Structural characteristics of municipal health promotion networks: Insights from egocentric network analysis in municipal settings in Germany

Structural characteristics of municipal health promotion networks: Insights from egocentric network analysis in municipal settings in Germany | 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 Structural characteristics of municipal health promotion networks: Insights from egocentric network analysis in municipal settings in Germany Hanna Richter, Tatiana Mamontova, Lisa Kühne This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9212156/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Health professionals increasingly coordinate integrated public health services across complex municipal structures. While described as brokers or network stewards, little is known about how health professionals network properties shape their coordination capacity. This study examines how structural network properties influence health professionals' ability to direct and coordinate integrated public health services at the municipal level. Methods Egocentric social network analysis mapped networks of 14 health professionals working in German primary schools and neighbourhood settings. Using name-generator surveys, we captured alters, inter-alter ties, and attributes across sectors. Networks were analysed in Gephi 1.0 , calculating size, density, clustering coefficient, betweenness/closeness centrality, modularity (with/without ego), and path length. ForceAtlas2 visualisations identified structural patterns against coordination outcomes Results The networks exhibited hybrid structures, combining centralised hubs, cohesive modular subgroups and small-world properties. The high level of modularity reflected the functional segmentation of areas such as education, social spaces, and professional peer domains. Although star-shaped networks enabled rapid coordination, they were highly vulnerable due to their dependence on the health professional in the central position. By contrast, mesh-like and small-world structures maintained connectivity and brokerage potential through distributed ties, demonstrating structural resilience. Conclusion The network structure is the fundamental determinant of coordination capacity in municipal health promotion. Although centralised forms enable a rapid response, their fragility highlights the limitations of individual coordination efforts. Distributed small-world configurations are better able to sustain cross-sectoral collaboration. In order to fulfil the Health in All Policies mandate of municipalities, it is essential to implement formal governance mechanisms, including institutionalised coordination functions, protected networking time, cross-sectoral steering committees and training in network-spanning activities. This shifts coordination from discretionary individual effort to structural organisational capacity, ensuring the sustainable integration of preventive services across fragmented local health systems. Network governance intersectoral collaboration integrated prevention local health systems health equity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Under the Health in All Policies (HiAP) approach, municipalities have become pivotal in promoting health and equity by shaping supportive living conditions across sectors like health, education, youth welfare, and social services. However, administrative fragmentation, marked by the distribution of responsibilities across various governmental levels and independent authorities, hinders intersectoral coordination. Prevention responsibilities are spread out and rely on informal connections rather than formal institutional structures [ 1 , 2 ]. Integrated, community-based approaches aim to address this fragmentation by aligning health and social care efforts, reducing health inequalities and strengthening local health systems [ 3 , 4 ]. In this context, municipal networks have emerged as key governance mechanisms, bringing together actors from health care, social services, education and civil society. These networks aim to reduce fragmentation and strengthen collective capacity [ 5 , 6 ]. Although international evidence indicates that well-functioning community health networks can support evidence-based interventions and promote health equity [ 7 , 8 ], their implementation frequently remains constrained by limited organisational and policy support for sustained collaboration. As a result, service gaps persist, particularly in preventive services located at the intersections of health care, social services and education [ 4 , 9 ]. Network governance theory provides a useful conceptual perspective through which to analyse how coordination is achieved within these settings. Governance structures, such as lead-organisation models, shared governance arrangements or hybrid forms, determine the allocation of authority, responsibilities and resources within networks, while relational mechanisms such as trust, information exchange and brokerage facilitate collaboration across organisational boundaries [ 10 – 12 ]. Recent research is increasingly highlighting that governance arrangements are structural as well as institutional phenomena, as network design influences coordination dynamics, vulnerability or resilience [ 13 , 14 ]. Within these structures, a coordinator’s ability to foster collaboration depends strongly on the organisational context and the soft skills they employ to bridge sectoral divides [ 15 ]. In other word, the way in which a network is constructed determines the actions that can be taken within it. Although research describes health professionals as brokers or network stewards [ 16 , 17 ], little empirical work has examined how network structures shape their coordination capacity. Despite the existence of numerous theoretical models of intersectoral collaboration at the local level [ 18 ], it remains unclear how specific network configurations influence health professionals' ability to direct and integrate services. To address this gap, the present study investigates the following research question within the German municipal public health context: How do the structural properties of health professionals' networks influence their ability to direct and coordinate integrated public health services at the local level? By identifying enabling and constraining network configurations, it contributes practical insights for designing resilient, equitable governance structures that bridge sectoral divides and enhance preventive service delivery in fragmented municipal health systems. Methods Study Design The aim of this study was to examine how the structural properties of health professional networks influence health professionals' coordination capacity in municipal health promotion. We employed ego-centred social network analysis (SNA) to establish a conceptual link between governance theory and the practical implementation of health professionals. By focusing on the immediate relational environment of individual actors, the analysis can reveal the structural conditions under which coordination takes place. This approach provides insight into why some professionals coordinate integrated public health services effectively while others struggle, despite having similar formal mandates. Participants and Recruitment The data is based on 14 egocentric network maps of health professionals working in German primary schools and local communities. These health professionals have dual qualifications, consisting of a university degree in health sciences and training in a health-related profession, such as nursing. The sample size reflects the principles of purposive sampling and the goal of theoretical rather than statistical generalisation. Studies of this type, which focus on a single individual's network, rely on a small number of information-rich cases, as each ego-network provides detailed structural and contextual insights into collaboration patterns [ 19 ]. Participants were selected through an established collaboration specifically because their work involve building, maintaining and coordinating networks for municipal health promotion. During recruitment, efforts were made to ensure diversity in terms of professional background, years of experience and area of activity. Data Collection The data, conducted between July 2023 and March 2024, were collected using the software Vennmaker, a tool for visual network generation. Ethical approval was granted by the University of Bremen and all participants provided informed, written consent. As an egocentric design, each network reflects the actor’s perspective on their relevant relationships. Following the concentric circle model of Kahn and Antonucci [ 20 ], respondents were asked to identify the individuals, institutions or task groups with whom they collaborate professionally. Additional attributes (e.g. organisational affiliation, role, or sector) and relations between each actor were recorded. These questions were specifically developed for this data collection to elicit participants’ egocentric network perceptions. This approach enables intra- and interpersonal comparisons to be made during the evaluation, as it allows ego perceptions and structural tendencies to be identified across networks [ 21 ]. Participants also differentiated between formal and informal collaborations, providing information on the function and field of activity of each alter. This allows sectoral patterns to be identified and cross-domain linkages to be established. Data Analysis Network data were processed and analysed using Gephi 1.0. For each ego network, key structural indicators were calculated to assess network size, cohesion, centralisation and brokerage potential. Network size was measured as the total number of alters, reflecting relational scope. Density, which is the ratio of existing to possible ties, indicates relational cohesion. The clustering coefficient captures the degree of subgroup formation, signaling local cohesion and redundancy. Betweenness centrality, which is based on the sum of the proportions of the shortest paths between all pairs of nodes passing through a node, reflects brokerage potential. Closeness centrality is the reciprocal of the sum of the shortest path distances from one node to all the others and indicates reachability and accessibility [ 22 ]. To assess internal differentiation, modularity analysis was conducted twice for each ego network: once including the ego and once excluding it. In the latter case, the ego and all direct ties were removed to examine the structure among alters only. The modularity resolution was set to 1.0, which is appropriate for detecting fine-grained subgroup structures in small ego-centred network [ 23 ]. The network visualisations were created using the ForceAtlas2 layout algorithm. This algorithm arranges nodes by simulating the physical forces of their relational proximity. This approach enabled the identification of structural patterns, such as cliques, coordination clusters and ego dependencies. These patterns were then interpreted using established typologies (e.g. star-shaped, tree-like and circular configurations) and theoretical frameworks like small-world network theory [ 24 , 25 ]. The analysis linked structural properties with functional outcomes in order to derive recommendations for the governance of community health professionals. Results Overall Network Characteristics The structural indicators across the 14 ego-centred networks reveal moderately cohesive, yet functionally differentiated, collaboration environments (see Table 1 ). Network size ranged from 29 to 64 people, reflecting significant variations in coordination responsibilities. The majority of networks showed moderate average degree values, indicating that most actors maintained regular communication with a limited number of key partners. Clustering coefficients (0.283–0.717) demonstrate significant variation in local cohesion, suggesting that some professionals operate within tightly knit environments, while others manage more dispersed networks. Measures of betweenness centrality reveal that some individuals occupy critical bridging positions between subgroups, highlighting the extent to which coordination capacity is anchored in their field of activity. Average path lengths were consistently short (2,073 − 2,923), indicating high global reachability, which is typical of small-world tendencies. The modularity values range from 0.373 to 0.579. These values suggest that municipal health promotion networks are organised around semi-autonomous functional domains, rather than being fully integrated or fully fragmented. The analysis shows that health professionals networks comprise not only bilateral connections, but also bridging ties between distinct functional sub-areas. Overall, the descriptive indicators demonstrate that the networks offer sufficient differentiation to handle various tasks and sectors while facilitating swift communication across the municipal public health environment. Table 1 Network Characteristics of Ego-Centered Networks Participant Network Size Average Degree Edge density Average path length Global clustering coefficient Modularity Harmonic Closeness Betweenness centrality Local clustering coefficent Number of triangles Ego 1 53 4,83 0,093 2,495 0,424 0,373 0,814 0,437 0,033 11 Ego 2 49 6,776 0,141 2,715 0,451 0,445 0,67 0,379 0,132 18 Ego 3 41 8,293 0,207 2,761 0,375 0,417 0,609 0,278 0,054 5 Ego 4 38 6,0 0,162 2,462 0,656 0,452 0,796 0,539 0,105 18 Ego 5 40 11,8 0,303 2,073 0,716 0,419 0,9 0,495 0,174 66 Ego 6 42 7,33 0,179 2,281 0,517 0,423 0,852 0,523 0,08 11 Ego 7 38 6,526 0,176 2,424 0,596 0,485 0,727 0,317 0,12 11 Ego 8 44 5,591 0,13 2,496 0,717 0,579 0,833 0,762 0,03 10 Ego 9 61 7,639 0,127 2,468 0,647 0,476 0,815 0,497 0,062 29 Ego 10 45 5,067 0,115 2,923 0,397 0,455 0,707 0,458 0,065 10 Ego 11 29 6,897 0,246 2,723 0,556 0,482 0,676 0,365 0,218 12 Ego 12 55 10,291 0,191 2,315 0,539 0,405 0,863 0,55 0,107 64 Ego 13 64 6,321 0,1 2,678 0,438 0,433 0,68 0,241 0,08 18 Ego 14 63 8,762 0,141 2,788 0,283 0,444 0,7 0,403 0,116 35 Cluster structures The modularity analysis identified five distinct module types: The Neighbourhood and Social Space Module; the Education Module; the Professional Coordination and Peer Network Module; the Health Promotion and Health Prevention Module; and the Health Care Service Module. These modules organise health promotion coordination across ego networks. Content analysis further shows that they cluster into three recurring governance domains present in nearly all cases, suggesting that health professionals operate within structurally similar coordination environments. Although the strength of connections and the degree of integration between modules varies across ego-networks, the consistent presence of these domains emphasises the key areas of operation for cross-sectoral health promotion. Rather than being rigidly assigned to a single domain, actors may operate across multiple domains, depending on their functions, tasks and local priorities. This reflects the flexible and adaptive nature of intersectoral health promotion governance. In addition to these core modules, some ego-networks included highly specialised modules reflecting the personal interests or engagement of health professionals. Detailed module compositions, typical actors, and ego-network distributions are presented in Tables A1 and A2 (Appendix). An education module consisting of a wide range of educational institutions, including early childhood centres (Kitas), schools, and school-related professionals such as school social workers, psychologists, and regional advisory and support services. It also includes non-school educational providers, such as community education programmes and adult learning institutions. This module focuses on the intersection of educational and health-related responsibilities, bringing together the relevant processes and actors where educational and health-promoting tasks overlap. A professional coordination and peer network module , including health professionals working in schools or neighbourhoods, as well as coordination institutions such as health departments, to facilitate knowledge exchange, joint planning and strategic alignment across the health, education and social sectors. A neighbourhood and social space module , often comprising family and neighbourhood centres, youth services, welfare organisations, and local social organisations. This module focuses on prevention and health promotion activities in everyday living environments, involving various actors. Network types and control implications In order to understand how structural properties, influence the coordination capacity of health professionals in municipal healthcare, we visualised and compared each network with and without the ego (see Table A3 in the Appendix). Table 2 provides a detailed explanation of the colour coding of modules in the network visualisations. Additionally, we examined structural metrics (centralisation, clustering and modularity) to clarify the extent to which local service integration depends on individual coordinators. The metrics varied across networks, reflecting the diverse configurations present. These networks rarely featured pure topologies, instead often exhibiting hybrid structures that blended star-like hubs with distributed connectivity, such as partially meshed or modular patterns. In several cases, central actors served as hubs within otherwise meshed networks, demonstrating that coordination capacity can emerge from mixed topologies rather than purely centralised or fully distributed forms. Overall, configurations ranged from strongly centralised stars to distributed meshes and small-world networks, often combining elements of both. Table 2 Colour legend for network figures Modul Colour Modul Pink Educational Module Blue Professional Coordination and Peer Network Module Green Neighbourhood and Social Space Module Orange Health Care Service Module Red Health Promotion and Prevention Module Black Cross Sector Module Participant 8's case represents a highly centralised star network that is heavily dependent on the health professional. The network exhibits the highest modularity (0.579) and a very high clustering coefficient (0.717) in the sample, indicating robust subgroup formation and substantial local cohesion. Betweenness centrality (0.762) emphasises the crucial bridging function of the participant. As illustrated in Fig. 1, the health professional functions as a central hub, connecting otherwise weakly linked modules and enabling efficient communication through a single coordinating centre. The network exhibits strong centralisation, with the ego occupying the primary bridging position. However, without the ego, as shown in Fig. 2, the network fragments into isolated clusters with no cross-module ties. Without the ego, the modules remain internally cohesive, but they lose all horizontal connections with each other. Network integration then becomes entirely dependent on the central position of the health professional. Without the ego, the modules become disconnected clusters with no cross-module ties. Figure 1: Centralised Star Network Figure 2: Centralised Star Network without Ego Participant 11's network is small (n = 29), dense (0.246), and has moderate clustering (0.556) and modularity (0.482). As shown in Fig. 3, the network contains a mixture of chain-like connections, one local hub and multiple mesh-like substructures, indicating a disturbed/mesh network . The health professional shows a non-dominant mediating part and several alters who maintain independent cross-cluster ties, distributing coordination across the network. Without Ego, as illustrated in Fig. 4, the structure shows only minor changes, with the clusters remaining interconnected and the chain-mesh topology intact. Most coordination pathways persist, demonstrating that integration does not depend on a single actor. This network is robust and decentralised, with integration distributed across multiple actors. It maintains connectivity even after ego removal. Figure 3: Decentralised Mesh Network Figure 4: Decentralised Mesh Network without Ego Participant 5's network is an example of a small-world network with moderate modularity (0.419), the highest density in the sample (0.303) and an exceptionally high clustering coefficient (0.716). As shown in Fig. 5, including Ego in the network creates strong local clusters and short average path lengths (2.073), indicating highly efficient global communication, as well as partial star-like elements around the health professional. Without Ego, as illustrated in Fig. 6, most modules remain connected, though one cluster loses its primary link to the rest. The ego contributes to integration, though cross-cluster connections persist without it. In this type of balanced governance structure, coordination processes are facilitated by multiple actors, supporting resilient information flow and connectivity between clusters. Figure 5: Small World Network Figure 6: Small Network without Ego Discussion This study examined the structural and functional configuration of professional networks among health professionals in municipal health promotion, focusing on how network properties influence coordination capacity. The results reveal a combination of centralised and decentralised network structures, reflecting the adaptive and multifaceted nature of collaboration in municipality-based health promotion. The findings show that coordination capacity is closely linked to structural patterns, the availability of institutional support, and the degree of interdependence of individuals within local systems. Therefore, network structures actively shape how and to what extent health professionals can fulfil their coordinating activities. This reveals both practical levers and urgent areas for policy intervention. In this study population, star-like networks dominate municipal health promotion. While such centralised structures can improve responsiveness in municipalities with chronic staff shortages, limited public health budgets and high coordination demands across fragmented sectors [ 3 , 17 ], their person-centric nature creates vulnerabilities through dependence on individual motivation and relational capital [ 14 ]. These results reinforce previous studies showing that the implementation of health promotion measures often depends on individuals taking the initiative and securing political or administrative backing [ 15 , 26 ], while organisational and system-level conditions often fail to support sustained collaboration over time [ 9 ]. Under these conditions, disturbed and distributed network configurations appear to be better suited to maintaining coordination and stability. Mesh-like structures are characterised by multiple cross-cluster connections and shared brokerage, reducing reliance on individual actors and enabling coordination to persist when participants withdraw. Similarly, small-world configurations combine strong local clustering with short path lengths to enable efficient information flow while providing redundancy across the network. In contexts characterised by limited resources and high coordination demands, these configurations provide a more resilient structural basis for cross-sectoral health promotion. Across all network types, modularity was evident, reflecting functional subgroups that correspond to spatial, educational, and healthcare domains. These findings align with network governance theory, which emphasises that the structure of a network determines how it is coordinated and how authority, responsibilities and resources are distributed. Without formalised mechanisms such as shared governance or a lead organisation, coordination reverts to individuals, which undermines network stability and resilience [ 10 ]. This reliance on individuals who perform boundary-spanning activities highlights the need to embed coordination capacity structurally within municipal health governance. Reliance on discretionary relational work creates structural fragility, which is exacerbated by limited institutional and regulatory frameworks for coordinated public health action, as well as staff shortages and high staff turnover [ 27 ]. Network analyses illustrate these consequences: when key coordinators leave, networks collapse and integrated care pathways fragment. The resulting dynamics expose a central governance challenge for municipal health systems: while they are expected to ensure cross-sectoral prevention and care continuity, they often lack the formal authority and resources necessary to sustain coordination [ 3 , 4 , 26 ]. Coordination is therefore a structural rather than an individual problem. Institutionalising coordinating roles and mechanisms is essential, as sustainable care and system relief cannot be achieved through short-term, underfunded initiatives, but rather require secure, permanent, community-based governance structures. Based on these governance considerations, the structural patterns identified in this study offer valuable insights into navigating networks and supporting integrated public health practice at a municipal level. The prevalence of star-like networks suggests that health professionals should establish long-lasting coordination relationships to prevent over-centralisation. These relationships are characterised by multiple connections combining professional trust, repeated collaboration over time and shared institutional responsibilities. From a network analysis perspective, navigating complex, cross-sectoral networks require capacities such as systems thinking, relationship management, trust-building, strategic communication and the prioritisation of resources. However, these capacities are not individual traits; rather, they become effective only within supportive governance structures. Without institutional recognition, protected time and formalised coordination spaces, these activities depend heavily on personal motivation and are difficult to sustain over time. Therefore, each functional module within the network should include multiple bridging ties to diversify relational pathways, strengthen network continuity, and reduce the personal burden on municipal public health professionals. Municipalities should formally recognise network brokerage as a core area of work within these positions. This could involve clarifying job descriptions, allocating protected networking time, or providing training in relational coordination and strategic communication. Secondly, the structural visibility of the modules indicates that health professionals should view these clusters as coordination spaces with their own specific internal logic, rather than as a single, homogeneous partnership network. This makes collaboration more sustainable and better aligned with local capacities, as professionals recognise and engage with the specific dynamics of each module. As modular structures are a defining feature of local health networks, municipalities should establish governance arrangements that facilitate inter-module connection. Examples include cross-sectoral steering committees, shared data infrastructures, and regular joint planning cycles between health, education, and social departments. Thirdly, the observed tendencies towards small-world characteristics suggest that municipal health networks naturally gravitate towards configurations balancing efficiency and relational depth, despite structural diversity [ 25 ]. Health professionals can leverage trust-based clusters while investing in cross-cluster linkages by facilitating regular cross-sectoral meetings, encouraging informal exchanges or establishing working groups centred on shared concerns across modules. From a governance perspective, this combination provides the most favourable structural conditions for coordinated action without excessive formalisation. Although municipal networks often appear flexible and context-specific, their purpose is not ad hoc collaboration, but rather continuity across professional and sectoral boundaries. Together, these findings emphasise the importance of viewing coordination as an institutional capability, rather than an individual task. This requires both structural design and sustained relational investment. This study also identifies several areas for future research. The fact that network structures are strongly person-dependent highlights the need for longitudinal designs that track how networks evolve when municipal health professionals enter, leave or change functions. Such studies could clarify the durability of network configurations and identify which relational investments produce lasting structural effects. Furthermore, methodological insights gained from comparing networks with and without the ego suggest that ego-network approaches can reveal hidden vulnerabilities. Therefore, expanding ego-centric SNA in public health could strengthen assessments of coordination capacity and help identify the relational competencies required for effective practice. Finally, given the clear practical importance of brokerage, future research should examine the competencies, leadership styles and organisational conditions that enable health professionals to carry out brokerage-related coordination work effectively. Viewing brokerage as both a structural position and a set of professional practices may facilitate more targeted training and inform the design of municipal public health governance. Limitations This study has several limitations that should be considered when interpreting the findings. Firstly, the sample size was relatively small, primarily due to the time-consuming nature of data collection. Secondly, the study focused on a specific group of health professionals working in municipal settings in Germany. While this approach yielded in-depth, context-sensitive insights, the findings cannot be generalised beyond comparable settings. Thirdly, the cross-sectional design only captures network structures at a single point in time. This means that it could miss the dynamic evolution of the network due to staff turnover or changing collaborations. Furthermore, as participation in the study was voluntary, there is potential for selection bias, as more engaged or reflective professionals may have been overrepresented, influencing the observed network patterns. Additionally, several inherent methodological limitations of ego-centred network analysis must be acknowledged. This approach relies exclusively on the ego’s perspective, meaning the perceptions of other network members are not directly captured. While previous research indicates that network data generated through name generators is generally valid [ 28 ], these findings should be interpreted with caution, particularly with regard to the potential influence of recall bias or social desirability effects. Finally, although the egocentric design allows for the detailed reconstruction of individual coordination contexts, future studies could complement this approach with whole-network or multiplex analyses to capture the broader systemic dimensions of municipal health governance. Conclusion This study demonstrates that the structural properties of health professionals' networks — particularly centralisation, modularity, and 'small-world' characteristics — significantly influence their ability to coordinate and integrate public health services at a municipal level. While star-like configurations can enable rapid coordination and responsiveness in settings with limited resources, they also create structural vulnerabilities by concentrating responsibility in individual actors. By contrast, more distributed and small-world-like structures enhance resilience and continuity by embedding coordination across multiple connections and enabling adaptive, cross-sector collaboration. These configurations enable networks to bridge the health promotion and healthcare sectors, integrating diverse stakeholders into coherent local prevention pathways. The findings contribute to network governance research by empirically illustrating how coordination often defaults to individuals in the absence of institutionalised mechanisms. For municipal health systems, this highlights the importance of viewing coordination as an organisational and structural capacity rather than an ad hoc or informal task. Strengthening municipal health promotion therefore requires the institutionalisation of coordinating capacities and mechanisms, the establishment of formal, cross-sector governance arrangements, and sustained investment in relational infrastructures. Recognising network work as a continuous strategic function that demands time, reflection, and institutional support is essential for enabling effective and equitable structures. Only by embedding coordination capacity within local public health governance can municipalities transition from short-term compensatory efforts to sustainable, integrated service provision and reduced health inequalities. Declarations Ethics approval The study was reviewed and approved by the ethics committee of the University of Bremen (reference number 2023-17) in accordance with the University’s ethics guidelines, which are based on the Declaration of Helsinki, relevant disciplinary standards such as the German Research Foundation guidelines on good scientific practice, and applicable legal regulations. An informed written declaration of consent is signed by all study participants. The respondents right to refuse or withdraw from participating in the interview at any time was fully respected and the information provided by each respondent was kept confidential by making each transcript coded and not sharing personal information of any patient of the third party. Consent for publication Written informed consent for publication was obtained from all participants prior to data collection. All participants were informed about the purpose of the study, its voluntary nature, and how their anonymised data would be used for scientific publication. Data Availability The data generated and analysed during this study are subject to ethical and data protection restrictions and are therefore not publicly available. The corresponding author can provide fully anonymised adjacency matrices without any node-level attributes upon reasonable request, for scientific purposes. This is provided that data sharing is compatible with the original consent of the participants, or that additional consent has been obtained where necessary. Conflict of Interest The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article. Funding This research did not receive any specific grant funding from any public, commercial or not-for-profit sector bodies. Authors contribution Hanna Richter conceived and designed the study, conducted the data collection and formal analysis, and wrote the first draft of the manuscript. The Co-Authors Tatiana Mamontova and Lisa Kühne contributed to the critical revision of the manuscript, providing substantial intellectual feedback. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank all the health professionals who participated in this study for their valuable time and insights. We would also like to express our sincere gratitude to Prof. Dr. Eva Quante-Brandt for her thoughtful feedback and academic guidance throughout the development of this work. References Kickbusch I, Gleicher D. Governance for Health in the 21st Century. Geneva: World Health Organization; 2012. World Health Organization. Health in all policies framework (HiaP) for country action. (2014). https://www.afro.who.int/publications/health-all-policies-framework-country-action Accessed 21 Oct 2025. Böhm K. Kommunale Gesundheitsförderung und Prävention: Elemente, Potentiale und Hemmnisse einer präventiven und gesundheitsförderlichen kommunalen Gesundheitspolitik. Z für Sozialreform. 2017;63:275–99. Amri M, Chatur A, O'Campo P. An umbrella review of intersectoral and multisectoral approaches to health policy. Social Science and Medicine. 202;315:115469. Kegler MC, Rigler J, Honeycutt S. How does community context influence coalitions in the formation stage? A multiple case study based on the Community Coalition Action Theory. BMC Public Health. 2010;10:90. Wiggins B, Anastasiou K, Cox DN. A systematic review of key factors in the effectiveness of multisector alliances in the public health domain. Am J Health Promotion. 2021;35(1):93–105. Fagan AA, Hanson K, Briney JS, Hawkins JD. Sustaining the utilization and high quality implementation of tested and effective prevention programs using the communities that care prevention system. Am J Community Psychol. 2012;49:365–77. Brown EC, Hawkins JD, Rhew IC, Shapiro VB, Abbott RD, Oesterle S, et al. Prevention System Mediation of Communities That Care Effects on Youth Outcomes. Prev Sci. 2014;15:623–32. Bosdijk A, Nieboer AP, Cramm JM. The development of an integrated neighborhood approach for health promotion and prevention: a qualitative exploration of stakeholders’ views. Health Res Policy Syst. 2023;21:125. Provan KG, Kenis P. Modes of Network Governance: Structure, Management, and Effectiveness. J Public Adm Res Theor. 2008;18:229–52. Sørensen E, Torfing J. Making governance networks effective and democratic through metagovernace. Public Adm. 2009;87:234–58. Valente TW. Network interventions. Science. 2012;337:49–53. Turnbull L, Hütt MC, Ioannides AA, Kininmonth S, Poeppl R, Tockner K, et al. Connectivity and complex systems: learning from a multi-disciplinary perspective. Appl Netw Sci. 2018;3:11. Reggiani A. The Architecture of Connectivity: A Key to Network Vulnerability, Complexity and Resilience. Networks Spat Econ. 2022;22:415–37. Corbin JH, Jones J, Barry MM. What makes intersectoral partnerships for health promotion work? A review of the international literature. Health Promot Int. 2018;33:4–26. Long JC, Cunningham FC, Braithwaite J. Bridges, brokers and boundary spanners in collaborative networks: a systematic review. BMC Health Service Res. 2013;13:158. Huiberts I, Collard D, Singh A, Hendriks M, Chinapaw MJM. (2024). Uncovering the key working mechanisms of a complex community-based obesity prevention programme in the Netherlands using ripple effects mapping. Health research policy and systems. 2024;22:122. Formularbeginn Formularende. Quiling E, Kruse S, Kuchler M, Leimann J, Walter U. Models of Intersectoral Cooperation in Municipal Health Promotion and Prevention: Findings from a Scoping Review. Sustainability. 2020;12:6544. Yin R. Case Study Research Design and Methods. 5th ed. Thousand Oaks, Los Angeles: Sage; 2014. Kahn RL, Antonucci TC. Convoys of life course: Attachment, roles and social suppor. In: Baltes P, Brim O, editors. Life-span development and behavior. New York: Academic; 1980. pp. 253–86. Hollstein B, Pfeffer J. Netzwerkkarten als Instrument zur Erhebung egozentrierter Netzwerke. 2010. https://www.researchgate.net/publication/265195974_Netzwerkkarten_als_Instrument_zur_Erhebung_egozentrierter_Netzwerke . Accessed 09 Sept 2025. Jansen D. Einführung in die Netzwerkanalyse. Grundlagen, Methoden, Forschungsbeispiele. Wiesbaden: VS; 2006. Newman ME. Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America. 2006;103:8577–82. Borgatti SP, Everett MG, Jeffrey CJ. Analyzing social networks. London: Sage; 2013. Watts D, Strogatz S. Collective dynamics of ‘small-world’ networks. Nature. 1998;393:440–2. Herbert-Maul A, Abu-Omar K, Till M, Fleuren T, Wolff AR, Reimers AK. Präventionsdilemma auf kommunaler Ebene? Prävention und Gesundheitsförderung. 2023;18:327–34. Rosenbrock R, Geene R. Gesundheit fördern und schützen: Die Chance für ein modernes Public Health System in Deutschland nutzen - jetzt! Gesundheitswesen. 2023;85:490–4. Wolf C. Egozentrierte Netzwerke: Datenerhebung und Datenanalyse. In: Stegbauer C, Häußling R, editors. Handbuch Netzwerkforschung. Wiesbaden: VS; 2010. pp. 471–83. Additional Declarations No competing interests reported. Supplementary Files TableA1ModuleTypesandTheirFrequencyAcrossEgoNetworks.docx TableA2Distributionofnetworkmodulesacrossegonetworks.docx TableA3ComparativeNetworkTypologiesWithandWithoutEgoAcrossCases.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 May, 2026 Reviews received at journal 02 May, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Editor invited by journal 10 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 10 Apr, 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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Richter","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACAwbGBmYQgx+IDzA2QIV5CGpJYGCQbCBeCwMDWIvBASCLKC3m0ocbPxf+sMk3vpF78ADjjntyuu29BxjeVODWYtmX2Cw9IyHNctuNvIQDjGeKjc3OnEtgnHMGj8POMLYx8yQcNjC7kWNw+G9bQuI2IIOZt42glv8GxjNyDA4wtiXUb7v/BqjlH0EtBwwMJCBaEsxu8AC1NODV0izNk5ZsIHHmDVDLmQTDbWdyDA7OOYZPC/vDzzw2dgb87TnGHxh3JMibHT9j+OBNDW4t2MEBUjWMglEwCkbBKEAFAPWAUqUyu5kAAAAAAElFTkSuQmCC","orcid":"","institution":"University of Bremen","correspondingAuthor":true,"prefix":"","firstName":"Hanna","middleName":"","lastName":"Richter","suffix":""},{"id":623602924,"identity":"34fda507-6d08-4c94-ac13-4ec6aff55dd0","order_by":1,"name":"Tatiana Mamontova","email":"","orcid":"","institution":"University of Bremen","correspondingAuthor":false,"prefix":"","firstName":"Tatiana","middleName":"","lastName":"Mamontova","suffix":""},{"id":623602929,"identity":"404dd504-d4ee-4e32-9e50-008796fcf20c","order_by":2,"name":"Lisa Kühne","email":"","orcid":"","institution":"University of Bremen","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"","lastName":"Kühne","suffix":""}],"badges":[],"createdAt":"2026-03-24 12:54:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9212156/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9212156/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107349773,"identity":"5f56d8bb-df96-4d80-84be-10e96a14ffcd","added_by":"auto","created_at":"2026-04-20 15:53:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42215,"visible":true,"origin":"","legend":"\u003cp\u003eCentralised Star Network\u003c/p\u003e","description":"","filename":"Figure1CentralisedStarNetwork.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9212156/v1/942d1fdb0d300510f1a554ec.jpg"},{"id":107349824,"identity":"515f3f4e-0a89-4f5b-84a5-698837e3d1cb","added_by":"auto","created_at":"2026-04-20 15:53:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24016,"visible":true,"origin":"","legend":"\u003cp\u003eCentralised Star Network without Ego\u003c/p\u003e","description":"","filename":"Figure2CentralisedStarNetworkwithoutEgo.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9212156/v1/bc5b0eeda0fd65415b3a69b6.jpg"},{"id":107349775,"identity":"35e402c2-0a61-4182-924a-42db03e08479","added_by":"auto","created_at":"2026-04-20 15:53:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25196,"visible":true,"origin":"","legend":"\u003cp\u003eDecentralised Mesh Network\u003c/p\u003e","description":"","filename":"Figure3DecentralisedMeshNetwork.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9212156/v1/92eda9a069db75fd9461359c.jpg"},{"id":107349808,"identity":"cb4ef6b5-ebbd-45e6-9a51-f33152dc1299","added_by":"auto","created_at":"2026-04-20 15:53:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33029,"visible":true,"origin":"","legend":"\u003cp\u003eDecentralised Mesh Network without Ego\u003c/p\u003e","description":"","filename":"Figure4DecentralisedMeshNetworkwithoutEgo.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9212156/v1/76400c09e64b697314ff5de6.jpg"},{"id":107349770,"identity":"fba25298-f0c8-4594-b883-d552162b6538","added_by":"auto","created_at":"2026-04-20 15:53:28","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":32572,"visible":true,"origin":"","legend":"\u003cp\u003eSmall World Network\u003c/p\u003e","description":"","filename":"Figure5SmallWorldNetwork.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9212156/v1/e4b836f82096ca9723031845.jpg"},{"id":107349815,"identity":"e6464bd0-e199-4f8e-89f3-8f04e73346ff","added_by":"auto","created_at":"2026-04-20 15:53:48","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":28182,"visible":true,"origin":"","legend":"\u003cp\u003eSmall Network without Ego\u003c/p\u003e","description":"","filename":"Figure6SmallWorldNetworkwithouEgo.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9212156/v1/c755599fcba37c25ba363844.jpg"},{"id":107350494,"identity":"07bc5348-d9b2-4bf6-b2f8-5f90de4506b6","added_by":"auto","created_at":"2026-04-20 15:56:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":562099,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9212156/v1/406860bb-3481-427d-81a0-16375f4d21d0.pdf"},{"id":107350162,"identity":"e1f9f347-9989-4a80-beb2-51d223d277bd","added_by":"auto","created_at":"2026-04-20 15:55:28","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19488,"visible":true,"origin":"","legend":"","description":"","filename":"TableA1ModuleTypesandTheirFrequencyAcrossEgoNetworks.docx","url":"https://assets-eu.researchsquare.com/files/rs-9212156/v1/571cb37f02b3364506839204.docx"},{"id":107349783,"identity":"1053eca4-ebee-4b48-a393-736628ef09aa","added_by":"auto","created_at":"2026-04-20 15:53:32","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":20136,"visible":true,"origin":"","legend":"","description":"","filename":"TableA2Distributionofnetworkmodulesacrossegonetworks.docx","url":"https://assets-eu.researchsquare.com/files/rs-9212156/v1/fc5f2bb525c5b3fd2f678c9e.docx"},{"id":107349974,"identity":"4ca24fac-4482-4540-8cc5-dc40d806c3f2","added_by":"auto","created_at":"2026-04-20 15:54:47","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":17419,"visible":true,"origin":"","legend":"","description":"","filename":"TableA3ComparativeNetworkTypologiesWithandWithoutEgoAcrossCases.docx","url":"https://assets-eu.researchsquare.com/files/rs-9212156/v1/cf0464795828fadb61dac8f5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Structural characteristics of municipal health promotion networks: Insights from egocentric network analysis in municipal settings in Germany","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnder the Health in All Policies (HiAP) approach, municipalities have become pivotal in promoting health and equity by shaping supportive living conditions across sectors like health, education, youth welfare, and social services. However, administrative fragmentation, marked by the distribution of responsibilities across various governmental levels and independent authorities, hinders intersectoral coordination. Prevention responsibilities are spread out and rely on informal connections rather than formal institutional structures [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIntegrated, community-based approaches aim to address this fragmentation by aligning health and social care efforts, reducing health inequalities and strengthening local health systems [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In this context, municipal networks have emerged as key governance mechanisms, bringing together actors from health care, social services, education and civil society. These networks aim to reduce fragmentation and strengthen collective capacity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although international evidence indicates that well-functioning community health networks can support evidence-based interventions and promote health equity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], their implementation frequently remains constrained by limited organisational and policy support for sustained collaboration. As a result, service gaps persist, particularly in preventive services located at the intersections of health care, social services and education [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNetwork governance theory provides a useful conceptual perspective through which to analyse how coordination is achieved within these settings. Governance structures, such as lead-organisation models, shared governance arrangements or hybrid forms, determine the allocation of authority, responsibilities and resources within networks, while relational mechanisms such as trust, information exchange and brokerage facilitate collaboration across organisational boundaries [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recent research is increasingly highlighting that governance arrangements are structural as well as institutional phenomena, as network design influences coordination dynamics, vulnerability or resilience [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Within these structures, a coordinator\u0026rsquo;s ability to foster collaboration depends strongly on the organisational context and the soft skills they employ to bridge sectoral divides [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In other word, the way in which a network is constructed determines the actions that can be taken within it.\u003c/p\u003e \u003cp\u003eAlthough research describes health professionals as brokers or network stewards [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], little empirical work has examined how network structures shape their coordination capacity. Despite the existence of numerous theoretical models of intersectoral collaboration at the local level [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], it remains unclear how specific network configurations influence health professionals' ability to direct and integrate services. To address this gap, the present study investigates the following research question within the German municipal public health context: How do the structural properties of health professionals' networks influence their ability to direct and coordinate integrated public health services at the local level? By identifying enabling and constraining network configurations, it contributes practical insights for designing resilient, equitable governance structures that bridge sectoral divides and enhance preventive service delivery in fragmented municipal health systems.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThe aim of this study was to examine how the structural properties of health professional networks influence health professionals' coordination capacity in municipal health promotion. We employed ego-centred social network analysis (SNA) to establish a conceptual link between governance theory and the practical implementation of health professionals. By focusing on the immediate relational environment of individual actors, the analysis can reveal the structural conditions under which coordination takes place. This approach provides insight into why some professionals coordinate integrated public health services effectively while others struggle, despite having similar formal mandates.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and Recruitment\u003c/h3\u003e\n\u003cp\u003eThe data is based on 14 egocentric network maps of health professionals working in German primary schools and local communities. These health professionals have dual qualifications, consisting of a university degree in health sciences and training in a health-related profession, such as nursing. The sample size reflects the principles of purposive sampling and the goal of theoretical rather than statistical generalisation. Studies of this type, which focus on a single individual's network, rely on a small number of information-rich cases, as each ego-network provides detailed structural and contextual insights into collaboration patterns [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Participants were selected through an established collaboration specifically because their work involve building, maintaining and coordinating networks for municipal health promotion. During recruitment, efforts were made to ensure diversity in terms of professional background, years of experience and area of activity.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eThe data, conducted between July 2023 and March 2024, were collected using the software Vennmaker, a tool for visual network generation. Ethical approval was granted by the University of Bremen and all participants provided informed, written consent.\u003c/p\u003e \u003cp\u003eAs an egocentric design, each network reflects the actor\u0026rsquo;s perspective on their relevant relationships. Following the concentric circle model of Kahn and Antonucci [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], respondents were asked to identify the individuals, institutions or task groups with whom they collaborate professionally. Additional attributes (e.g. organisational affiliation, role, or sector) and relations between each actor were recorded. These questions were specifically developed for this data collection to elicit participants\u0026rsquo; egocentric network perceptions. This approach enables intra- and interpersonal comparisons to be made during the evaluation, as it allows ego perceptions and structural tendencies to be identified across networks [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Participants also differentiated between formal and informal collaborations, providing information on the function and field of activity of each alter. This allows sectoral patterns to be identified and cross-domain linkages to be established.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eNetwork data were processed and analysed using Gephi 1.0. For each ego network, key structural indicators were calculated to assess network size, cohesion, centralisation and brokerage potential. Network size was measured as the total number of alters, reflecting relational scope. Density, which is the ratio of existing to possible ties, indicates relational cohesion. The clustering coefficient captures the degree of subgroup formation, signaling local cohesion and redundancy. Betweenness centrality, which is based on the sum of the proportions of the shortest paths between all pairs of nodes passing through a node, reflects brokerage potential. Closeness centrality is the reciprocal of the sum of the shortest path distances from one node to all the others and indicates reachability and accessibility [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo assess internal differentiation, modularity analysis was conducted twice for each ego network: once including the ego and once excluding it. In the latter case, the ego and all direct ties were removed to examine the structure among alters only. The modularity resolution was set to 1.0, which is appropriate for detecting fine-grained subgroup structures in small ego-centred network [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe network visualisations were created using the ForceAtlas2 layout algorithm. This algorithm arranges nodes by simulating the physical forces of their relational proximity. This approach enabled the identification of structural patterns, such as cliques, coordination clusters and ego dependencies. These patterns were then interpreted using established typologies (e.g. star-shaped, tree-like and circular configurations) and theoretical frameworks like small-world network theory [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The analysis linked structural properties with functional outcomes in order to derive recommendations for the governance of community health professionals.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOverall Network Characteristics\u003c/h2\u003e \u003cp\u003eThe structural indicators across the 14 ego-centred networks reveal moderately cohesive, yet functionally differentiated, collaboration environments (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Network size ranged from 29 to 64 people, reflecting significant variations in coordination responsibilities. The majority of networks showed moderate average degree values, indicating that most actors maintained regular communication with a limited number of key partners. Clustering coefficients (0.283\u0026ndash;0.717) demonstrate significant variation in local cohesion, suggesting that some professionals operate within tightly knit environments, while others manage more dispersed networks. Measures of betweenness centrality reveal that some individuals occupy critical bridging positions between subgroups, highlighting the extent to which coordination capacity is anchored in their field of activity. Average path lengths were consistently short (2,073\u0026thinsp;\u0026minus;\u0026thinsp;2,923), indicating high global reachability, which is typical of small-world tendencies. The modularity values range from 0.373 to 0.579. These values suggest that municipal health promotion networks are organised around semi-autonomous functional domains, rather than being fully integrated or fully fragmented. The analysis shows that health professionals networks comprise not only bilateral connections, but also bridging ties between distinct functional sub-areas. Overall, the descriptive indicators demonstrate that the networks offer sufficient differentiation to handle various tasks and sectors while facilitating swift communication across the municipal public health environment.\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\u003eNetwork Characteristics of Ego-Centered Networks\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNetwork Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage Degree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEdge density\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage path length\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGlobal clustering coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModularity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHarmonic Closeness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBetweenness centrality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLocal clustering coefficent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNumber of triangles\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 3\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\u003e8,293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 5\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\u003e11,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgo 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCluster structures\u003c/h3\u003e\n\u003cp\u003eThe modularity analysis identified five distinct module types: The Neighbourhood and Social Space Module; the Education Module; the Professional Coordination and Peer Network Module; the Health Promotion and Health Prevention Module; and the Health Care Service Module. These modules organise health promotion coordination across ego networks. Content analysis further shows that they cluster into three recurring governance domains present in nearly all cases, suggesting that health professionals operate within structurally similar coordination environments. Although the strength of connections and the degree of integration between modules varies across ego-networks, the consistent presence of these domains emphasises the key areas of operation for cross-sectoral health promotion. Rather than being rigidly assigned to a single domain, actors may operate across multiple domains, depending on their functions, tasks and local priorities. This reflects the flexible and adaptive nature of intersectoral health promotion governance. In addition to these core modules, some ego-networks included highly specialised modules reflecting the personal interests or engagement of health professionals. Detailed module compositions, typical actors, and ego-network distributions are presented in Tables A1 and A2 (Appendix).\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAn \u003cem\u003eeducation module\u003c/em\u003e consisting of a wide range of educational institutions, including early childhood centres (Kitas), schools, and school-related professionals such as school social workers, psychologists, and regional advisory and support services. It also includes non-school educational providers, such as community education programmes and adult learning institutions. This module focuses on the intersection of educational and health-related responsibilities, bringing together the relevant processes and actors where educational and health-promoting tasks overlap.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eA \u003cem\u003eprofessional coordination and peer network module\u003c/em\u003e, including health professionals working in schools or neighbourhoods, as well as coordination institutions such as health departments, to facilitate knowledge exchange, joint planning and strategic alignment across the health, education and social sectors.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eA \u003cem\u003eneighbourhood and social space module\u003c/em\u003e, often comprising family and neighbourhood centres, youth services, welfare organisations, and local social organisations. This module focuses on prevention and health promotion activities in everyday living environments, involving various actors.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eNetwork types and control implications\u003c/h3\u003e\n\u003cp\u003eIn order to understand how structural properties, influence the coordination capacity of health professionals in municipal healthcare, we visualised and compared each network with and without the ego (see Table A3 in the Appendix). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a detailed explanation of the colour coding of modules in the network visualisations. Additionally, we examined structural metrics (centralisation, clustering and modularity) to clarify the extent to which local service integration depends on individual coordinators. The metrics varied across networks, reflecting the diverse configurations present. These networks rarely featured pure topologies, instead often exhibiting hybrid structures that blended star-like hubs with distributed connectivity, such as partially meshed or modular patterns. In several cases, central actors served as hubs within otherwise meshed networks, demonstrating that coordination capacity can emerge from mixed topologies rather than purely centralised or fully distributed forms. Overall, configurations ranged from strongly centralised stars to distributed meshes and small-world networks, often combining elements of both.\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\u003eColour legend for network figures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModul Colour\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModul\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducational Module\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional Coordination and Peer Network Module\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeighbourhood and Social Space Module\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth Care Service Module\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth Promotion and Prevention Module\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross Sector Module\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\u003eParticipant 8's case represents a highly \u003cb\u003ecentralised star network\u003c/b\u003e that is heavily dependent on the health professional. The network exhibits the highest modularity (0.579) and a very high clustering coefficient (0.717) in the sample, indicating robust subgroup formation and substantial local cohesion. Betweenness centrality (0.762) emphasises the crucial bridging function of the participant. As illustrated in Fig.\u0026nbsp;1, the health professional functions as a central hub, connecting otherwise weakly linked modules and enabling efficient communication through a single coordinating centre. The network exhibits strong centralisation, with the ego occupying the primary bridging position. However, without the ego, as shown in Fig.\u0026nbsp;2, the network fragments into isolated clusters with no cross-module ties. Without the ego, the modules remain internally cohesive, but they lose all horizontal connections with each other. Network integration then becomes entirely dependent on the central position of the health professional. Without the ego, the modules become disconnected clusters with no cross-module ties.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 1: Centralised Star Network\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 2: Centralised Star Network without Ego\u003c/em\u003e \u003c/p\u003e \u003cp\u003eParticipant 11's network is small (n\u0026thinsp;=\u0026thinsp;29), dense (0.246), and has moderate clustering (0.556) and modularity (0.482). As shown in Fig.\u0026nbsp;3, the network contains a mixture of chain-like connections, one local hub and multiple mesh-like substructures, indicating a \u003cb\u003edisturbed/mesh network\u003c/b\u003e. The health professional shows a non-dominant mediating part and several alters who maintain independent cross-cluster ties, distributing coordination across the network. Without Ego, as illustrated in Fig.\u0026nbsp;4, the structure shows only minor changes, with the clusters remaining interconnected and the chain-mesh topology intact. Most coordination pathways persist, demonstrating that integration does not depend on a single actor. This network is robust and decentralised, with integration distributed across multiple actors. It maintains connectivity even after ego removal.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 3: Decentralised Mesh Network\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 4: Decentralised Mesh Network without Ego\u003c/em\u003e \u003c/p\u003e \u003cp\u003eParticipant 5's network is an example of a \u003cb\u003esmall-world network\u003c/b\u003e with moderate modularity (0.419), the highest density in the sample (0.303) and an exceptionally high clustering coefficient (0.716). As shown in Fig.\u0026nbsp;5, including Ego in the network creates strong local clusters and short average path lengths (2.073), indicating highly efficient global communication, as well as partial star-like elements around the health professional. Without Ego, as illustrated in Fig.\u0026nbsp;6, most modules remain connected, though one cluster loses its primary link to the rest. The ego contributes to integration, though cross-cluster connections persist without it. In this type of balanced governance structure, coordination processes are facilitated by multiple actors, supporting resilient information flow and connectivity between clusters.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 5: Small World Network\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 6: Small Network without Ego\u003c/em\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the structural and functional configuration of professional networks among health professionals in municipal health promotion, focusing on how network properties influence coordination capacity. The results reveal a combination of centralised and decentralised network structures, reflecting the adaptive and multifaceted nature of collaboration in municipality-based health promotion. The findings show that coordination capacity is closely linked to structural patterns, the availability of institutional support, and the degree of interdependence of individuals within local systems. Therefore, network structures actively shape how and to what extent health professionals can fulfil their coordinating activities. This reveals both practical levers and urgent areas for policy intervention.\u003c/p\u003e \u003cp\u003eIn this study population, star-like networks dominate municipal health promotion. While such centralised structures can improve responsiveness in municipalities with chronic staff shortages, limited public health budgets and high coordination demands across fragmented sectors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], their person-centric nature creates vulnerabilities through dependence on individual motivation and relational capital [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These results reinforce previous studies showing that the implementation of health promotion measures often depends on individuals taking the initiative and securing political or administrative backing [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], while organisational and system-level conditions often fail to support sustained collaboration over time [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Under these conditions, disturbed and distributed network configurations appear to be better suited to maintaining coordination and stability. Mesh-like structures are characterised by multiple cross-cluster connections and shared brokerage, reducing reliance on individual actors and enabling coordination to persist when participants withdraw. Similarly, small-world configurations combine strong local clustering with short path lengths to enable efficient information flow while providing redundancy across the network. In contexts characterised by limited resources and high coordination demands, these configurations provide a more resilient structural basis for cross-sectoral health promotion. Across all network types, modularity was evident, reflecting functional subgroups that correspond to spatial, educational, and healthcare domains.\u003c/p\u003e \u003cp\u003eThese findings align with network governance theory, which emphasises that the structure of a network determines how it is coordinated and how authority, responsibilities and resources are distributed. Without formalised mechanisms such as shared governance or a lead organisation, coordination reverts to individuals, which undermines network stability and resilience [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This reliance on individuals who perform boundary-spanning activities highlights the need to embed coordination capacity structurally within municipal health governance. Reliance on discretionary relational work creates structural fragility, which is exacerbated by limited institutional and regulatory frameworks for coordinated public health action, as well as staff shortages and high staff turnover [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Network analyses illustrate these consequences: when key coordinators leave, networks collapse and integrated care pathways fragment. The resulting dynamics expose a central governance challenge for municipal health systems: while they are expected to ensure cross-sectoral prevention and care continuity, they often lack the formal authority and resources necessary to sustain coordination [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Coordination is therefore a structural rather than an individual problem. Institutionalising coordinating roles and mechanisms is essential, as sustainable care and system relief cannot be achieved through short-term, underfunded initiatives, but rather require secure, permanent, community-based governance structures.\u003c/p\u003e \u003cp\u003eBased on these governance considerations, the structural patterns identified in this study offer valuable insights into navigating networks and supporting integrated public health practice at a municipal level. The prevalence of star-like networks suggests that health professionals should establish long-lasting coordination relationships to prevent over-centralisation. These relationships are characterised by multiple connections combining professional trust, repeated collaboration over time and shared institutional responsibilities. From a network analysis perspective, navigating complex, cross-sectoral networks require capacities such as systems thinking, relationship management, trust-building, strategic communication and the prioritisation of resources. However, these capacities are not individual traits; rather, they become effective only within supportive governance structures. Without institutional recognition, protected time and formalised coordination spaces, these activities depend heavily on personal motivation and are difficult to sustain over time. Therefore, each functional module within the network should include multiple bridging ties to diversify relational pathways, strengthen network continuity, and reduce the personal burden on municipal public health professionals. Municipalities should formally recognise network brokerage as a core area of work within these positions. This could involve clarifying job descriptions, allocating protected networking time, or providing training in relational coordination and strategic communication. Secondly, the structural visibility of the modules indicates that health professionals should view these clusters as coordination spaces with their own specific internal logic, rather than as a single, homogeneous partnership network. This makes collaboration more sustainable and better aligned with local capacities, as professionals recognise and engage with the specific dynamics of each module. As modular structures are a defining feature of local health networks, municipalities should establish governance arrangements that facilitate inter-module connection. Examples include cross-sectoral steering committees, shared data infrastructures, and regular joint planning cycles between health, education, and social departments. Thirdly, the observed tendencies towards small-world characteristics suggest that municipal health networks naturally gravitate towards configurations balancing efficiency and relational depth, despite structural diversity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Health professionals can leverage trust-based clusters while investing in cross-cluster linkages by facilitating regular cross-sectoral meetings, encouraging informal exchanges or establishing working groups centred on shared concerns across modules. From a governance perspective, this combination provides the most favourable structural conditions for coordinated action without excessive formalisation. Although municipal networks often appear flexible and context-specific, their purpose is not ad hoc collaboration, but rather continuity across professional and sectoral boundaries. Together, these findings emphasise the importance of viewing coordination as an institutional capability, rather than an individual task. This requires both structural design and sustained relational investment.\u003c/p\u003e \u003cp\u003eThis study also identifies several areas for future research. The fact that network structures are strongly person-dependent highlights the need for longitudinal designs that track how networks evolve when municipal health professionals enter, leave or change functions. Such studies could clarify the durability of network configurations and identify which relational investments produce lasting structural effects. Furthermore, methodological insights gained from comparing networks with and without the ego suggest that ego-network approaches can reveal hidden vulnerabilities. Therefore, expanding ego-centric SNA in public health could strengthen assessments of coordination capacity and help identify the relational competencies required for effective practice. Finally, given the clear practical importance of brokerage, future research should examine the competencies, leadership styles and organisational conditions that enable health professionals to carry out brokerage-related coordination work effectively. Viewing brokerage as both a structural position and a set of professional practices may facilitate more targeted training and inform the design of municipal public health governance.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations that should be considered when interpreting the findings. Firstly, the sample size was relatively small, primarily due to the time-consuming nature of data collection. Secondly, the study focused on a specific group of health professionals working in municipal settings in Germany. While this approach yielded in-depth, context-sensitive insights, the findings cannot be generalised beyond comparable settings. Thirdly, the cross-sectional design only captures network structures at a single point in time. This means that it could miss the dynamic evolution of the network due to staff turnover or changing collaborations. Furthermore, as participation in the study was voluntary, there is potential for selection bias, as more engaged or reflective professionals may have been overrepresented, influencing the observed network patterns.\u003c/p\u003e \u003cp\u003eAdditionally, several inherent methodological limitations of ego-centred network analysis must be acknowledged. This approach relies exclusively on the ego\u0026rsquo;s perspective, meaning the perceptions of other network members are not directly captured. While previous research indicates that network data generated through name generators is generally valid [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], these findings should be interpreted with caution, particularly with regard to the potential influence of recall bias or social desirability effects. Finally, although the egocentric design allows for the detailed reconstruction of individual coordination contexts, future studies could complement this approach with whole-network or multiplex analyses to capture the broader systemic dimensions of municipal health governance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that the structural properties of health professionals' networks \u0026mdash; particularly centralisation, modularity, and 'small-world' characteristics \u0026mdash; significantly influence their ability to coordinate and integrate public health services at a municipal level. While star-like configurations can enable rapid coordination and responsiveness in settings with limited resources, they also create structural vulnerabilities by concentrating responsibility in individual actors. By contrast, more distributed and small-world-like structures enhance resilience and continuity by embedding coordination across multiple connections and enabling adaptive, cross-sector collaboration. These configurations enable networks to bridge the health promotion and healthcare sectors, integrating diverse stakeholders into coherent local prevention pathways.\u003c/p\u003e \u003cp\u003eThe findings contribute to network governance research by empirically illustrating how coordination often defaults to individuals in the absence of institutionalised mechanisms. For municipal health systems, this highlights the importance of viewing coordination as an organisational and structural capacity rather than an ad hoc or informal task. Strengthening municipal health promotion therefore requires the institutionalisation of coordinating capacities and mechanisms, the establishment of formal, cross-sector governance arrangements, and sustained investment in relational infrastructures. Recognising network work as a continuous strategic function that demands time, reflection, and institutional support is essential for enabling effective and equitable structures. Only by embedding coordination capacity within local public health governance can municipalities transition from short-term compensatory efforts to sustainable, integrated service provision and reduced health inequalities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eThe study was reviewed and approved by the ethics committee of the University of Bremen (reference number 2023-17) in accordance with the University\u0026rsquo;s ethics guidelines, which are based on the Declaration of Helsinki, relevant disciplinary standards such as the German Research Foundation guidelines on good scientific practice, and applicable legal regulations. An informed written declaration of consent is signed by all study participants. The respondents right to refuse or withdraw from participating in the interview at any time was fully respected and the information provided by each respondent was kept confidential by making each transcript coded and not sharing personal information of any patient of the third party.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWritten informed consent for publication was obtained from all participants prior to data collection. All participants were informed about the purpose of the study, its voluntary nature, and how their anonymised data would be used for scientific publication.\u003c/p\u003e\n\u003ch2\u003eData Availability\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe data generated and analysed during this study are subject to ethical and data protection restrictions and are therefore not publicly available. The corresponding author can provide fully anonymised adjacency matrices without any node-level attributes upon reasonable request, for scientific purposes. This is provided that data sharing is compatible with the original consent of the participants, or that additional consent has been obtained where necessary.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant funding from any public, commercial or not-for-profit sector bodies.\u003c/p\u003e\n\u003ch2\u003eAuthors contribution\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eHanna Richter conceived and designed the study, conducted the data collection and formal analysis, and wrote the first draft of the manuscript. The Co-Authors Tatiana Mamontova and Lisa K\u0026uuml;hne contributed to the critical revision of the manuscript, providing substantial intellectual feedback. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank all the health professionals who participated in this study for their valuable time and insights. We would also like to express our sincere gratitude to Prof. Dr. Eva Quante-Brandt for her thoughtful feedback and academic guidance throughout the development of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKickbusch I, Gleicher D. Governance for Health in the 21st Century. Geneva: World Health Organization; 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Health in all policies framework (HiaP) for country action. (2014). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.afro.who.int/publications/health-all-policies-framework-country-action\u003c/span\u003e\u003cspan address=\"https://www.afro.who.int/publications/health-all-policies-framework-country-action\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 21 Oct 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026ouml;hm K. Kommunale Gesundheitsf\u0026ouml;rderung und Pr\u0026auml;vention: Elemente, Potentiale und Hemmnisse einer pr\u0026auml;ventiven und gesundheitsf\u0026ouml;rderlichen kommunalen Gesundheitspolitik. Z f\u0026uuml;r Sozialreform. 2017;63:275\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmri M, Chatur A, O'Campo P. An umbrella review of intersectoral and multisectoral approaches to health policy. Social Science and Medicine. 202;315:115469.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKegler MC, Rigler J, Honeycutt S. How does community context influence coalitions in the formation stage? A multiple case study based on the Community Coalition Action Theory. BMC Public Health. 2010;10:90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiggins B, Anastasiou K, Cox DN. A systematic review of key factors in the effectiveness of multisector alliances in the public health domain. Am J Health Promotion. 2021;35(1):93\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFagan AA, Hanson K, Briney JS, Hawkins JD. Sustaining the utilization and high quality implementation of tested and effective prevention programs using the communities that care prevention system. Am J Community Psychol. 2012;49:365\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown EC, Hawkins JD, Rhew IC, Shapiro VB, Abbott RD, Oesterle S, et al. Prevention System Mediation of Communities That Care Effects on Youth Outcomes. Prev Sci. 2014;15:623\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBosdijk A, Nieboer AP, Cramm JM. The development of an integrated neighborhood approach for health promotion and prevention: a qualitative exploration of stakeholders\u0026rsquo; views. Health Res Policy Syst. 2023;21:125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProvan KG, Kenis P. Modes of Network Governance: Structure, Management, and Effectiveness. J Public Adm Res Theor. 2008;18:229\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;rensen E, Torfing J. Making governance networks effective and democratic through metagovernace. Public Adm. 2009;87:234\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValente TW. Network interventions. Science. 2012;337:49\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurnbull L, H\u0026uuml;tt MC, Ioannides AA, Kininmonth S, Poeppl R, Tockner K, et al. Connectivity and complex systems: learning from a multi-disciplinary perspective. Appl Netw Sci. 2018;3:11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReggiani A. The Architecture of Connectivity: A Key to Network Vulnerability, Complexity and Resilience. Networks Spat Econ. 2022;22:415\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorbin JH, Jones J, Barry MM. What makes intersectoral partnerships for health promotion work? A review of the international literature. Health Promot Int. 2018;33:4\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong JC, Cunningham FC, Braithwaite J. Bridges, brokers and boundary spanners in collaborative networks: a systematic review. BMC Health Service Res. 2013;13:158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuiberts I, Collard D, Singh A, Hendriks M, Chinapaw MJM. (2024). Uncovering the key working mechanisms of a complex community-based obesity prevention programme in the Netherlands using ripple effects mapping. Health research policy and systems. 2024;22:122. Formularbeginn Formularende.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuiling E, Kruse S, Kuchler M, Leimann J, Walter U. Models of Intersectoral Cooperation in Municipal Health Promotion and Prevention: Findings from a Scoping Review. Sustainability. 2020;12:6544.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin R. Case Study Research Design and Methods. 5th ed. Thousand Oaks, Los Angeles: Sage; 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKahn RL, Antonucci TC. Convoys of life course: Attachment, roles and social suppor. In: Baltes P, Brim O, editors. Life-span development and behavior. New York: Academic; 1980. pp. 253\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHollstein B, Pfeffer J. Netzwerkkarten als Instrument zur Erhebung egozentrierter Netzwerke. 2010. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/265195974_Netzwerkkarten_als_Instrument_zur_Erhebung_egozentrierter_Netzwerke\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/265195974_Netzwerkkarten_als_Instrument_zur_Erhebung_egozentrierter_Netzwerke\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 09 Sept 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJansen D. Einf\u0026uuml;hrung in die Netzwerkanalyse. Grundlagen, Methoden, Forschungsbeispiele. Wiesbaden: VS; 2006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman ME. Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America. 2006;103:8577\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorgatti SP, Everett MG, Jeffrey CJ. Analyzing social networks. London: Sage; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatts D, Strogatz S. Collective dynamics of \u0026lsquo;small-world\u0026rsquo; networks. Nature. 1998;393:440\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerbert-Maul A, Abu-Omar K, Till M, Fleuren T, Wolff AR, Reimers AK. Pr\u0026auml;ventionsdilemma auf kommunaler Ebene? Pr\u0026auml;vention und Gesundheitsf\u0026ouml;rderung. 2023;18:327\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenbrock R, Geene R. Gesundheit f\u0026ouml;rdern und sch\u0026uuml;tzen: Die Chance f\u0026uuml;r ein modernes Public Health System in Deutschland nutzen - jetzt! Gesundheitswesen. 2023;85:490\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolf C. Egozentrierte Netzwerke: Datenerhebung und Datenanalyse. In: Stegbauer C, H\u0026auml;u\u0026szlig;ling R, editors. Handbuch Netzwerkforschung. Wiesbaden: VS; 2010. pp. 471\u0026ndash;83.\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":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Network governance, intersectoral collaboration integrated prevention, local health systems, health equity","lastPublishedDoi":"10.21203/rs.3.rs-9212156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9212156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHealth professionals increasingly coordinate integrated public health services across complex municipal structures. While described as brokers or network stewards, little is known about how health professionals network properties shape their coordination capacity. This study examines how structural network properties influence health professionals' ability to direct and coordinate integrated public health services at the municipal level.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eEgocentric social network analysis mapped networks of 14 health professionals working in German primary schools and neighbourhood settings. Using name-generator surveys, we captured alters, inter-alter ties, and attributes across sectors. Networks were analysed in \u003cem\u003eGephi 1.0\u003c/em\u003e, calculating size, density, clustering coefficient, betweenness/closeness centrality, modularity (with/without ego), and path length. ForceAtlas2 visualisations identified structural patterns against coordination outcomes\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe networks exhibited hybrid structures, combining centralised hubs, cohesive modular subgroups and small-world properties. The high level of modularity reflected the functional segmentation of areas such as education, social spaces, and professional peer domains. Although star-shaped networks enabled rapid coordination, they were highly vulnerable due to their dependence on the health professional in the central position. By contrast, mesh-like and small-world structures maintained connectivity and brokerage potential through distributed ties, demonstrating structural resilience.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe network structure is the fundamental determinant of coordination capacity in municipal health promotion. Although centralised forms enable a rapid response, their fragility highlights the limitations of individual coordination efforts. Distributed small-world configurations are better able to sustain cross-sectoral collaboration. In order to fulfil the Health in All Policies mandate of municipalities, it is essential to implement formal governance mechanisms, including institutionalised coordination functions, protected networking time, cross-sectoral steering committees and training in network-spanning activities. This shifts coordination from discretionary individual effort to structural organisational capacity, ensuring the sustainable integration of preventive services across fragmented local health systems.\u003c/p\u003e","manuscriptTitle":"Structural characteristics of municipal health promotion networks: Insights from egocentric network analysis in municipal settings in Germany","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 15:53:04","doi":"10.21203/rs.3.rs-9212156/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"202603102775151129478439467608133970592","date":"2026-05-04T20:34:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T07:09:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T18:14:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116824983463476644020425088556060708575","date":"2026-04-27T17:22:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36588723091731237458005200156412877838","date":"2026-04-20T11:09:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T08:22:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T08:17:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-10T18:06:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-10T17:42:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-04-10T15:50:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8b08653a-0bea-4175-abf3-8265b9bf9892","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"202603102775151129478439467608133970592","date":"2026-05-04T20:34:55+00:00","index":73,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T07:09:59+00:00","index":71,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T15:53:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 15:53:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9212156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9212156","identity":"rs-9212156","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}