underpins public health decisions. Objectives The objective of this study was to conduct phase one of a Rapid Evidence Support System Assessment to provide a baseline understanding of the Australian health evidence infrastructure, including key contributors on the evidence demand and supply side, in line with the Global Evidence Commission. The aim was to generate a comprehensive and dynamic baseline model that can inform national and international dialogue on strengthening evidence infrastructure for equitable, efficient, and sustainable public health decision-making. The next phase will involve key informant interviews to review the findings of this first phase. Methods Desktop audit A structured desktop audit was conducted using public internet search engines to identify primary organisations constituting the Australian evidence health ecosystem. Organisations and their functional roles were classified according to the Global Evidence Commission’s technical lexicon, focusing on Domain 2 – the evidence interface between evidence production (supply) and evidence-informed decision-making (demand)( 1 ). Websites and online documents describing organisational functions, governance structures, and inter-institutional relationships were reviewed. Data were extracted into a mapping matrix capturing organisation name, sector, functional role(s), and affiliations. Kumu, an online cloud-based platform for mapping and visualising complex relationships ( https://kumu.io/ ) was then used to develop a living network model( 7 ). National organisations were coded by head office location and functionally categorised according to the technical roles they play. This entails that many will satisfy more than one category. Additionally, organisations were mapped by their functional roles within the evidence ecosystem, with some classified under multiple Commission categories when their remit, programs, or services spanned more than one function of the evidence-support system. The network model’s ties (edges) were structured according to their online listings of affiliated organisations; however, international affiliates were excluded to delimit the scope to the domestic ecosystem, thereby truncating the reach of the actual network’s in/out degrees. Search terms The search strategy combined general and targeted terms drawn from the Commission’s taxonomy of evidence-support systems. Searches included combinations of: Australian evidence-based health, Australian government health and aged care department, Australian government primary body, Australian government secondary body, Australian government tertiary body, Australian health evidence network, Australian health ecosystem, Australian health department, Australian health institute, Australian health organisation, Australian health think tank, health care companies in Australia, mental health organisations in Australia, medical associations in Australia, medical research institutes in Australia. Inclusion criteria Formal designations of Australian health sector/ health service organisations, health organisations, health research units, university health departments, health network, health committee, health foundation, government body (Health and Aged Care), primary bodies (non-corporate commonwealth entity, corporate commonwealth entity, commonwealth company, secondary statutory structures, statutory advisory structure, statutory office holder, offices and committees secondary non-statutory structure), peak bodies. Exclusion criteria Individual researchers, laboratories, empirical research, medical trials, material evidence syntheses (i.e. the products of evidence production), hospitals that did not clearly specify units or programs of work related to evidence-based activities. Social Network Analysis The extracted relational data were analysed using social network analysis tools in Kumu to explore the structure and dynamics of Australia’s health evidence ecosystem( 7 ). Nodes represented organisations and edges denoted declared affiliations. Centrality measures (degree, betweenness, closeness, eigenvector, and reach) were calculated to identify highly connected and influential organisations. Directed ties distinguished outgoing and incoming relationships, approximating information flow and perceived influence authority. Results were used to interpret the overall configuration of the ecosystem, identify key bridging actors and potential coordination gaps, and inform future phases of RESSA application in Australia. Results Overview of the Australian evidence ecosystem The Phase 1 RESSA identified 1,382 unique organisations, performing 1,653 functional roles, operating within Australia's health evidence ecosystem (see Table 1 and Fig. 1 ). Organisations were functionally classified according to the Commission’s technical taxonomy and grouped into five broad domains: decision-makers (DM), hybrid decision maker intermediary (HDI), evidence-intermediary (EI), hybrid evidence intermediary producers (HIP), and evidence-producers (EP). A total of 215 organisations were mapped to more than one Commission category, reflecting the breadth of their functions across evidence supply, translation, and demand. This overlap accounts for the higher total of functional classifications (n = 1653) relative to the number of distinct organisations (n = 1382) captured in the network. The resulting distribution revealed a dense and decentralised network, characterised by numerous professional and organisational nodes, modest inter-jurisdictional integration, and substantial concentration of activity within the eastern states (see supplementary file 1 for a full list of organisations and their functional classifications). Table 1 Australian health ecosystem organisational functional classifications according to the Commission Australian health ecosystem: Organisational functional classifications according to the Commission Type Sub-type Sub-type total Decision Makers Government policy makers 24 Organisational leaders 48 Professionals 365 Citizens 30 DM Sub-total 467 Hybrid Decision maker Intermediary Technical units within multilateral organizations… 5 Domestic commissions 19 Government advisory bodies 127 Government science advice 27 Government evidence support 128 HDI Sub-total 306 Evidence Intermediary Fact-checking organizations 1 Science academies 7 Think tanks 29 Knowledge Translation Platform/ knowledge brokers 144 EI Sub-total 181 Hybrid evidence Intermediary Producers Impact-oriented data-analytics units 120 Impact-oriented modelling units 43 Impact-oriented evaluation units 55 Impact-oriented behavioural/ implementation research unit 116 Impact-oriented qualitative insights unit 30 Impact-oriented evidence synthesis units 35 Technology-assessment units 54 Guideline units 82 HIP Sub-total 535 Evidence Producers 164 TOTAL : 1653 Composition by functional domain Among the five domains (see Table 1 and Fig. 2 ), organisations performing HIP functions (n = 535; 32%) represented the largest category (Fig. 3 ), encompassing impact-oriented evidence synthesis units, modelling, evaluation, behavioural science and implementation research organisations. The second-largest cluster comprised Decision-Makers (n = 467; 28%) (Fig. 4 ), including 25 government policymaking bodies, 48 organisational leaders, 365 professional associations, and 30 citizen-focused mechanisms. Organisations with HDI functions (Figs. 5 – 6 ) accounted for 306 (19%), including government advisory bodies and government evidence support, while organisations performing EI functions (think tanks, science academies, and knowledge-translation platforms) numbered 181 (11%). Organisations classified as EPs (Fig. 7 ) constituted the remaining 164 (10%), typically located within universities and collaborative research structures. Geographic distribution State-based analysis demonstrated marked regional asymmetry (see Table 2 and Fig. 8 ). New South Wales (n = 517) and Victoria (n = 501) together hosted over half of all organisational classifications, reflecting the concentration of national research funding and headquarters functions. The Australian Capital Territory (ACT) exhibited disproportionate representation of organisations with HDI functions (n = 171; 54% of the national total), consistent with its role as the seat of federal government and home to advisory and analytical agencies. Western Australia (n = 136) and Queensland (n = 158) showed broad functional coverage but limited inter-state linkages. Smaller jurisdictions, including South Australia (n = 127), the Northern Territory (n = 34), and Tasmania (n = 26), were under-represented, highlighting geographic inequities in national evidence infrastructure. 76 organisations operate in multiple jurisdictions. Consequently, when aggregating commission categories by state, the totals exceeded those derived from unique organisation counts in the dataset. Table 2 Commission categories by state Commission categories by state Types by state DM HDI EI HIP EP 1. Western Australia 40 14 10 54 18 2. Northern Territory 17 1 1 11 4 3. South Australia 43 11 18 42 13 4. Queensland 59 12 22 43 22 5. New South Wales 128 56 60 217 56 6. Australian Capital Territory 41 171 12 63 7 7. Victoria 174 47 59 166 55 8. Tasmania 12 6 2 3 3 TOTAL 514 318 184 599 178 Network topology and centrality Social-network analysis of relational data (edges = 2690) revealed a polycentric structure with multiple bridging nodes rather than a single dominant hub. Several metrics were used to examine structural dynamics (See Appendix 2). Betweenness centrality Betweenness scores identified nodes that act as information bridges. The Australian Health Research Alliance (AHRA) exhibited the highest betweenness (0.023), followed by the NHMRC HEAL National Research Network (0.016), National Health and Medical Research Council (NHMRC) (0.016) and Monash University (0.015). Closeness centrality Closeness centrality highlighted organisations able to disseminate information rapidly across the network. The Paul Ramsay Foundation ranked first (0.249), followed by the Better Health Channel (0.226), Centre for Evidence and Implementation (0.214), and NHMRC HEAL Network (0.204). These organisations combine philanthropic and/or funding reach, public-facing communication, and translational capacity, suggesting that influential connectors extend beyond traditional government or research institutions Degree and directionality Degree centrality confirmed the Paul Ramsay Foundation (187 links), Better Health Channel (154 links), Climate and Health Alliance (98 links) and The Sax Institute (86 links) as major hubs, with substantial connections to both professional networks and citizens. Outdegree scores (outgoing links) were similarly dominated by these four actors, indicating high broadcast capacity. At the same time, indegree measures, reflecting advisory influence, were led by Monash University (34) and the University of Sydney (25), reinforcing the central role of large universities as evidence producers. Eigenvector and systemic influence Eigenvector centrality, which weights influence by the quality of connections, identified Monash University (0.0132), AHRA (0.0108), Deakin University (0.0105) and NHMRC (0.0103) as the most influential actors, each embedded within clusters of similarly well-connected organisations. These findings illustrate an academically anchored yet distributed network with high redundancy among major universities, an indicator of resilience, but also potential fragmentation when coordination mechanisms are absent. Cross-impact and leverage analysis MICMAC analysis, exploring each element’s exposure and influence, further clarified system leverage points. The Hearing Health Sector Alliance (HHSA) demonstrated maximal overall influence, followed closely by the Australian Health Policy Collaboration (AHPC) and TAPPC, indicating high potential to affect other actors’ behaviour. Exposure scores, i.e. how much an element is affected by others, were highest for professional and member-based organisations such as the Medical Women’s Society and the Health Roundtable, signalling vulnerability to external shifts in funding or policy. Emerging ‘super-connectors’ A subset of organisations, CAHA, Australian Living Evidence Collaboration, Sax Institute, CEI, AHRA, NHMRC HEAL National Research Network, Monash University, Better Health Channel, Paul Ramsay Foundation and others, consistently scored within the top decile across multiple centrality measures (see Appendix 2). These organisations bridge domains (supply, demand, and intermediaries) and maintain multi-sectoral collaborations. Their cross-cutting influence positions them as potential backbone organisations for a future coordinated evidence-support system (see Figs. 9 – 11 ). Thematic patterns across Commission domains Mapping across the Commission’s functional categories revealed five overarching themes: 1. Policy density and advisory duplication. The ACT hosts a concentration of advisory and analytic units (e.g. Science and Policy Interface, Chief Scientist structures) with overlapping mandates, suggesting potential efficiency gains through consolidation. 2. Knowledge-translation strength but coordination gaps. Australia exhibits an exceptionally large EI cohort (144 knowledge-brokerage platforms), yet it has limited formal mechanisms to connect these intermediaries to policy demand. 3. Dominance of impact-oriented evidence production. The HIP domain reflects a strong culture of applied evidence, including implementation and behavioural-science units, but inconsistent integration with national decision-making forums. 4. Academic gravitational centres. Eigenvector results reinforce the dominance of the Group-of-Eight universities and allied research institutes as system anchors. 5. Peripheral under-representation. The scarcity of organisations in smaller states and territories underscores regional inequities in access to evidence-support infrastructure. Network morphology Visualisation through the living network model revealed a multi-layered, small-world topology with dense intra-state clustering and limited inter-state edges. Clusters aligned with state health department structures, national research alliances, and thematic initiatives (e.g. climate and health, women’s health, rural health). Cross-domain links were thinner, indicating the need for structured national coordination mechanisms to bridge the “evidence interface” between production and use. Discussion This phase one Australian Rapid Evidence Support System Assessment (RESSA) represents the first national mapping and network analysis of the organisations and their functional roles that constitute Australia’s health evidence ecosystem. By adopting the Commission’s technical lexicon, this study provides a structured and comparable taxonomy of actors involved in evidence generation, translation, and use. The approach enabled consistent classification and facilitated alignment with international initiatives( 8 ), strengthening Australia’s capacity to engage in global dialogues on evidence infrastructure reform, such as those occurring as part of the Evidence Synthesis Infrastructure Collaborative (ESIC)( 9 , 10 ). The findings demonstrate that Australia possesses a mature and diverse evidence ecosystem characterised by substantial translational capacity and a strong culture of applied research. The dominance of organisations performing hybrid intermediary-producer functions, particularly in behavioural and implementation science, data analytics, and evaluation, reflects an advanced orientation toward evidence use in practice and policy. This distribution highlights the evolution of the Australian research landscape from traditional evidence production to a globally leading integrated model of knowledge translation and impact measurement. However, the network analysis also reveals fragmentation and duplication, particularly among advisory and analytic bodies concentrated in the Australian Capital Territory. The coexistence of multiple government science advice and evidence-support mechanisms suggests potential inefficiencies and a need for greater coordination and/or consolidation of workflows. Rationalisation of overlapping functions could enhance system efficiency and ensure that advisory processes are coherent, transparent, and responsive to the needs of policymakers and practitioners. The uneven geographic distribution of organisations represents another structural challenge. Concentration within the eastern states and the relative under-representation of organisations in smaller jurisdictions, while not surprising, risk perpetuating inequities in access to evidence-support infrastructure. A more balanced distribution of evidence capabilities, potentially supported by digital platforms, virtual collaborations, and regional nodes, could enhance inclusivity and strengthen the national evidence base. Addressing these disparities aligns with national public health priorities under the SDGs, particularly those related to reducing inequalities and promoting strong institutions. At the systemic level, the Australian evidence ecosystem exhibits a polycentric and resilient structure with multiple bridging nodes rather than a single dominant hub. Organisations such as the Paul Ramsay Foundation, the Centre for Evidence and Implementation, the Sax Institute, the Australian Living Evidence Collaboration and others act as “super-connectors” across the evidence interface (Appendix 2). Their influence extends beyond traditional academic or government domains, reflecting a diversification of leadership within the evidence system. These cross-sectoral connectors present an opportunity to establish coordinated national mechanisms that could operate as backbone organisations for a living, adaptive evidence-support infrastructure. Although this phase one RESSA focused on mapping domestic actors within Australia’s evidence ecosystem, it is also important to acknowledge the established role of globally networked evidence organisations such as Cochrane, JBI, Guidelines International Network, Monash Sustainable Development Institute and others. These organisations serve as globally connected hubs that both inform and are informed by Australia’s evidence ecosystem, facilitating bidirectional knowledge flows, methodological innovation, and cross-country collaboration. Their long-standing engagement with WHO, UN agencies, and global research alliances positions them as valuable partners in strengthening Australia’s evidence-support infrastructure and ensuring its alignment with international efforts. Limitations The datasets included here represent a dynamical ecosystem and are thus neither exhaustive nor complete. Network density has likely changed since publication due to the shifting nature of the evidence ecosystem’s organisations and their affiliations. This living network model, therefore, will at once increase in fidelity as more data and verification become available, but will always only ever approximate a structure-in-motion. Furthermore, the nature of the desktop audit and the functional classification of organisations according to information available online, almost certainly resulted in unaccounted for relevant organisations and inaccurate categorisations, which likely under-estimate the significant roles they play. The strict application of the Global Evidence Commission lexicon enhanced classification reliability but may have obscured local nuances and emergent domains of activity. Categories such as big data collaborations, digital health platforms, libraries, and professional colleges occupy ambiguous positions within the taxonomy yet play pivotal roles in evidence dissemination and use. Implications for future research Future research should examine how such organisations contribute to the national evidence ecosystem and explore hybrid classifications that better capture their integrative functions. Comparative analysis with alternative frameworks could also illuminate areas where Australia’s system diverges from or exceeds global standards. Coordinating systems as complex as Australia’s health sector has long been recognised as a persistent challenge( 4 ). The living network model introduced in this study offers a practical response: by compressing and representing the relationships among health evidence organisations, it provides a simplified yet dynamic interface for collaboration and system navigation. Such visualisation and connectivity mechanisms can reduce inefficiencies and duplication( 11 ), enable more agile responses under pressure, and enhance the collective capacity for informed, demand-driven decision-making. Future research should examine how maintaining and governing this model could contribute to sustained coordination, transparency, and adaptive learning across the national evidence ecosystem. Finally, a further implication of the analysis relates to system governance and sustainability. Despite strong capacity within individual institutions, Australia lacks a unifying mechanism to coordinate strategic investment, standard setting, and performance monitoring across the evidence continuum. The RESSA findings support calls from the Global Evidence Commission for the establishment of national evidence-support systems that are coherent, transparent, and sustainable. Developing and maintaining a “living” network model, as initiated in this study, could enable continuous monitoring, stakeholder engagement, and evidence-informed reform. Such a platform could also facilitate rapid response capacity in public health crises by identifying and mobilising relevant expertise. Conclusion The Australian Rapid Evidence Support System Assessment establishes a foundational understanding of the national evidence ecosystem. It highlights both the depth of expertise and translational capability across sectors, as well as the need for improved coordination, equity, and strategic governance. Embedding this living network model within a broader systems-strengthening framework offers an opportunity to enhance coherence, responsiveness, and impact— positioning Australia as a global leader in evidence-informed public health decision-making. Taken together, these findings illustrate both the strengths and the vulnerabilities of Australia’s evidence ecosystem. The system is distinguished by a high degree of maturity and technical sophistication but constrained by fragmentation, regional inequity, and inconsistent integration between supply and demand functions. Addressing these issues will require deliberate policy attention, investment in coordination mechanisms, and a commitment to open data and collaboration. The Australian phase one RESSA thus provides a baseline for future dialogue and collective action to strengthen the national evidence-support system in alignment with international best practice. Declarations Authorship (Following CRediT taxonomy) ZL: conceptualisation, methodology, supervision, validation, writing – original draft; BP: methodology, supervision, validation, writing - original draft; DS: Data curation/analysis, visualisation, writing - original draft; PB: validation, review and editing. Declaration of funding This work was not funded. Data availability statement The data that supports this study are openly available at https://kumu.io/. The data collected for inclusion was derived from websites in the public domain. Conflict of Interest The authors declare no conflict of interest. References Challenges GCoEtAS. Evidence Commission Update 2023: Strengthening Domestic Evidence-Support Systems, Enhancing the Global Evidence Architecture, and Putting Evidence at the Centre of Everyday Life. Hamilton: McMaster Health Forum; 2023. Bell J, Head BW. Knowledge mobilisation intermediaries operating at the research-policy-practice nexus in Australia. Developing Practice: The Child, Youth and Family Work Journal. 2017(48):7–29. Lawrence A. Where is the evidence? Research publishing and public policy in Australia: RMIT University; 2024. Calder R, Dunkin R, Rochford C, Nichols T. Australian health services: too complex to navigate: a review of the national reviews of Australia's health service arrangements. 2019. 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Soares-Weiser K, Jordan Z, Boeira L, Mahlanza‐Langer L, Moy W, Mijumbi R, et al. Call to action: building a better future together, powered by evidence, guided by collective impact. Cochrane Database of Systematic Reviews. 2025(9). Wolfenden L, Ziersch A, Robinson P, Lowe J, Wiggers J. Reducing research waste and improving research impact. Aust NZJ Public Health. 2015;39(4):303–4. Additional Declarations No competing interests reported. Supplementary Files Appendices.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 25 Mar, 2026 Submission checks completed at journal 25 Mar, 2026 First submitted to journal 23 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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2","display":"","copyAsset":false,"role":"figure","size":458463,"visible":true,"origin":"","legend":"\u003cp\u003eAustralian health ecosystem entity types according to the Commission\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9205700/v1/21bbcb4f1cd7fc2773b793b8.png"},{"id":108490773,"identity":"88b7eac7-e69b-49c7-9d6f-e653041dc9b2","added_by":"auto","created_at":"2026-05-05 09:48:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":364686,"visible":true,"origin":"","legend":"\u003cp\u003eHybrid evidence intermediary producers (HIPs)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9205700/v1/f62ceb2f388dacaa2f471edf.png"},{"id":108020062,"identity":"29d13fe6-3006-43fa-9ede-e09f8f7ebed0","added_by":"auto","created_at":"2026-04-28 14:17:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":514666,"visible":true,"origin":"","legend":"\u003cp\u003eDecision Makers (DM)\u003c/p\u003e","description":"","filename":"floatimage41.png","url":"https://assets-eu.researchsquare.com/files/rs-9205700/v1/18d6b6eb46c9fbd358375dad.png"},{"id":108020063,"identity":"b54d741d-b4fd-4f6d-b551-189e2471f4c4","added_by":"auto","created_at":"2026-04-28 14:17:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":324598,"visible":true,"origin":"","legend":"\u003cp\u003eHybrid decision maker intermediaries (HDIs)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9205700/v1/c0a7d571df0a83a44645f994.png"},{"id":108181736,"identity":"d43e4f7c-bfa5-4eb2-bea7-c39988f1d1af","added_by":"auto","created_at":"2026-04-30 08:58:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":571063,"visible":true,"origin":"","legend":"\u003cp\u003eExample cross-section of Hybrid decision maker intermediaries (HDIs)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9205700/v1/6d8988b96bdbf13d84d1556c.png"},{"id":108803672,"identity":"741e171f-249d-4ae1-892f-5cabcd43088d","added_by":"auto","created_at":"2026-05-08 15:03:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":302586,"visible":true,"origin":"","legend":"\u003cp\u003eEvidence producers (EPs)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9205700/v1/16d637d660bb5898559f401b.png"},{"id":108020065,"identity":"3889b74d-bf9a-4c68-88ad-e741e1f58c5a","added_by":"auto","created_at":"2026-04-28 14:17:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":139896,"visible":true,"origin":"","legend":"\u003cp\u003eCommission categories by state\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9205700/v1/274bd2228458e209c9443055.png"},{"id":108020066,"identity":"1063f4fa-18a5-4845-b7dc-039061ecaa8f","added_by":"auto","created_at":"2026-04-28 14:17:21","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":744326,"visible":true,"origin":"","legend":"\u003cp\u003eExample cross-section of Climate and Health Alliance network (CAHA) affiliations\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9205700/v1/6b3490caf5f15322f1d35407.png"},{"id":108020067,"identity":"aa3ab588-bc2f-4853-8ccb-490ca0da34d9","added_by":"auto","created_at":"2026-04-28 14:17:21","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":662705,"visible":true,"origin":"","legend":"\u003cp\u003eExample cross-section of Australian Living Evidence Collaboration (ALEC) network affiliations\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9205700/v1/14994e83312fb5b599a7e4d4.png"},{"id":108181813,"identity":"bdcc590c-710f-4bed-a9aa-a15db17928c0","added_by":"auto","created_at":"2026-04-30 08:58:56","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":706084,"visible":true,"origin":"","legend":"\u003cp\u003eExample cross-section of The Sax Institute network affiliations\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-9205700/v1/5f544920e28c14baafde1c37.png"},{"id":108809063,"identity":"8bff17bc-ed7f-40f0-b17b-b1ec2dbd39b0","added_by":"auto","created_at":"2026-05-08 15:49:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5342223,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9205700/v1/d04ad397-add0-4a83-9d75-50174cc82160.pdf"},{"id":108020058,"identity":"30c8db7d-c5e0-431b-92cb-08cd869305fd","added_by":"auto","created_at":"2026-04-28 14:17:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":46913,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-9205700/v1/ab4705ab4013a99a421ecb05.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Australian Rapid Evidence Support System Assessment: mapping Australia’s Health Evidence Ecosystem","fulltext":[{"header":"Key points","content":"\u003cp\u003e• This study provides the first systematic mapping of Australia’s health evidence ecosystem using the Global Evidence Commission’s taxonomy.\u003c/p\u003e\u003cp\u003e• Findings reveal strong translational capacity but fragmented coordination and regional inequities.\u003c/p\u003e\u003cp\u003e• A small number of “super-connector” organisations bridge evidence supply and demand.\u003c/p\u003e\u003cp\u003e• Maintaining a living, updatable network model could support national collaboration, reduce duplication, and strengthen Australia’s evidence-support system for public health decision-making.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe Global Commission on Evidence to Address Societal Challenges (hereafter the Global Evidence Commission)(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) has called for coordinated national efforts to strengthen evidence-support systems that ensure decision-making is informed by trustworthy, timely, and relevant evidence. To support this global agenda, Rapid Evidence Support System Assessments (RESSAs) have been developed to identify existing capacities, gaps, and opportunities for improved coordination within national evidence ecosystems. These assessments provide a structured means of understanding how countries generate, translate, and use evidence to inform health and social policy(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin this context, Australia’s health evidence infrastructure has evolved into a complex network of evidence producers, intermediaries, and users of evidence, encompassing universities, government agencies, research collaborations, think tanks, and non-government organisations(\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e). However, despite considerable national investment in evidence generation and translation, questions remain about the coherence, efficiency, and equity of the system as a whole(\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e). Mapping this ecosystem provides an opportunity to identify where strengths and gaps exist, and to inform future policy and infrastructure development aimed at improving public health outcomes.\u003c/p\u003e \u003cp\u003eThe impetus for this work also aligns with Australia’s commitment to the United Nations Sustainable Development Goals (SDGs)(\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e). Strengthening the national evidence ecosystem contributes directly to these global priorities by promoting transparent, accountable, and effective institutions, advancing innovation in evidence synthesis and translation, and ensuring that the best available knowledge underpins public health decisions.\u003c/p\u003e \u003cp\u003eObjectives\u003c/p\u003e \u003cp\u003eThe objective of this study was to conduct phase one of a Rapid Evidence Support System Assessment to provide a baseline understanding of the Australian health evidence infrastructure, including key contributors on the evidence demand and supply side, in line with the Global Evidence Commission. The aim was to generate a comprehensive and dynamic baseline model that can inform national and international dialogue on strengthening evidence infrastructure for equitable, efficient, and sustainable public health decision-making. The next phase will involve key informant interviews to review the findings of this first phase.\u003c/p\u003e \n\n \n\n\n\n\n\n"},{"header":"Methods","content":"\u003ch3\u003eDesktop audit\u003c/h3\u003e\u003cp\u003eA structured desktop audit was conducted using public internet search engines to identify primary organisations constituting the Australian evidence health ecosystem. Organisations and their functional roles were classified according to the Global Evidence Commission’s technical lexicon, focusing on Domain 2 – the evidence interface between evidence production (supply) and evidence-informed decision-making (demand)(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e). Websites and online documents describing organisational functions, governance structures, and inter-institutional relationships were reviewed. Data were extracted into a mapping matrix capturing organisation name, sector, functional role(s), and affiliations. Kumu, an online cloud-based platform for mapping and visualising complex relationships (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kumu.io/\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was then used to develop a living network model(\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e). National organisations were coded by head office location and functionally categorised according to the technical roles they play. This entails that many will satisfy more than one category. Additionally, organisations were mapped by their functional roles within the evidence ecosystem, with some classified under multiple Commission categories when their remit, programs, or services spanned more than one function of the evidence-support system.\u003c/p\u003e\u003cp\u003eThe network model’s ties (edges) were structured according to their online listings of affiliated organisations; however, international affiliates were excluded to delimit the scope to the domestic ecosystem, thereby truncating the reach of the actual network’s in/out degrees.\u003c/p\u003e\u003ch2\u003eSearch terms\u003c/h2\u003e\u003cp\u003eThe search strategy combined general and targeted terms drawn from the Commission’s taxonomy of evidence-support systems. Searches included combinations of: Australian evidence-based health, Australian government health and aged care department, Australian government primary body, Australian government secondary body, Australian government tertiary body, Australian health evidence network, Australian health ecosystem, Australian health department, Australian health institute, Australian health organisation, Australian health think tank, health care companies in Australia, mental health organisations in Australia, medical associations in Australia, medical research institutes in Australia.\u003c/p\u003e\u003ch3\u003eInclusion criteria\u003c/h3\u003e\u003cp\u003eFormal designations of Australian health sector/ health service organisations, health organisations, health research units, university health departments, health network, health committee, health foundation, government body (Health and Aged Care), primary bodies (non-corporate commonwealth entity, corporate commonwealth entity, commonwealth company, secondary statutory structures, statutory advisory structure, statutory office holder, offices and committees secondary non-statutory structure), peak bodies.\u003c/p\u003e\u003ch3\u003eExclusion criteria\u003c/h3\u003e\u003cp\u003eIndividual researchers, laboratories, empirical research, medical trials, material evidence syntheses (i.e. the products of evidence production), hospitals that did not clearly specify units or programs of work related to evidence-based activities.\u003c/p\u003e\u003ch3\u003eSocial Network Analysis\u003c/h3\u003e\u003cp\u003eThe extracted relational data were analysed using social network analysis tools in Kumu to explore the structure and dynamics of Australia’s health evidence ecosystem(\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e). Nodes represented organisations and edges denoted declared affiliations. Centrality measures (degree, betweenness, closeness, eigenvector, and reach) were calculated to identify highly connected and influential organisations. Directed ties distinguished outgoing and incoming relationships, approximating information flow and perceived influence authority. Results were used to interpret the overall configuration of the ecosystem, identify key bridging actors and potential coordination gaps, and inform future phases of RESSA application in Australia.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOverview of the Australian evidence ecosystem\u003c/h2\u003e \u003cp\u003eThe Phase 1 RESSA identified 1,382 unique organisations, performing 1,653 functional roles, operating within Australia's health evidence ecosystem (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Organisations were functionally classified according to the Commission\u0026rsquo;s technical taxonomy and grouped into five broad domains: decision-makers (DM), hybrid decision maker intermediary (HDI), evidence-intermediary (EI), hybrid evidence intermediary producers (HIP), and evidence-producers (EP). A total of 215 organisations were mapped to more than one Commission category, reflecting the breadth of their functions across evidence supply, translation, and demand. This overlap accounts for the higher total of functional classifications (n\u0026thinsp;=\u0026thinsp;1653) relative to the number of distinct organisations (n\u0026thinsp;=\u0026thinsp;1382) captured in the network. The resulting distribution revealed a dense and decentralised network, characterised by numerous professional and organisational nodes, modest inter-jurisdictional integration, and substantial concentration of activity within the eastern states (see supplementary file 1 for a full list of organisations and their functional classifications).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAustralian health ecosystem organisational functional classifications according to the Commission\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAustralian health ecosystem: Organisational functional classifications according to the Commission\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSub-type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSub-type total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eDecision Makers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment policy makers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganisational leaders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCitizens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM Sub-total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e467\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eHybrid Decision maker Intermediary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnical units within multilateral organizations\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDomestic commissions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment advisory bodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment science advice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment evidence support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDI Sub-total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e306\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEvidence Intermediary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFact-checking organizations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScience academies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThink tanks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge Translation Platform/ knowledge brokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEI Sub-total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e181\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eHybrid evidence Intermediary Producers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact-oriented data-analytics units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact-oriented modelling units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact-oriented evaluation units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact-oriented behavioural/ implementation research unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact-oriented qualitative insights unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact-oriented evidence synthesis units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology-assessment units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuideline units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHIP Sub-total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e535\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEvidence Producers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e164\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1653\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComposition by functional domain\u003c/h3\u003e\n\u003cp\u003eAmong the five domains (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), organisations performing HIP functions (n\u0026thinsp;=\u0026thinsp;535; 32%) represented the largest category (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), encompassing impact-oriented evidence synthesis units, modelling, evaluation, behavioural science and implementation research organisations. The second-largest cluster comprised Decision-Makers (n\u0026thinsp;=\u0026thinsp;467; 28%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), including 25 government policymaking bodies, 48 organisational leaders, 365 professional associations, and 30 citizen-focused mechanisms. Organisations with HDI functions (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) accounted for 306 (19%), including government advisory bodies and government evidence support, while organisations performing EI functions (think tanks, science academies, and knowledge-translation platforms) numbered 181 (11%). Organisations classified as EPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) constituted the remaining 164 (10%), typically located within universities and collaborative research structures.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eGeographic distribution\u003c/h3\u003e\n\u003cp\u003eState-based analysis demonstrated marked regional asymmetry (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). New South Wales (n\u0026thinsp;=\u0026thinsp;517) and Victoria (n\u0026thinsp;=\u0026thinsp;501) together hosted over half of all organisational classifications, reflecting the concentration of national research funding and headquarters functions. The Australian Capital Territory (ACT) exhibited disproportionate representation of organisations with HDI functions (n\u0026thinsp;=\u0026thinsp;171; 54% of the national total), consistent with its role as the seat of federal government and home to advisory and analytical agencies. Western Australia (n\u0026thinsp;=\u0026thinsp;136) and Queensland (n\u0026thinsp;=\u0026thinsp;158) showed broad functional coverage but limited inter-state linkages. Smaller jurisdictions, including South Australia (n\u0026thinsp;=\u0026thinsp;127), the Northern Territory (n\u0026thinsp;=\u0026thinsp;34), and Tasmania (n\u0026thinsp;=\u0026thinsp;26), were under-represented, highlighting geographic inequities in national evidence infrastructure. 76 organisations operate in multiple jurisdictions. Consequently, when aggregating commission categories by state, the totals exceeded those derived from unique organisation counts in the dataset.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCommission categories by state\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eCommission categories by state\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes by state\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. \u003cb\u003eWestern Australia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. \u003cb\u003eNorthern Territory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. \u003cb\u003eSouth Australia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. \u003cb\u003eQueensland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. \u003cb\u003eNew South Wales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. \u003cb\u003eAustralian Capital Territory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. \u003cb\u003eVictoria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. \u003cb\u003eTasmania\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e514\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e318\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e184\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e599\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e178\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNetwork topology and centrality\u003c/h2\u003e \u003cp\u003eSocial-network analysis of relational data (edges\u0026thinsp;=\u0026thinsp;2690) revealed a polycentric structure with multiple bridging nodes rather than a single dominant hub. Several metrics were used to examine structural dynamics (See Appendix 2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBetweenness centrality\u003c/h2\u003e \u003cp\u003eBetweenness scores identified nodes that act as information bridges. The Australian Health Research Alliance (AHRA) exhibited the highest betweenness (0.023), followed by the NHMRC HEAL National Research Network (0.016), National Health and Medical Research Council (NHMRC) (0.016) and Monash University (0.015).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCloseness centrality\u003c/h2\u003e \u003cp\u003eCloseness centrality highlighted organisations able to disseminate information rapidly across the network. The Paul Ramsay Foundation ranked first (0.249), followed by the Better Health Channel (0.226), Centre for Evidence and Implementation (0.214), and NHMRC HEAL Network (0.204). These organisations combine philanthropic and/or funding reach, public-facing communication, and translational capacity, suggesting that influential connectors extend beyond traditional government or research institutions\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDegree and directionality\u003c/h2\u003e \u003cp\u003eDegree centrality confirmed the Paul Ramsay Foundation (187 links), Better Health Channel (154 links), Climate and Health Alliance (98 links) and The Sax Institute (86 links) as major hubs, with substantial connections to both professional networks and citizens. Outdegree scores (outgoing links) were similarly dominated by these four actors, indicating high broadcast capacity. At the same time, indegree measures, reflecting advisory influence, were led by Monash University (34) and the University of Sydney (25), reinforcing the central role of large universities as evidence producers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEigenvector and systemic influence\u003c/h2\u003e \u003cp\u003eEigenvector centrality, which weights influence by the quality of connections, identified Monash University (0.0132), AHRA (0.0108), Deakin University (0.0105) and NHMRC (0.0103) as the most influential actors, each embedded within clusters of similarly well-connected organisations. These findings illustrate an academically anchored yet distributed network with high redundancy among major universities, an indicator of resilience, but also potential fragmentation when coordination mechanisms are absent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCross-impact and leverage analysis\u003c/h2\u003e \u003cp\u003eMICMAC analysis, exploring each element\u0026rsquo;s exposure and influence, further clarified system leverage points. The Hearing Health Sector Alliance (HHSA) demonstrated maximal overall influence, followed closely by the Australian Health Policy Collaboration (AHPC) and TAPPC, indicating high potential to affect other actors\u0026rsquo; behaviour. Exposure scores, i.e. how much an element is affected by others, were highest for professional and member-based organisations such as the Medical Women\u0026rsquo;s Society and the Health Roundtable, signalling vulnerability to external shifts in funding or policy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEmerging \u0026lsquo;super-connectors\u0026rsquo;\u003c/h2\u003e \u003cp\u003eA subset of organisations, CAHA, Australian Living Evidence Collaboration, Sax Institute, CEI, AHRA, NHMRC HEAL National Research Network, Monash University, Better Health Channel, Paul Ramsay Foundation and others, consistently scored within the top decile across multiple centrality measures (see Appendix 2). These organisations bridge domains (supply, demand, and intermediaries) and maintain multi-sectoral collaborations. Their cross-cutting influence positions them as potential backbone organisations for a future coordinated evidence-support system (see Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eThematic patterns across Commission domains\u003c/h2\u003e \u003cp\u003eMapping across the Commission\u0026rsquo;s functional categories revealed five overarching themes:\u003c/p\u003e \u003cp\u003e1. \u003cb\u003ePolicy density and advisory duplication.\u003c/b\u003e The ACT hosts a concentration of advisory and analytic units (e.g. Science and Policy Interface, Chief Scientist structures) with overlapping mandates, suggesting potential efficiency gains through consolidation.\u003c/p\u003e \u003cp\u003e2. \u003cb\u003eKnowledge-translation strength but coordination gaps.\u003c/b\u003e Australia exhibits an exceptionally large EI cohort (144 knowledge-brokerage platforms), yet it has limited formal mechanisms to connect these intermediaries to policy demand.\u003c/p\u003e \u003cp\u003e3. \u003cb\u003eDominance of impact-oriented evidence production.\u003c/b\u003e The HIP domain reflects a strong culture of applied evidence, including implementation and behavioural-science units, but inconsistent integration with national decision-making forums.\u003c/p\u003e \u003cp\u003e4. \u003cb\u003eAcademic gravitational centres.\u003c/b\u003e Eigenvector results reinforce the dominance of the Group-of-Eight universities and allied research institutes as system anchors.\u003c/p\u003e \u003cp\u003e5. \u003cb\u003ePeripheral under-representation.\u003c/b\u003e The scarcity of organisations in smaller states and territories underscores regional inequities in access to evidence-support infrastructure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eNetwork morphology\u003c/h2\u003e \u003cp\u003eVisualisation through the living network model revealed a multi-layered, small-world topology with dense intra-state clustering and limited inter-state edges. Clusters aligned with state health department structures, national research alliances, and thematic initiatives (e.g. climate and health, women\u0026rsquo;s health, rural health). Cross-domain links were thinner, indicating the need for structured national coordination mechanisms to bridge the \u0026ldquo;evidence interface\u0026rdquo; between production and use.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis phase one Australian Rapid Evidence Support System Assessment (RESSA) represents the first national mapping and network analysis of the organisations and their functional roles that constitute Australia\u0026rsquo;s health evidence ecosystem. By adopting the Commission\u0026rsquo;s technical lexicon, this study provides a structured and comparable taxonomy of actors involved in evidence generation, translation, and use. The approach enabled consistent classification and facilitated alignment with international initiatives(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), strengthening Australia\u0026rsquo;s capacity to engage in global dialogues on evidence infrastructure reform, such as those occurring as part of the Evidence Synthesis Infrastructure Collaborative (ESIC)(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe findings demonstrate that Australia possesses a mature and diverse evidence ecosystem characterised by substantial translational capacity and a strong culture of applied research. The dominance of organisations performing hybrid intermediary-producer functions, particularly in behavioural and implementation science, data analytics, and evaluation, reflects an advanced orientation toward evidence use in practice and policy. This distribution highlights the evolution of the Australian research landscape from traditional evidence production to a globally leading integrated model of knowledge translation and impact measurement.\u003c/p\u003e \u003cp\u003eHowever, the network analysis also reveals fragmentation and duplication, particularly among advisory and analytic bodies concentrated in the Australian Capital Territory. The coexistence of multiple government science advice and evidence-support mechanisms suggests potential inefficiencies and a need for greater coordination and/or consolidation of workflows. Rationalisation of overlapping functions could enhance system efficiency and ensure that advisory processes are coherent, transparent, and responsive to the needs of policymakers and practitioners.\u003c/p\u003e \u003cp\u003eThe uneven geographic distribution of organisations represents another structural challenge. Concentration within the eastern states and the relative under-representation of organisations in smaller jurisdictions, while not surprising, risk perpetuating inequities in access to evidence-support infrastructure. A more balanced distribution of evidence capabilities, potentially supported by digital platforms, virtual collaborations, and regional nodes, could enhance inclusivity and strengthen the national evidence base. Addressing these disparities aligns with national public health priorities under the SDGs, particularly those related to reducing inequalities and promoting strong institutions.\u003c/p\u003e \u003cp\u003eAt the systemic level, the Australian evidence ecosystem exhibits a polycentric and resilient structure with multiple bridging nodes rather than a single dominant hub. Organisations such as the Paul Ramsay Foundation, the Centre for Evidence and Implementation, the Sax Institute, the Australian Living Evidence Collaboration and others act as \u0026ldquo;super-connectors\u0026rdquo; across the evidence interface (Appendix 2). Their influence extends beyond traditional academic or government domains, reflecting a diversification of leadership within the evidence system. These cross-sectoral connectors present an opportunity to establish coordinated national mechanisms that could operate as backbone organisations for a living, adaptive evidence-support infrastructure. Although this phase one RESSA focused on mapping domestic actors within Australia\u0026rsquo;s evidence ecosystem, it is also important to acknowledge the established role of globally networked evidence organisations such as Cochrane, JBI, Guidelines International Network, Monash Sustainable Development Institute and others. These organisations serve as globally connected hubs that both inform and are informed by Australia\u0026rsquo;s evidence ecosystem, facilitating bidirectional knowledge flows, methodological innovation, and cross-country collaboration. Their long-standing engagement with WHO, UN agencies, and global research alliances positions them as valuable partners in strengthening Australia\u0026rsquo;s evidence-support infrastructure and ensuring its alignment with international efforts.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe datasets included here represent a dynamical ecosystem and are thus neither exhaustive nor complete. Network density has likely changed since publication due to the shifting nature of the evidence ecosystem\u0026rsquo;s organisations and their affiliations. This living network model, therefore, will at once increase in fidelity as more data and verification become available, but will always only ever approximate a structure-in-motion.\u003c/p\u003e \u003cp\u003eFurthermore, the nature of the desktop audit and the functional classification of organisations according to information available online, almost certainly resulted in unaccounted for relevant organisations and inaccurate categorisations, which likely under-estimate the significant roles they play.\u003c/p\u003e \u003cp\u003eThe strict application of the Global Evidence Commission lexicon enhanced classification reliability but may have obscured local nuances and emergent domains of activity. Categories such as big data collaborations, digital health platforms, libraries, and professional colleges occupy ambiguous positions within the taxonomy yet play pivotal roles in evidence dissemination and use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eImplications for future research\u003c/h2\u003e \u003cp\u003eFuture research should examine how such organisations contribute to the national evidence ecosystem and explore hybrid classifications that better capture their integrative functions. Comparative analysis with alternative frameworks could also illuminate areas where Australia\u0026rsquo;s system diverges from or exceeds global standards.\u003c/p\u003e \u003cp\u003eCoordinating systems as complex as Australia\u0026rsquo;s health sector has long been recognised as a persistent challenge(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The living network model introduced in this study offers a practical response: by compressing and representing the relationships among health evidence organisations, it provides a simplified yet dynamic interface for collaboration and system navigation. Such visualisation and connectivity mechanisms can reduce inefficiencies and duplication(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), enable more agile responses under pressure, and enhance the collective capacity for informed, demand-driven decision-making. Future research should examine how maintaining and governing this model could contribute to sustained coordination, transparency, and adaptive learning across the national evidence ecosystem.\u003c/p\u003e \u003cp\u003eFinally, a further implication of the analysis relates to system governance and sustainability. Despite strong capacity within individual institutions, Australia lacks a unifying mechanism to coordinate strategic investment, standard setting, and performance monitoring across the evidence continuum. The RESSA findings support calls from the Global Evidence Commission for the establishment of national evidence-support systems that are coherent, transparent, and sustainable. Developing and maintaining a \u0026ldquo;living\u0026rdquo; network model, as initiated in this study, could enable continuous monitoring, stakeholder engagement, and evidence-informed reform. Such a platform could also facilitate rapid response capacity in public health crises by identifying and mobilising relevant expertise.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe Australian Rapid Evidence Support System Assessment establishes a foundational understanding of the national evidence ecosystem. It highlights both the depth of expertise and translational capability across sectors, as well as the need for improved coordination, equity, and strategic governance. Embedding this living network model within a broader systems-strengthening framework offers an opportunity to enhance coherence, responsiveness, and impact— positioning Australia as a global leader in evidence-informed public health decision-making.\u003c/p\u003e \u003cp\u003eTaken together, these findings illustrate both the strengths and the vulnerabilities of Australia’s evidence ecosystem. The system is distinguished by a high degree of maturity and technical sophistication but constrained by fragmentation, regional inequity, and inconsistent integration between supply and demand functions. Addressing these issues will require deliberate policy attention, investment in coordination mechanisms, and a commitment to open data and collaboration. The Australian phase one RESSA thus provides a baseline for future dialogue and collective action to strengthen the national evidence-support system in alignment with international best practice.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthorship\u003c/h2\u003e\n\u003cp\u003e(Following CRediT taxonomy) ZL: conceptualisation, methodology, supervision, validation, writing \u0026ndash; original draft; BP: methodology, supervision, validation, writing - original draft; DS: Data curation/analysis, visualisation, writing - original draft; PB: validation, review and editing.\u003c/p\u003e\n\n\u003ch2\u003eDeclaration of funding\u003c/h2\u003e\n\u003cp\u003eThis work was not funded.\u003c/p\u003e\n\n\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eThe data that supports this study are openly available at https://kumu.io/. The data collected for inclusion was derived from websites in the public domain.\u003c/p\u003e\n\n\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChallenges GCoEtAS. Evidence Commission Update 2023: Strengthening Domestic Evidence-Support Systems, Enhancing the Global Evidence Architecture, and Putting Evidence at the Centre of Everyday Life. Hamilton: McMaster Health Forum; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBell J, Head BW. Knowledge mobilisation intermediaries operating at the research-policy-practice nexus in Australia. Developing Practice: The Child, Youth and Family Work Journal. 2017(48):7\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawrence A. Where is the evidence? Research publishing and public policy in Australia: RMIT University; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalder R, Dunkin R, Rochford C, Nichols T. Australian health services: too complex to navigate: a review of the national reviews of Australia's health service arrangements. 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrolan CE, Hall N, Creamer S, Johnston I, Dantas JA. Health's role in achieving Australia's Sustainable Development Goal commitments. The Medical Journal of Australia. 2019;210(5):204.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJordan Z, Pilla B. From agenda to action: JBI Evidence Synthesis and the United Nations Sustainable Development Goals. JBI Evidence Synthesis. 2024;22(3):364\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoenen J, Glass L-M, Sanderink L. Two degrees and the SDGs: a network analysis of the interlinkages between transnational climate actions and the Sustainable Development Goals. Sustainability Science. 2022;17(4):1489\u0026ndash;510.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhelan B, Tierney M, Burke NN, Saif-Ur-Rahman K, Creely C, Duffy T, et al. A Rapid Evidence Support System Assessment (RESSA) of health policymaking in Ireland\u0026ndash;A Protocol. HRB Open Research. 2025;8:70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLavis JN, Grimshaw JM, Stewart R, Elliott J, Moy W, Meerpohl JJ. SHOW ME the evidence: Features of an approach to reliably deliver research evidence to those who need it. Wiley Online Library; 2024. p. e70006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoares-Weiser K, Jordan Z, Boeira L, Mahlanza‐Langer L, Moy W, Mijumbi R, et al. Call to action: building a better future together, powered by evidence, guided by collective impact. Cochrane Database of Systematic Reviews. 2025(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolfenden L, Ziersch A, Robinson P, Lowe J, Wiggers J. Reducing research waste and improving research impact. Aust NZJ Public Health. 2015;39(4):303\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"health-research-policy-and-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hrps","sideBox":"Learn more about [Health Research Policy and Systems](http://health-policy-systems.biomedcentral.com/)","snPcode":"12961","submissionUrl":"https://submission.nature.com/new-submission/12961/3","title":"Health Research Policy and Systems","twitterHandle":"@HarpsJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Australia, evidence ecosystem, network mapping, network analysis","lastPublishedDoi":"10.21203/rs.3.rs-9205700/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9205700/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: To map and analyse Australia’s health evidence ecosystem as part of a Rapid Evidence Support System Assessment (RESSA), identifying key structures, gaps and opportunities to strengthen national evidence-support capacity.\u003c/p\u003e\n\u003cp\u003eMethods: Publicly available online data were used to identify organisations active in evidence generation, translation and use. Organisations were functionally classified according to the Global Evidence Commission’s taxonomy and mapped using Kumu software. Network metrics (degree, betweenness, closeness, eigenvector and reach) were calculated to assess structure, influence and connectivity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: A total of 1382 organisations performing 1,653 functional roles were identified across five domains: decision-makers (28%), hybrid decision-maker intermediaries (19%), evidence intermediaries (11%), hybrid intermediary–producers (32%) and evidence producers (10%). The network displayed a decentralised, polycentric structure anchored by several “super-connectors” bridging supply and demand, notably the Sax Institute, Australian Living Evidence Collaboration, Centre for Evidence and Implementation, Paul Ramsay Foundation, NHMRC HEAL National Research Network and Monash University. Geographic analysis revealed strong concentration in New South Wales, Victoria and the Australian Capital Territory, with limited representation elsewhere.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults implications: Australia’s evidence ecosystem demonstrates strong translational capacity but fragmented coordination and regional inequities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions: This phase-one RESSA provides the first national baseline of Australia’s health evidence ecosystem. Maintaining a living, updatable network model could support coordination, reduce duplication and enhance evidence-informed public health decision-making.\u003c/p\u003e","manuscriptTitle":"Australian Rapid Evidence Support System Assessment: mapping Australia’s Health Evidence Ecosystem","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 14:17:09","doi":"10.21203/rs.3.rs-9205700/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"100060343172513902480823425699853194325","date":"2026-04-22T10:11:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T09:29:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-25T05:55:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T05:55:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Health Research Policy and Systems","date":"2026-03-24T02:18:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"health-research-policy-and-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hrps","sideBox":"Learn more about [Health Research Policy and Systems](http://health-policy-systems.biomedcentral.com/)","snPcode":"12961","submissionUrl":"https://submission.nature.com/new-submission/12961/3","title":"Health Research Policy and Systems","twitterHandle":"@HarpsJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3fdc593-f4c9-4443-ab4b-56dead84264b","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T14:17:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 14:17:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9205700","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9205700","identity":"rs-9205700","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}