Exploring the Impact of Formal and Informal Network Consistency on Job Performance in Hypertension Management Teams: A Cross-Sectional Study

Exploring the Impact of Formal and Informal Network Consistency on Job Performance in Hypertension Management Teams: A Cross-Sectional Study | 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 Exploring the Impact of Formal and Informal Network Consistency on Job Performance in Hypertension Management Teams: A Cross-Sectional Study Gege Jia, Yanyun Xu, Luxia Yu, Ziyue Huang, Yumei Fang, Xingyu Zhu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8190499/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Integrated care is essential for managing noncommunicable diseases, with cross-organizational and multidisciplinary collaboration playing a key role. However, while structural and functional integration in healthcare have been widely studied, the impact of process-level and interpersonal integration remains underexplored. This study applies social network analysis to examine how the overlap between formal and informal networks influences the job performance of hypertension management personnel. Using China’s primary care–based hypertension management model as a case, we explore how network congruence shapes team effectiveness in a multidisciplinary, cross-organizational setting. Methods We conducted a cross-sectional survey of hypertension management personnel from nine community health service centers in Hangzhou, China, between September 25 and October 25, 2023. A total of 436 questionnaires were distributed, and 401 valid responses were obtained. The survey included validated instruments to measure formal networks and informal networks. Work performance was assessed across four dimensions: task, relational, learning, and innovation. Network consistency was calculated based on the overlap between formal and informal networks. Data were analyzed using correlation analysis and multilevel linear regression analysis, controlling for network size and demographics (gender, age, work experience, education). Results Correlation analysis revealed significant relationships between formal and informal networks, with network consistency influencing overall performance. Specifically, the consistency between the reciprocal workflow network and the advice network positively influences hypertension management personnel’s overall work performance, learning performance, and innovation performance. In contrast, the consistency between the reciprocal workflow network and the friendship network negatively influences overall work performance and innovation performance among hypertension management personnel. Finally, the consistency between the sequential workflow network and the advice network negatively influences hypertension management personnel’s overall work performance. Conclusions Our findings elucidate how interactions between formal and informal networks affect job performance, offer a new perspective for improving the performance of hypertension management team members, and enriche and expands the substantive scope of research on human resources for health management. Hypertension management Social networks Network consistency Job performance Figures Figure 1 Background Integrated care is a core component of healthcare reform designed to address contemporary challenges in medicine and, ultimately, improve population health[ 1 ]. Many countries have adopted diverse measures to foster coordination and integration among healthcare organizations, including structural, functional, and clinical integration. The integration process involves establishing mechanisms for the division of labor and collaboration to ensure that patient services are coordinated across disciplines, institutions, and support systems. Cross-organizational and multidisciplinary collaboration is a central element of integrated care in the management of noncommunicable diseases (NCDs) [ 2 , 3 ]. Evidence from global NCD management models, such as the U.S. The Healthy Communities Initiative [ 4 ], Finland’s North Karelia Project[ 5 ], and Singapore’s Primary Care Networks [ 6 ] indicate that effective collaboration among primary care teams is critical for the prevention and control of NCDs. While structural and functional integration have been widely studied in the healthcare context, process-level and interpersonal integration remain underexplored and warrant further investigation [ 7 ]. To address this research gap, we adopt social network analysis (SNA)—a theoretical and methodological approach that has matured since the 1990s [ 8 ]. By illuminating the structural and dynamic properties of social ties, SNA offers tools to examine complex social phenomena beyond individual attributes [ 9 ] and enables a more comprehensive understanding of interpersonal connections and their functions in society [ 10 ]. Within organizations, formal networks (the institutional “skeleton”) and informal networks (the affective “nervous system”) jointly shape behavior [ 11 , 12 ]. Formal networks can be further divided into reciprocal workflow network (RWFN) and sequential workflow network (SWFN) [ 13 ]. RWFN promotes knowledge recombination and innovation, whereas SWFN standardizes processes and supports efficient task completion [ 14 , 15 ]. Informal networks commonly include advice network (AN) and friendship network (FN). FN arises when employees maintain close interpersonal ties based on shared interests, mutual appreciation, trust, or liking, whereas AN is formed through spontaneous acts of seeking and providing counsel [ 16 , 17 ]. Critically, the overlap between formal and informal networks—“network consistency” (Soda and Zaheer 2012)—can exert negative, positive, or nonlinear effects [ 12 , 18 , 19 ]. Yet most studies still dichotomize networks (formal vs. informal) without fine-grained distinctions [ 20 ], focus on single-firm settings with limited cross-organizational interaction and homogeneous professions [ 21 ], or examine only a subset of network types [ 22 , 23 ]. Moreover, the determinants of performance in cross-organizational and multidisciplinary work teams remain largely unexplored. Although Xia examined the effects of complex cross-organizational and multidisciplinary social networks on team members’ job performance [ 24 ], the study primarily focused on the centrality and structural features of individual networks (such as advice, information, friendship, and trust) rather than on the interactions among multiple networks. Accordingly, there is an urgent need to investigate how the overlap between formal and informal networks influences team performance within cross-organizational and multidisciplinary teams. Hypertension is a leading NCD worldwide, with its prevalence increasing rapidly in low- and middle-income countries [ 25 ]. However, few studies have examined the communication and collaboration networks among hypertension management team members or the effectiveness of these networks. Such teams typically consist of clinicians, nurses, and public health professionals working in community health service centers, township health centers, community health service stations, and village clinics. Since 2009, China has implemented a primary care–based hypertension management model that emphasizes strong team collaboration. This model not only provides a unique empirical setting for investigating how the overlap between formal and informal networks influences work performance but also serves as a prototypical case for applying SNA to explore the mechanisms underlying collaboration in healthcare. Grounded in social network theory, this study developed a conceptual framework that identifies the congruence between formal and informal networks among urban community hypertension managers as the independent variable and work performance as the dependent variable. Using correlation analysis and hierarchical regression, we empirically examined the mechanisms through which network congruence influences performance. This study broadens the application scope of social network theory and extends its relevance to the healthcare sector, particularly in research on human resources for health management. It also offers SNA-informed insights and actionable strategies for improving the performance of the hypertension management team. Theory and Hypotheses Theoretical perspectives from social network research contend that individual and collective behaviors are embedded within complex, multidimensional networks of social relations in which actors are mutually interdependent and subject to reciprocal constraints and influences. This perspective provides a theoretical foundation for analyzing the relational networks within community-based hypertension management teams. In particular, embeddedness theory posits that actors’ behaviors are conditioned by the specific interpersonal ties they maintain and their positional attributes within the overall network structure [ 26 ]. A moderate level of network embeddedness facilitates resource exchange and enhances collaborative efficiency, whereas excessive cohesion may lead to information homogenization and hinder innovation [ 27 ]. Formal and informal relationships inevitably coexist within any subset of actors in a network or among the alters connected to a focal actor; consequently, their interplay generates distinct patterns of overlap between these two types of networks [ 28 ]. Soda and Zaheer define network congruence as the degree to which employees’ formal and informal networks within an organization are interrelated and exhibit cross-network overlap, a formulation that has become a foundational reference for further research. In their study, Soda and Zaheer applied the convergent correlations algorithm of the structural equivalence procedure to measure consistency across the networks[ 13 ]. Currently, there is no universally accepted measure of “network congruence.” Most studies operationalized it by calculating the correlation coefficients between the corresponding matrix elements of the two networks. For instance, LV used the quadratic assignment procedure (QAP) method to estimate congruence between formal and informal networks at the departmental level[ 29 ]. Moreover, comparison, correlation, and Euclidean distance methods were employed to compare the structural equivalence of nodes. Drawing on, we measured network consistency by computing the correlation coefficients that reflect the degree of overlap between formal (reciprocal and sequential workflows) and informal (friendship and advice) networks. Based on this classification, we identified four distinct types of network consistency among hypertension management personnel: (1) RWFN–FN consistency (between the reciprocal workflow network and the friendship network); (2) RWFN–AN consistency (between the reciprocal workflow network and the advice network); (3) SWFN–FN consistency (between the sequential workflow network and the friendship network); and (4) SWFN–AN consistency (between the sequential workflow network and the advice network). Because follow-up visits, adjustments to individualized intervention plans, and the management of acute events entail substantial uncertainty, they are best supported by dynamic and RFWN. Sustained communication and collaboration enable team members to flexibly respond to the complexities that arise during follow-up care. Such an organizational arrangement not only strengthens professional ties but also fosters friendship and trust; over time, the FN tends to expand and consolidate [ 30 ]. However, the presence of structural holes can reduce work performance, as actors occupying brokerage positions may block or distort information flows to maximize personal benefits [ 31 , 32 ]. When RWFN–FN consistency is high, dense friendship ties among colleagues involved in task interactions increase both the frequency and quality of exchanges, lower communication costs, and alleviate the obstructive impact of structural holes [ 13 ]. Such overlap enhances the execution of core activities, such as documenting follow-up visits and collecting data, thereby improving task performance. Moreover, friendship-based working relationships help cultivate a positive team climate and promote cross-departmental collaboration, ultimately strengthening relationship performance. Frequent social interactions create opportunities for experience sharing, and the cross-fertilization of ideas stimulates innovative thinking, thereby enhancing learning and innovation performance. Accordingly, we propose the following hypotheses: H1: RWFN–FN consistency positively influences the overall work performance of hypertension management personnel. H1a: RWFN–FN consistency positively influences the task performance of hypertension management personnel. H1b: RWFN–FN consistency positively influences the relational performance of hypertension management personnel. H1c: RWFN–FN consistency positively influences the learning performance of hypertension management personnel. H1d: RWFN–FN consistency positively influences the innovation performance of hypertension management personnel. High RWFN–AN consistency indicates that hypertension managers engage in extensive mutual consultation with colleagues while performing hypertension management tasks. In day-to-day work, such as diagnosing and treating primary hypertension and conducting long-term follow-ups, orderly interactions with colleagues deepen their familiarity with existing medical knowledge and promote its diffusion among the staff. Moreover, because policies governing essential public health services are frequently revised and standards are continuously elevated, advice ties are vital for disseminating and interpreting policy information[ 33 ]. Advice-seeking exchanges of opinions within the reciprocal workflow allow employees to perform hypertension management tasks more efficiently and creatively, thereby strengthening collaboration and innovation and enhancing overall work performance [ 14 ]. High RWFN–AN consistency allows the professional knowledge embedded in the workflow to be effectively mobilized and integrated within the resource base and trust structure of the advice network. This integration promotes resource sharing within the team [ 34 ] and ultimately improves both team and individual performance, particularly in terms of learning and innovation performance. Accordingly,we propose the following hypotheses: H2: RWFN–AN consistency positively influences the overall work performance of hypertension management personnel. H2a: RWFN–AN consistency positively influences the task performance of hypertension management personnel. H2b: RWFN–AN consistency positively influences the relational performance of hypertension management personnel. H2c: RWFN–AN consistency positively influences the learning performance of hypertension management personnel. H2d: RWFN–AN consistency positively influences the innovation performance of hypertension management personnel. The formal SWFN generally exhibit a clear hierarchical structure, with the primary objective of optimizing the efficiency of flows within the network. In outpatient services at community health service centers, for instance, such structures impose stringent requirements on both the quantity and quality of service delivery. When SWFN–FN consistency is high, extensive friendship ties among members along the service chain help reduce individual role conflict [ 35 ], create additional channels for information exchange, and facilitate smooth day-to-day handoffs, thereby enhancing the task performance of employees[ 36 ]. Formal working relationships can foster the development of a FN, further enhancing relational performance. The two mutually reinforce each other in a virtuous cycle that ultimately improves both individual and team performance. Conversely, when the overlap between SWFN and an informal FN is low, strong ties formed through friendship cannot be effectively leveraged within formal work processes. This misoverlap may disrupt one-way handoffs, impede information transmission, and slow operational progress, leading to adverse effects on overall team effectiveness. Accordingly, we propose the following hypotheses: H3: SWFN–FN consistency positively influences the overall work performance of hypertension management personnel. H3a: SWFN–FN consistency positively influences the task performance of hypertension management personnel. H3b: SWFN–FN consistency positively influences the relational performance of hypertension management personnel. H3c: SWFN–FN consistency positively influences the learning performance of hypertension management personnel. H3d: SWFN–FN consistency positively influences the innovation performance of hypertension management personnel. When SWFN–AN consistency is high, a hypertension manager occupies a prominent advisory position along the task chain, frequently being consulted by upstream colleagues while also seeking input from those downstream. The core principle of sequential workflow is strict adherence to clear rules and protocols that ensure orderly operations. Team members are required to follow clinical operating standards and complete their assigned tasks in accordance with the specifications of the National Essential Public Health Services Program. In this context, excessive communication, particularly consultations that merely replicate information already embedded in the workflow, can hinder efficiency and effectiveness. Such exchanges may create knowledge redundancy by repeatedly transmitting identical information, thereby increasing communication costs. Furthermore, overcommunication can lead to knowledge conflicts when members hold different interpretations of the same procedures, ultimately disrupting smooth execution [ 37 ]. Accordingly, we propose the following hypotheses: H4: SWFN–AN consistency negatively affects the overall work performance of hypertension management personnel. H4a: SWFN–AN consistency negatively affects the task performance of hypertension management personnel. H4b: SWFN–AN consistency negatively affects the relational performance of hypertension management personnel. H4c: SWFN–AN consistency negatively affects the learning performance of hypertension management personnel. H4d: SWFN–AN consistency negatively affects the innovation performance of hypertension management personnel. In summary, Fig. 1 illustrates the logical framework of this study. Methods To test our hypotheses, we conducted a questionnaire survey of hypertension management personnel at nine community health service centers in Hangzhou, China. The survey items were primarily adapted from established scales developed in widely cited, authoritative studies. These instruments have been validated in diverse contexts and have consistently demonstrated high reliability and validity. In the preparatory phase, we first contacted the directors of the participating centers to obtain staff rosters. Following standardized egocentric network survey protocols, we cleaned, coded, and compiled the rosters into a list of respondent identification numbers to facilitate subsequent network data collection. Survey Questionnaire Given that this study targets a specific population—urban community hypertension management personnel—we first adapted the relevant measurement instruments to align with the study context and respondent characteristics and developed a draft questionnaire. To further ensure scientific rigor and contextual fit, we convened an expert panel of nine members, including hospital management scholars, managers from community health service centers, and representatives from frontline primary care practitioners. The panel conducted an in-depth review to assess whether the dimensional structure of each construct was appropriate, whether the item selection was representative, and whether any items were redundant or ambiguously worded. Based on the feedback of the panel, we carefully reviewed and revised the draft, removing or refining inappropriate items, and ultimately finalizing a questionnaire that was well-suited to the objectives of this study. The questionnaire comprised three sections: (i) Background information on hypertension management personnel, (ii) a work performance scale, and (iii) a social network scale (Appendix A1). Except for the background items and social network roster data, all items were rated on a five-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5). Each item score represents the degree of agreement or endorsement by the respondents. For each construct, we computed the arithmetic mean of its constituent items to obtain a composite score, with higher values reflecting higher levels on the corresponding dimension (such as work performance). In the social network section, participants were asked to list the identification codes of personnel corresponding to each prompt using the provided roster of staff codes. Sample data Data for the sample were collected in Hangzhou, China, from September 25 to October 25, 2023. After selecting the study city, we randomly selected three counties and districts from it. Within each, we chose three community health service centers and their affiliated stations as field sites and surveyed the members of the hypertension management teams. In total, 436 questionnaires were distributed, and after excluding invalid responses, 401 valid questionnaires were obtained, yielding a valid response rate of 92.0%. The sample comprised 169 clinicians (42.1%), 185 nursing staff (46.2%), 23 ancillary staff (5.7%), 11 public health physicians (2.8%), and 13 other personnel (3.2%). The descriptive statistics and composition of urban community hypertension management personnel are presented in Appendix A2. Variable Measurement Independent variable: Formal-informal network consistency The formal network was divided into two components: the SWFN and the formal RWFN, measured using items adapted from [ 13 ]. The informal network was similarly divided into an AN and a FN; the AN was measured using items from Luo [ 38 ], while the FN was measured using items from Krackhardt and Brass [ 39 ]. To construct the formal (RWFN and SWFN) and informal (AN and FN) networks for each respondent, a social network nomination approach was employed. Network consistency was operationalized as the degree of overlap between formal and informal networks and was calculated using similarity-based measures. Details of the measurement and computation procedures are provided in the Appendix. Dependent variable: Work performance Work performance was measured using the well-established scale developed by Han Yi[ 40 ]. The instrument captures four dimensions—relationship, task, learning, and innovation performance—with item wording adapted to fit the context of the urban community hypertension management teams. The final scale comprised 20 items: Four each for task performance, 4 for learning performance, and 6 each for relationship performance and innovation performance (Appendix). Previous research on performance assessment methods has primarily relied on both objective and subjective evaluations. Although objective evaluation provides more precise estimates, it depends heavily on a rigorous and consistent assessment system supported by high-quality data. Conversely, subjective evaluation is somewhat less precise; however, studies have demonstrated that its correlation with actual job performance is stronger than expected [ 41 ]. Moreover, because subjective evaluation applies uniform scoring criteria and is relatively easy to administer, it has become the predominant approach in academic research, particularly when examining the interrelationships between variables[ 42 ]. Following this convention, the present study adopted a subjective evaluation. Control variables Prior research indicates that an actor’s position within formal and informal networks can influence work performance. Accordingly, we included the effective size of formal networks (RWFN and SWFN) and informal networks (FN and AN) as control variables to account for nonredundant ties in the network. We also controlled for demographic covariates, including gender, age, work experience, and educational attainment. Results Reliability and validity First, Cronbach’s alpha coefficients for all variables exceeded 0.80, surpassing the commonly accepted threshold of 0.70, thereby indicating strong internal consistency and reliability. The composite reliability (CR) for the task performance, relationship performance, learning performance, and innovation performance dimensions was above 0.800 in all cases, satisfying the conventional requirement that the CR value should exceed 0.600. The survey instrument was adapted from a set of well-established, validated scales and further refined through an extensive literature review and structured interviews with domestic experts and relevant administrators, ensuring strong content validity. We assessed the measurement properties of the scale using the SPSS (version 26.0). The Kaiser–Meyer–Olkin measure of sampling adequacy was 0.930, and Bartlett’s test of sphericity yielded χ² = 5471.963 ( p < 0.0001), confirming that the data were suitable for factor analysis. Details are further provided in the Appendix. To further assess the validity of each dimension, we employed a structural equation model to conduct a confirmatory factor analysis (CFA) to evaluate both convergent and discriminant validity. Without pre-specifying the number of factors, we performed principal component analysis with orthogonal varimax rotation and retained factors with eigenvalues greater than 1. Four factors were extracted from the 20 structured items, which accounted for a cumulative variance of 71.114%. We then conducted a CFA on the work performance dimensions of urban community hypertension management personnel using Analyze of Moment Structures software (version 24.0). The results indicated that χ²/df = 3.617, the Standardized Root Mean Square Residual (SRMR) was 0.076, the Root Mean Square Error of Approximation (RMSEA) was 0.081, the Goodness-of-Fit Index (GFI) was 0.854, the Adjusted Goodness-of-Fit Index (AGFI) was 0.813, the Incremental Fit Index (IFI) was 0.921, the Comparative Fit Index (CFI) was 0.920, and the Tucker–Lewis Index (TLI) was 0.908. These indices met the conventional model fit criteria, demonstrating good structural validity of the scale. All item factor loadings exceeded 0.60, and the Average Variance Extracted (AVE) for each dimension was above 0.50, supporting convergent validity. Moreover, for each pair of constructs, the inter-construct correlation was lower than the square root of that construct’s AVE, confirming satisfactory discriminant validity (Appendix B). Descriptive statistics and correlations QAP is a method used to compare the similarity of elements between two matrices and is commonly applied to analyze the relationships between networks. In this study, a QAP correlation analysis was employed to examine the correlations among the RWFN, SWFN, FN, and AN. The results revealed that the correlations among all networks were statistically significant ( p < 0.01), with correlation coefficients exceeding 0.500, indicating strong interrelationships between the networks (Table 1 ). Table 1 Correlations among the dimensions of the social network Reciprocal workflow network Sequential workflow network Advice Network Friendship Network Reciprocal workflow network 1 Sequential workflow network 0.701** 1 Advice Network 0.652** 0.749** 1 Friendship Network 0.637** 0.704** 0.814** 1 Note. ** p < 0.01. The network data were processed using UCINET VI to measure consistency between formal and informal networks. Correlation coefficients reflecting the degree of overlap between RWFN and SWFN with FN and AN were calculated based on the similarity of the actors’ network structures. The correlation coefficients ranged from − 1.00 (indicating completely opposite connections between the two networks) to 0 (indicating no correlation), and + 1.00 (indicating identical connections between the two networks). Four network consistency indicators were derived from this analysis.Table 2 presents the descriptive statistics and correlations between variables. The average scores and variances were computed based on the items within each dimension. Overall, the respondents exhibited varying degrees of overlap between their formal and informal networks, with the consistency between the SWFN and the AN being the highest, indicating the greatest overlap in connections. Pearson’s rank correlation coefficient was applied to examine the relationships between network consistency and the work performance of hypertension management personnel, as well as the correlations among other related factors. Table 2 Descriptive statistics and correlations Mean SD 1 2 3 4 5 6 7 8 9 1. Consistency between the reciprocal workflow network and the friendship network 0.26 0.36 1 2. Consistency between the reciprocal workflow network and the advice network 0.30 0.35 0.808** 1 3. Consistency between the sequential workflow networks and the friendship network 0.38 0.70 0.407** 0.352** 1 4. Consistency between the sequential workflow networks and the advice network 0.44 0.68 0.313** 0.396** 0.840** 1 5. Task performance 4.27 0.63 -0.033 0.007 -0.111* -0.097 1 6. Relationship performance 4.17 0.58 0.005 0.035 -0.031 -0.062 0.553** 1 7. Learning performance 4.16 0.60 -0.006 0.044 -0.075 -0.09 0.557** 0.712** 1 8. Innovation performance 3.61 0.73 -0.068 -0.008 -0.046 -0.061 0.270** 0.492** 0.439** 1 9. Work performance 80.16 10.09 -0.037 0.021 -0.076 -0.094 0.690** 0.867** 0.813** 0.774** 1 Note. SD, Standard deviation; *** p < 0.001; ** p < 0.01; * p < 0.05. Hypothesis testing results Independent-samples t-tests and one-way analysis of variance (ANOVA) were conducted to examine the differences in task performance, relationship performance, learning performance, innovation performance, and overall work performance scores of urban community hypertension management personnel according to their demographic characteristics. The results of the one-way ANOVA indicated significant differences in demographic variables such as gender, years of work experience, and job type among the hypertension management personnel (Appendix C1). Correlation analysis further revealed meaningful associations between network consistency and various dimensions of work performance. Based on these findings, we performed five multilevel linear regression analyses. The overall work performance score and the scores for each performance dimension were used as dependent variables, while demographic characteristics that exhibited statistically significant differences in the one-way ANOVA and the effective scale of structural holes were included as Level 1 control variables. Network consistency variables were incorporated as Level 2 predictors (Appendix C2). Before running the models, dummy variable coding was applied to the unordered categorical variables that demonstrated statistical significance in the one-way ANOVA, with the coding results presented in Appendix C3. Table 3 presents the results of the multilevel linear regression analysis, in which task, relationship, learning, innovation, and overall work performance scores of urban community hypertension management personnel were treated as dependent variables. The results demonstrated that the variance inflation factors for all variables were below 10, indicating the absence of significant multicollinearity.When demographic characteristics and the network effectiveness scale were included in the model, the change in ΔR² was statistically significant. A comparison of the ΔR² changes revealed that, relative to demographic characteristics and the effective network scale, the elements of network consistency exerted a greater influence on performance dimensions. The explanatory power for task, relationship, learning, innovation, and overall work performance increased by 8.5%, 4.5%, 4.6%, 5.7%, and 6.1%, respectively. Table 3 Multilevel linear regression analyses results TP RP LP IP WP Level 1 Standard Beta Level 2 Standard Beta Level 1 Standard Beta Level 2 Standard Beta Level 1 Standard Beta Level 2 Standard Beta Level 1 Standard Beta Level 2 Standard Beta Level 1 Standard Beta Level 2 Standard Beta Gender (Reference group = male) Female -0.140** -0.145** -0.081 -0.082 Years in position (reference group = 2–5 years) 6 to 10 years 0.067 0.074 11 to 15 years -0.083 -0.075 6 years or more 0.053 0.063 Job type (Reference group = clinical doctor) Nursing staff 0.110* 0.109* -0.049 -0.05 -0.056 -0.058 -0.093 -0.093 -0.033 -0.035 Auxiliary department staff 0.207*** 0.200*** 0.131* 0.125* 0.134* 0.122* 0.033 0.032 0.143** 0.136** Public health physician -0.024 -0.028 -0.075 -0.079 -0.085 -0.089 -0.028 -0.034 -0.064 -0.07 Others 0.096 0.095 0.032 0.041 0.043 0.05 0.028 0.033 0.06 0.066 Effective scale RWFN 0.086 0.091 0.095 0.105 0.065 0.076 0.139 0.16 0.129 0.146 SWFN -0.082 -0.075 -0.037 -0.041 0.016 0.02 -0.044 -0.06 -0.049 -0.055 FN 0.038 0.029 0.096 0.087 0.038 0.025 -0.008 -0.034 0.048 0.028 AN -0.144* -0.119 -0.136* -0.113 -0.131 -0.095 -0.086 -0.045 -0.151* -0.11 Network consistency RWFN-FN -0.079 -0.098 -0.134 -0.232* -0.189* RWFN-AN 0.091 0.133 0.187* 0.187* 0.195* SWFN-FN -0.015 0.129 0.071 0.119 0.108 SWFN-AN -0.065 -0.191 -0.182 -0.168 -0.196* R 2 0.085 0.092 0.045 0.056 0.046 0.063 0.057 0.076 0.061 0.079 Adjusted R 2 0.059 0.057 0.026 0.027 0.026 0.034 0.036 0.045 0.039 0.048 ΔR 2 0.085 0.007 0.045 0.011 0.046 0.017 0.057 0.019 0.061 0.018 F 3.295*** 2.598** 2.328* 1.923* 2.342* 2.169* 2.642** 2.464** 2.805 2.553 ΔF 3.295*** 0.708 2.328* 1.108 2.342* 1.785 2.642** 2.002 2.805 1.925 VIFmax 2.990 4.256 2.979 4.225 2.979 4.225 2.981 4.228 2.981 4.228 Notes. TP, Task performance; RP, Relationship performance; LP, Learning performance; IP, Innovation performance; WP, Work performance. RWFN, Reciprocal workflow network; SWFN, Sequential workflow network; FN, Friendship Network; AN, Advice Network. *** P <0.001;** P <0.01༛* P <0.05 Among the Level 2 independent variables, nursing staff demonstrated higher task performance (β = 0.109, p < 0.05) than clinicians in hypertension management, while ancillary department staff exhibited higher task performance (β = 0.200, p < 0.001), relationship performance (β = 0.125, p < 0.05), learning performance (β = 0.122, p < 0.05), and overall work performance (β = 0.136, p < 0.05). Conversely, female hypertension management personnel had lower innovation performance scores (β = − 0.145, p < 0.01) compared to their male counterparts. Table 3 presents the results of examining the impact of network consistency on workers’ performance. The consistency between the RWFN and FN negatively influenced the overall work performance (β = − 0.189, p < 0.05) and innovation performance (β = − 0.232, p < 0.05) of hypertension management personnel, contradicting the trends hypothesized in H1 and H1d. According to group dynamics theory, relationships and interactions within a group play a critical role in shaping the group’s work performance. When RWFN and FN become overly intertwined, informal social connections within the group may begin to overshadow formal work-related interactions, thereby undermining efficiency and role clarity. In such cases, employees may be more likely to adhere to informal rules arising from the FN rather than following the formal processes and standards of the workflow network, leading to non-standard work practices, decision-making errors, and reduced work efficiency, all of which negatively affect overall work performance. Additionally, a higher degree of overlap may foster groupthink, in which team members tend to conform to shared views within the FN, disregarding or resisting differing opinions or new ideas relevant to their work. This reinforces information homogeneity and constrains the generation of innovative perspectives, thereby inhibiting the improvement of innovation performance. It is also possible that, given that our research subjects are community-based hypertension management personnel, their core professional norms—prioritizing human-centered care and the provision of the best possible healthcare to patients—may not be substantially influenced by the formation of friendship ties, unlike competitive dynamics observed in corporate settings, and therefore may not impede medical work. The consistency between the RWFN and AN positively influenced the overall work performance (β = 0.195, p < 0.05), learning performance (β = 0.187, p < 0.05), and innovation performance (β = 0.187, p < 0.05) of hypertension management personnel. Therefore, hypotheses H2, H2c, and H2d were supported. Hypertension management personnel must engage in frequent communication within the reciprocal workflow, and the complexity of their tasks encourages the development of an extensive advisory network connections. Higher network consistency strengthens relational ties among network members[ 43 ], facilitating the formation of cooperative relationships, increasing familiarity and closeness, enhancing the willingness to exchange knowledge, broadening channels of information flow, and promoting the transfer and acquisition of tacit knowledge[ 44 ]. It also reduces the costs associated with searching for and acquiring resources, thereby improving work performance. The consistency between RWFN and AN further positively influenced learning performance and innovation performance. When this consistency is high, the shared behavioral norms and language established in the formal RWFN can be transferred to the informal AN[ 29 ]. Through interactions within the AN, employees are exposed to diverse perspectives and ideas, broadening their horizons and stimulating innovation in the workplace. This process enhances their ability to understand and apply the latest knowledge, as well as to better utilize existing knowledge and generate new knowledge [ 34 ]. The effect of consistency between the SWFN and the FN on work performance was not statistically significant. However, consistency between the SWFN and the AN had a significant negative impact on the overall work performance of hypertension management personnel (β = − 0.196, p < 0.05), supporting H4. This negative association may be attributed to the fact that excessive consultation and discussion can disrupt task execution within the workflow network, thereby reducing efficiency. A high degree of overlap between the SWFN and the AN can lead to knowledge redundancy and elevated communication costs, ultimately hindering improvements in work performance. Furthermore, cultural and communication differences among members may exacerbate these inefficiencies and weaken the effectiveness of the AN. A robustness analysis was performed by varying the sample size and randomly selecting subsamples. Li employed three randomly selected subsamples (90%, 80%, and 70%) for their robustness analysis, and this approach was adapted in the present study[ 45 ]. Specifically, 80% of the observations were randomly drawn from the full sample to form a subsample, and empirical testing was conducted using this subsample.The results demonstrated that the relationships between the core explanatory variables and the dependent variables remained statistically significant, with the direction of the coefficients consistent with the regression results obtained from the full sample, further supporting the validity of the study’s hypotheses. The details of these supplementary analyses are provided online. Discussion A significant body of social network research has demonstrated substantial effects of networks on performance [ 46 – 48 ]. In hypertension management, personnel work within formal workflows and informal relationships. In this study, we employed network consistency indicators to capture the interactions between these formal and informal networks and examined their impact on work performance in integrated healthcare [ 49 ]. The findings indicate that RWFN–AN consistency positively influences work performance, supporting the proposed hypothesis. When tasks require interactive collaboration, overlap between formal workflows and informal advisory ties can strengthen coordination and accelerate information exchange. Shared norms and language from the formal network can be transferred to the advice network, exposing personnel to diverse perspectives and ideas [ 34 ]. We found that SWFN–AN consistency negatively influenced work performance. When tasks are sequential and process-driven, excessive informal consultation can interrupt handoffs and slow execution, suggesting that overlap between informal and formal is not always beneficial for care delivery efficiency. The impact of RWFN–FN consistency on work performance was also negative. When reciprocal workflow ties become intertwined with friendship relations, informal norms may crowd out formal standards, leading to non‑compliance, decision errors, and reduced efficiency. These help explain heterogeneous conclusions on formal–informal interactions [ 11 , 50 , 51 ]. Our use of a network approach is valuable for examining how hypertension care teams communicate [ 52 ] and acquire, exchange, and integrate resources to support patient pathways [ 44 ]. By modeling formal workflow networks alongside informal advice and friendship ties, we describe interactions that span clinical coordination and everyday collaboration in integrated care settings. Specifically, we demonstrate that the impact of network consistency on organizational outcomes is neither uniform nor fixed, and research must simultaneously account for both formal and informal network types. For teams primarily structured around reciprocal workflows, team members should be encouraged to establish advisory relationships through formal or informal channels to facilitate the exchange of knowledge and experience. Conversely, for teams primarily based on sequential workflows, informal communication channels should be minimized, and a simplified formal work structure should be maintained to prevent efficiency losses due to excessive consultation. Taken together, these findings motivate a practice-oriented calibration. Organizational operations in hypertension care are shaped by network‑based mechanisms that structure collaboration among clinicians, nurses, and public health personnel [ 53 ]. Teams should cultivate both formal and informal networks. Practical measures include multidisciplinary case huddles and joint clinics, and spaces and routines that promote brief, purposeful interaction—e.g., shared workrooms in community health centers, colocated offices across partner institutions, or short pre‑/post‑clinic debriefs. Additionally, this study underscores the unique challenges associated with interorganizational collaboration. In today's highly interconnected [ 54 ] and knowledge-intensive society [ 55 ], cross-organizational and cross-professional collaboration has become a critical approach for fostering innovation and addressing complex problems. By adopting the perspective of interorganizational and interdisciplinary teams, this study analyzes the specific context of primary care for chronic disease management. This study illuminates the complex mechanisms through which network consistency influences work performance and provides theoretical and empirical support for organizational management in interorganizational collaboration settings. Our fine-grained differentiation addresses this gap and is well suited to evaluating performance in integrated, cross-organizational hypertension care. This study utilized a widely adopted measurement tool for assessing healthcare professionals’ individual work performance—the four-dimensional performance scale—which categorizes organizational performance into task performance, relationship performance, learning performance, and innovation performance [ 40 ].Through empirical analysis, we found that the consistency between formal and informal networks plays distinct roles in task execution, relationship maintenance, knowledge acquisition, and innovation. From the perspective of team performance evaluation in hypertension management, previous research has largely emphasized the completion of project-related tasks. However, team performance can also be assessed through the lens of social networks by examining team structure, cohesion, and relationships among members. Resources are often concentrated among members with higher centrality in the social network, who wield greater power and trust. These individuals have access to richer social capital and occupy key positions within their networks. Members who effectively leverage these relationships are more likely to receive support from resource holders, thereby enhancing the overall team performance. The conclusions of this study offer practical guidance for team-based management in hypertension care, especially for leaders coordinating cross-organizational collaboration across primary care, hospitals, and public health units. From a normative perspective, the study deepens understanding of care pathway design by showing how informal social ties can be purposefully incorporated into formal clinical workflows and governance processes. Such integration helps hypertension management teams convert diverse networks into value-creating capabilities—improving coordination, continuity of care, and ultimately patient outcomes. Conclusions Hypertension management personnel operate within intertwined formal workflows and informal social ties, and our findings show that the consistency between these networks has heterogeneous effects on work performance. Specifically, RWFN–AN consistency is associated with higher performance—supporting clinical learning, knowledge recombination, and coordinated problem-solving. By contrast, SWFN–AN consistency is linked to lower performance, suggesting that extensive consultation can impede efficiency when tasks require standardized, sequential execution. Likewise, excessive overlap between RWFN and FN is negatively associated with performance, indicating that strong affective ties can crowd out formal procedures when they become overly dominant. These results underscore that network overlap is not uniformly beneficial; its value depends on the type of formal workflow and the content of informal ties. Practically, teams organized around reciprocal workflows should cultivate advice relationships to accelerate learning and innovation, whereas teams operating under sequential workflows should streamline informal exchanges to preserve task efficiency. Declarations Consent for publication Not applicable. Competing interests The authors declare no conflict of interest. Ethics approval and consent to participate This study was approved by the Research Ethics Committee of Hangzhou Normal University (No. 2019-032), and all methods were carried out in accordance with the Declaration of Helsinki. Informed consent was fully obtained before the survey on the subjects. Funding Author Contribution M.Z. designed the study. G.J., Y.X. and L.Y. collected data. G.J. and Y.X. coded and analysed data. G.J. and Y.X. wrote the first draft of the manuscript. Y. F., Z.H. and X.Z. revised the manuscript. G.J., Y.X. and M.Z. intensively revised and prepared the final manuscript. All authors read and approved the final manuscript. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Contact information: [email protected] References Bodenheimer T, Sinsky C. From Triple to Quadruple Aim: Care of the Patient Requires Care of the Provider. Ann Fam Med. 2014;12(6):573–6. Miao Y, Wu J, Niu Y, Zhang LJCJHP. Retrospection of hierarchical medical system and key policy recommendations during the 14th five-year plan period. 2021, 14:1–6. Zhai YJCSS. 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Research on the social capital, knowledge quality and product innovation performance of knowledge-intensive firms in China. Front Psychol 2022, 13. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8190499","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":560152455,"identity":"d113a565-5a18-46ed-9129-d4ff60bdb663","order_by":0,"name":"Gege Jia","email":"","orcid":"","institution":"Hangzhou Normal University","correspondingAuthor":false,"prefix":"","firstName":"Gege","middleName":"","lastName":"Jia","suffix":""},{"id":560152456,"identity":"89ed637d-fb76-423e-880f-df50f71b9737","order_by":1,"name":"Yanyun Xu","email":"","orcid":"","institution":"Hangzhou Normal 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Many countries have adopted diverse measures to foster coordination and integration among healthcare organizations, including structural, functional, and clinical integration. The integration process involves establishing mechanisms for the division of labor and collaboration to ensure that patient services are coordinated across disciplines, institutions, and support systems. Cross-organizational and multidisciplinary collaboration is a central element of integrated care in the management of noncommunicable diseases (NCDs) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Evidence from global NCD management models, such as the U.S. The Healthy Communities Initiative [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], Finland’s North Karelia Project[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and Singapore’s Primary Care Networks [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] indicate that effective collaboration among primary care teams is critical for the prevention and control of NCDs. While structural and functional integration have been widely studied in the healthcare context, process-level and interpersonal integration remain underexplored and warrant further investigation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo address this research gap, we adopt social network analysis (SNA)—a theoretical and methodological approach that has matured since the 1990s [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. By illuminating the structural and dynamic properties of social ties, SNA offers tools to examine complex social phenomena beyond individual attributes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and enables a more comprehensive understanding of interpersonal connections and their functions in society [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin organizations, formal networks (the institutional “skeleton”) and informal networks (the affective “nervous system”) jointly shape behavior [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Formal networks can be further divided into reciprocal workflow network (RWFN) and sequential workflow network (SWFN) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. RWFN promotes knowledge recombination and innovation, whereas SWFN standardizes processes and supports efficient task completion [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Informal networks commonly include advice network (AN) and friendship network (FN). FN arises when employees maintain close interpersonal ties based on shared interests, mutual appreciation, trust, or liking, whereas AN is formed through spontaneous acts of seeking and providing counsel [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCritically, the overlap between formal and informal networks—“network consistency” (Soda and Zaheer 2012)—can exert negative, positive, or nonlinear effects [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Yet most studies still dichotomize networks (formal vs. informal) without fine-grained distinctions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], focus on single-firm settings with limited cross-organizational interaction and homogeneous professions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], or examine only a subset of network types [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Moreover, the determinants of performance in cross-organizational and multidisciplinary work teams remain largely unexplored. Although Xia examined the effects of complex cross-organizational and multidisciplinary social networks on team members’ job performance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the study primarily focused on the centrality and structural features of individual networks (such as advice, information, friendship, and trust) rather than on the interactions among multiple networks. Accordingly, there is an urgent need to investigate how the overlap between formal and informal networks influences team performance within cross-organizational and multidisciplinary teams.\u003c/p\u003e \u003cp\u003eHypertension is a leading NCD worldwide, with its prevalence increasing rapidly in low- and middle-income countries [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, few studies have examined the communication and collaboration networks among hypertension management team members or the effectiveness of these networks. Such teams typically consist of clinicians, nurses, and public health professionals working in community health service centers, township health centers, community health service stations, and village clinics. Since 2009, China has implemented a primary care–based hypertension management model that emphasizes strong team collaboration. This model not only provides a unique empirical setting for investigating how the overlap between formal and informal networks influences work performance but also serves as a prototypical case for applying SNA to explore the mechanisms underlying collaboration in healthcare. Grounded in social network theory, this study developed a conceptual framework that identifies the congruence between formal and informal networks among urban community hypertension managers as the independent variable and work performance as the dependent variable. Using correlation analysis and hierarchical regression, we empirically examined the mechanisms through which network congruence influences performance. This study broadens the application scope of social network theory and extends its relevance to the healthcare sector, particularly in research on human resources for health management. It also offers SNA-informed insights and actionable strategies for improving the performance of the hypertension management team.\u003c/p\u003e\n\u003ch3\u003eTheory and Hypotheses\u003c/h3\u003e\n\u003cp\u003eTheoretical perspectives from social network research contend that individual and collective behaviors are embedded within complex, multidimensional networks of social relations in which actors are mutually interdependent and subject to reciprocal constraints and influences. This perspective provides a theoretical foundation for analyzing the relational networks within community-based hypertension management teams. In particular, embeddedness theory posits that actors’ behaviors are conditioned by the specific interpersonal ties they maintain and their positional attributes within the overall network structure [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A moderate level of network embeddedness facilitates resource exchange and enhances collaborative efficiency, whereas excessive cohesion may lead to information homogenization and hinder innovation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFormal and informal relationships inevitably coexist within any subset of actors in a network or among the alters connected to a focal actor; consequently, their interplay generates distinct patterns of overlap between these two types of networks [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Soda and Zaheer define network congruence as the degree to which employees’ formal and informal networks within an organization are interrelated and exhibit cross-network overlap, a formulation that has become a foundational reference for further research. In their study, Soda and Zaheer applied the convergent correlations algorithm of the structural equivalence procedure to measure consistency across the networks[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Currently, there is no universally accepted measure of “network congruence.” Most studies operationalized it by calculating the correlation coefficients between the corresponding matrix elements of the two networks. For instance, LV used the quadratic assignment procedure (QAP) method to estimate congruence between formal and informal networks at the departmental level[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, comparison, correlation, and Euclidean distance methods were employed to compare the structural equivalence of nodes.\u003c/p\u003e \u003cp\u003eDrawing on, we measured network consistency by computing the correlation coefficients that reflect the degree of overlap between formal (reciprocal and sequential workflows) and informal (friendship and advice) networks. Based on this classification, we identified four distinct types of network consistency among hypertension management personnel: (1) RWFN–FN consistency (between the reciprocal workflow network and the friendship network); (2) RWFN–AN consistency (between the reciprocal workflow network and the advice network); (3) SWFN–FN consistency (between the sequential workflow network and the friendship network); and (4) SWFN–AN consistency (between the sequential workflow network and the advice network).\u003c/p\u003e \u003cp\u003eBecause follow-up visits, adjustments to individualized intervention plans, and the management of acute events entail substantial uncertainty, they are best supported by dynamic and RFWN. Sustained communication and collaboration enable team members to flexibly respond to the complexities that arise during follow-up care. Such an organizational arrangement not only strengthens professional ties but also fosters friendship and trust; over time, the FN tends to expand and consolidate [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, the presence of structural holes can reduce work performance, as actors occupying brokerage positions may block or distort information flows to maximize personal benefits [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. When RWFN–FN consistency is high, dense friendship ties among colleagues involved in task interactions increase both the frequency and quality of exchanges, lower communication costs, and alleviate the obstructive impact of structural holes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Such overlap enhances the execution of core activities, such as documenting follow-up visits and collecting data, thereby improving task performance. Moreover, friendship-based working relationships help cultivate a positive team climate and promote cross-departmental collaboration, ultimately strengthening relationship performance. Frequent social interactions create opportunities for experience sharing, and the cross-fertilization of ideas stimulates innovative thinking, thereby enhancing learning and innovation performance. Accordingly, we propose the following hypotheses:\u003c/p\u003e \u003cp\u003eH1: RWFN–FN consistency positively influences the overall work performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH1a: RWFN–FN consistency positively influences the task performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH1b: RWFN–FN consistency positively influences the relational performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH1c: RWFN–FN consistency positively influences the learning performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH1d: RWFN–FN consistency positively influences the innovation performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eHigh RWFN–AN consistency indicates that hypertension managers engage in extensive mutual consultation with colleagues while performing hypertension management tasks. In day-to-day work, such as diagnosing and treating primary hypertension and conducting long-term follow-ups, orderly interactions with colleagues deepen their familiarity with existing medical knowledge and promote its diffusion among the staff. Moreover, because policies governing essential public health services are frequently revised and standards are continuously elevated, advice ties are vital for disseminating and interpreting policy information[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Advice-seeking exchanges of opinions within the reciprocal workflow allow employees to perform hypertension management tasks more efficiently and creatively, thereby strengthening collaboration and innovation and enhancing overall work performance [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. High RWFN–AN consistency allows the professional knowledge embedded in the workflow to be effectively mobilized and integrated within the resource base and trust structure of the advice network. This integration promotes resource sharing within the team [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and ultimately improves both team and individual performance, particularly in terms of learning and innovation performance. Accordingly,we propose the following hypotheses:\u003c/p\u003e \u003cp\u003eH2: RWFN–AN consistency positively influences the overall work performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH2a: RWFN–AN consistency positively influences the task performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH2b: RWFN–AN consistency positively influences the relational performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH2c: RWFN–AN consistency positively influences the learning performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH2d: RWFN–AN consistency positively influences the innovation performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eThe formal SWFN generally exhibit a clear hierarchical structure, with the primary objective of optimizing the efficiency of flows within the network. In outpatient services at community health service centers, for instance, such structures impose stringent requirements on both the quantity and quality of service delivery. When SWFN–FN consistency is high, extensive friendship ties among members along the service chain help reduce individual role conflict [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], create additional channels for information exchange, and facilitate smooth day-to-day handoffs, thereby enhancing the task performance of employees[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Formal working relationships can foster the development of a FN, further enhancing relational performance. The two mutually reinforce each other in a virtuous cycle that ultimately improves both individual and team performance. Conversely, when the overlap between SWFN and an informal FN is low, strong ties formed through friendship cannot be effectively leveraged within formal work processes. This misoverlap may disrupt one-way handoffs, impede information transmission, and slow operational progress, leading to adverse effects on overall team effectiveness. Accordingly, we propose the following hypotheses:\u003c/p\u003e \u003cp\u003eH3: SWFN–FN consistency positively influences the overall work performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH3a: SWFN–FN consistency positively influences the task performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH3b: SWFN–FN consistency positively influences the relational performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH3c: SWFN–FN consistency positively influences the learning performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH3d: SWFN–FN consistency positively influences the innovation performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eWhen SWFN–AN consistency is high, a hypertension manager occupies a prominent advisory position along the task chain, frequently being consulted by upstream colleagues while also seeking input from those downstream. The core principle of sequential workflow is strict adherence to clear rules and protocols that ensure orderly operations. Team members are required to follow clinical operating standards and complete their assigned tasks in accordance with the specifications of the National Essential Public Health Services Program. In this context, excessive communication, particularly consultations that merely replicate information already embedded in the workflow, can hinder efficiency and effectiveness. Such exchanges may create knowledge redundancy by repeatedly transmitting identical information, thereby increasing communication costs. Furthermore, overcommunication can lead to knowledge conflicts when members hold different interpretations of the same procedures, ultimately disrupting smooth execution [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Accordingly, we propose the following hypotheses:\u003c/p\u003e \u003cp\u003eH4: SWFN–AN consistency negatively affects the overall work performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH4a: SWFN–AN consistency negatively affects the task performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH4b: SWFN–AN consistency negatively affects the relational performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH4c: SWFN–AN consistency negatively affects the learning performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eH4d: SWFN–AN consistency negatively affects the innovation performance of hypertension management personnel.\u003c/p\u003e \u003cp\u003eIn summary, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the logical framework of this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \n\n "},{"header":"Methods","content":"\u003cp\u003eTo test our hypotheses, we conducted a questionnaire survey of hypertension management personnel at nine community health service centers in Hangzhou, China. The survey items were primarily adapted from established scales developed in widely cited, authoritative studies. These instruments have been validated in diverse contexts and have consistently demonstrated high reliability and validity. In the preparatory phase, we first contacted the directors of the participating centers to obtain staff rosters. Following standardized egocentric network survey protocols, we cleaned, coded, and compiled the rosters into a list of respondent identification numbers to facilitate subsequent network data collection.\u003c/p\u003e\u003ch3\u003eSurvey Questionnaire\u003c/h3\u003e\u003cp\u003eGiven that this study targets a specific population—urban community hypertension management personnel—we first adapted the relevant measurement instruments to align with the study context and respondent characteristics and developed a draft questionnaire. To further ensure scientific rigor and contextual fit, we convened an expert panel of nine members, including hospital management scholars, managers from community health service centers, and representatives from frontline primary care practitioners. The panel conducted an in-depth review to assess whether the dimensional structure of each construct was appropriate, whether the item selection was representative, and whether any items were redundant or ambiguously worded. Based on the feedback of the panel, we carefully reviewed and revised the draft, removing or refining inappropriate items, and ultimately finalizing a questionnaire that was well-suited to the objectives of this study.\u003c/p\u003e\u003cp\u003eThe questionnaire comprised three sections: (i) Background information on hypertension management personnel, (ii) a work performance scale, and (iii) a social network scale (Appendix A1). Except for the background items and social network roster data, all items were rated on a five-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5). Each item score represents the degree of agreement or endorsement by the respondents. For each construct, we computed the arithmetic mean of its constituent items to obtain a composite score, with higher values reflecting higher levels on the corresponding dimension (such as work performance). In the social network section, participants were asked to list the identification codes of personnel corresponding to each prompt using the provided roster of staff codes.\u003c/p\u003e\n\u003ch3\u003eSample data\u003c/h3\u003e\n\u003cp\u003eData for the sample were collected in Hangzhou, China, from September 25 to October 25, 2023. After selecting the study city, we randomly selected three counties and districts from it. Within each, we chose three community health service centers and their affiliated stations as field sites and surveyed the members of the hypertension management teams. In total, 436 questionnaires were distributed, and after excluding invalid responses, 401 valid questionnaires were obtained, yielding a valid response rate of 92.0%. The sample comprised 169 clinicians (42.1%), 185 nursing staff (46.2%), 23 ancillary staff (5.7%), 11 public health physicians (2.8%), and 13 other personnel (3.2%). The descriptive statistics and composition of urban community hypertension management personnel are presented in Appendix A2.\u003c/p\u003e\n\u003ch3\u003eVariable Measurement\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIndependent variable: Formal-informal network consistency\u003c/h2\u003e \u003cp\u003eThe formal network was divided into two components: the SWFN and the formal RWFN, measured using items adapted from [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The informal network was similarly divided into an AN and a FN; the AN was measured using items from Luo [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], while the FN was measured using items from Krackhardt and Brass [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. To construct the formal (RWFN and SWFN) and informal (AN and FN) networks for each respondent, a social network nomination approach was employed. Network consistency was operationalized as the degree of overlap between formal and informal networks and was calculated using similarity-based measures. Details of the measurement and computation procedures are provided in the Appendix.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDependent variable: Work performance\u003c/h2\u003e \u003cp\u003eWork performance was measured using the well-established scale developed by Han Yi[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The instrument captures four dimensions\u0026mdash;relationship, task, learning, and innovation performance\u0026mdash;with item wording adapted to fit the context of the urban community hypertension management teams. The final scale comprised 20 items: Four each for task performance, 4 for learning performance, and 6 each for relationship performance and innovation performance (Appendix). Previous research on performance assessment methods has primarily relied on both objective and subjective evaluations. Although objective evaluation provides more precise estimates, it depends heavily on a rigorous and consistent assessment system supported by high-quality data. Conversely, subjective evaluation is somewhat less precise; however, studies have demonstrated that its correlation with actual job performance is stronger than expected [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Moreover, because subjective evaluation applies uniform scoring criteria and is relatively easy to administer, it has become the predominant approach in academic research, particularly when examining the interrelationships between variables[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Following this convention, the present study adopted a subjective evaluation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eControl variables\u003c/h3\u003e\n\u003cp\u003ePrior research indicates that an actor\u0026rsquo;s position within formal and informal networks can influence work performance. Accordingly, we included the effective size of formal networks (RWFN and SWFN) and informal networks (FN and AN) as control variables to account for nonredundant ties in the network. We also controlled for demographic covariates, including gender, age, work experience, and educational attainment.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eReliability and validity\u003c/h2\u003e \u003cp\u003eFirst, Cronbach\u0026rsquo;s alpha coefficients for all variables exceeded 0.80, surpassing the commonly accepted threshold of 0.70, thereby indicating strong internal consistency and reliability. The composite reliability (CR) for the task performance, relationship performance, learning performance, and innovation performance dimensions was above 0.800 in all cases, satisfying the conventional requirement that the CR value should exceed 0.600. The survey instrument was adapted from a set of well-established, validated scales and further refined through an extensive literature review and structured interviews with domestic experts and relevant administrators, ensuring strong content validity. We assessed the measurement properties of the scale using the SPSS (version 26.0). The Kaiser\u0026ndash;Meyer\u0026ndash;Olkin measure of sampling adequacy was 0.930, and Bartlett\u0026rsquo;s test of sphericity yielded χ\u0026sup2; = 5471.963 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), confirming that the data were suitable for factor analysis. Details are further provided in the Appendix.\u003c/p\u003e \u003cp\u003eTo further assess the validity of each dimension, we employed a structural equation model to conduct a confirmatory factor analysis (CFA) to evaluate both convergent and discriminant validity. Without pre-specifying the number of factors, we performed principal component analysis with orthogonal varimax rotation and retained factors with eigenvalues greater than 1. Four factors were extracted from the 20 structured items, which accounted for a cumulative variance of 71.114%.\u003c/p\u003e \u003cp\u003eWe then conducted a CFA on the work performance dimensions of urban community hypertension management personnel using Analyze of Moment Structures software (version 24.0). The results indicated that χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.617, the Standardized Root Mean Square Residual (SRMR) was 0.076, the Root Mean Square Error of Approximation (RMSEA) was 0.081, the Goodness-of-Fit Index (GFI) was 0.854, the Adjusted Goodness-of-Fit Index (AGFI) was 0.813, the Incremental Fit Index (IFI) was 0.921, the Comparative Fit Index (CFI) was 0.920, and the Tucker\u0026ndash;Lewis Index (TLI) was 0.908. These indices met the conventional model fit criteria, demonstrating good structural validity of the scale. All item factor loadings exceeded 0.60, and the Average Variance Extracted (AVE) for each dimension was above 0.50, supporting convergent validity. Moreover, for each pair of constructs, the inter-construct correlation was lower than the square root of that construct\u0026rsquo;s AVE, confirming satisfactory discriminant validity (Appendix B).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics and correlations\u003c/h2\u003e \u003cp\u003eQAP is a method used to compare the similarity of elements between two matrices and is commonly applied to analyze the relationships between networks. In this study, a QAP correlation analysis was employed to examine the correlations among the RWFN, SWFN, FN, and AN. The results revealed that the correlations among all networks were statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with correlation coefficients exceeding 0.500, indicating strong interrelationships between the networks (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eCorrelations among the dimensions of the social network\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReciprocal workflow network\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSequential workflow network\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdvice Network\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFriendship Network\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReciprocal workflow network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSequential workflow network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.701**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvice Network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.652**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.749**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFriendship Network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.637**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.704**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.814**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe network data were processed using UCINET VI to measure consistency between formal and informal networks. Correlation coefficients reflecting the degree of overlap between RWFN and SWFN with FN and AN were calculated based on the similarity of the actors\u0026rsquo; network structures. The correlation coefficients ranged from \u0026minus;\u0026thinsp;1.00 (indicating completely opposite connections between the two networks) to 0 (indicating no correlation), and +\u0026thinsp;1.00 (indicating identical connections between the two networks).\u003c/p\u003e \u003cp\u003eFour network consistency indicators were derived from this analysis.Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptive statistics and correlations between variables. The average scores and variances were computed based on the items within each dimension. Overall, the respondents exhibited varying degrees of overlap between their formal and informal networks, with the consistency between the SWFN and the AN being the highest, indicating the greatest overlap in connections. Pearson\u0026rsquo;s rank correlation coefficient was applied to examine the relationships between network consistency and the work performance of hypertension management personnel, as well as the correlations among other related factors.\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\u003eDescriptive statistics and correlations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Consistency between the reciprocal workflow network and the friendship network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Consistency between the reciprocal workflow network and the advice network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.808**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Consistency between the sequential workflow networks and the friendship network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.407**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.352**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Consistency between the sequential workflow networks and the advice network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.313**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.396**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.840**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Task performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.111*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Relationship performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.553**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Learning performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.557**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.712**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Innovation performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.270**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.492**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.439**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. Work performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.690**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.867**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.813**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.774**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003eNote.\u003c/em\u003e SD, Standard deviation; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHypothesis testing results\u003c/h2\u003e \u003cp\u003eIndependent-samples t-tests and one-way analysis of variance (ANOVA) were conducted to examine the differences in task performance, relationship performance, learning performance, innovation performance, and overall work performance scores of urban community hypertension management personnel according to their demographic characteristics. The results of the one-way ANOVA indicated significant differences in demographic variables such as gender, years of work experience, and job type among the hypertension management personnel (Appendix C1). Correlation analysis further revealed meaningful associations between network consistency and various dimensions of work performance. Based on these findings, we performed five multilevel linear regression analyses. The overall work performance score and the scores for each performance dimension were used as dependent variables, while demographic characteristics that exhibited statistically significant differences in the one-way ANOVA and the effective scale of structural holes were included as Level 1 control variables. Network consistency variables were incorporated as Level 2 predictors (Appendix C2). Before running the models, dummy variable coding was applied to the unordered categorical variables that demonstrated statistical significance in the one-way ANOVA, with the coding results presented in Appendix C3.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the multilevel linear regression analysis, in which task, relationship, learning, innovation, and overall work performance scores of urban community hypertension management personnel were treated as dependent variables. The results demonstrated that the variance inflation factors for all variables were below 10, indicating the absence of significant multicollinearity.When demographic characteristics and the network effectiveness scale were included in the model, the change in ΔR\u0026sup2; was statistically significant. A comparison of the ΔR\u0026sup2; changes revealed that, relative to demographic characteristics and the effective network scale, the elements of network consistency exerted a greater influence on performance dimensions. The explanatory power for task, relationship, learning, innovation, and overall work performance increased by 8.5%, 4.5%, 4.6%, 5.7%, and 6.1%, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultilevel linear regression analyses results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eWP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel 1\u003c/p\u003e \u003cp\u003eStandard Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel 2\u003c/p\u003e \u003cp\u003eStandard Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLevel 1\u003c/p\u003e \u003cp\u003eStandard Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLevel 2\u003c/p\u003e \u003cp\u003eStandard Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLevel 1\u003c/p\u003e \u003cp\u003eStandard Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLevel 2\u003c/p\u003e \u003cp\u003eStandard Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLevel 1\u003c/p\u003e \u003cp\u003eStandard Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLevel 2\u003c/p\u003e \u003cp\u003eStandard Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLevel 1\u003c/p\u003e \u003cp\u003eStandard Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eLevel 2\u003c/p\u003e \u003cp\u003eStandard Beta\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Reference group\u0026thinsp;=\u0026thinsp;male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.140**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.145**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears in position (reference group\u0026thinsp;=\u0026thinsp;2\u0026ndash;5 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 to 10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 to 15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 years or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob type (Reference group\u0026thinsp;=\u0026thinsp;clinical doctor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNursing staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.110*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.109*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuxiliary department staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.207***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.200***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.131*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.125*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.134*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.122*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.143**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.136**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic health physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffective scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRWFN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWFN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.144*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.136*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.151*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRWFN-FN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.232*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.189*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRWFN-AN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.187*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.187*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.195*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWFN-FN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSWFN-AN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.196*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eΔR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.295***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.598**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.328*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.923*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.342*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.169*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.642**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.464**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eΔF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.295***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.328*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.342*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.642**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVIFmax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003eNotes.\u003c/em\u003e TP, Task performance; RP, Relationship performance; LP, Learning performance; IP, Innovation performance; WP, Work performance.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eRWFN, Reciprocal workflow network; SWFN, Sequential workflow network; FN, Friendship Network; AN, Advice Network.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e*** \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001;** \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01༛* \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the Level 2 independent variables, nursing staff demonstrated higher task performance (β\u0026thinsp;=\u0026thinsp;0.109, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than clinicians in hypertension management, while ancillary department staff exhibited higher task performance (β\u0026thinsp;=\u0026thinsp;0.200, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), relationship performance (β\u0026thinsp;=\u0026thinsp;0.125, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), learning performance (β\u0026thinsp;=\u0026thinsp;0.122, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and overall work performance (β\u0026thinsp;=\u0026thinsp;0.136, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, female hypertension management personnel had lower innovation performance scores (β = \u0026minus;\u0026thinsp;0.145, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) compared to their male counterparts.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of examining the impact of network consistency on workers\u0026rsquo; performance. The consistency between the RWFN and FN negatively influenced the overall work performance (β = \u0026minus;\u0026thinsp;0.189, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and innovation performance (β = \u0026minus;\u0026thinsp;0.232, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of hypertension management personnel, contradicting the trends hypothesized in H1 and H1d. According to group dynamics theory, relationships and interactions within a group play a critical role in shaping the group\u0026rsquo;s work performance. When RWFN and FN become overly intertwined, informal social connections within the group may begin to overshadow formal work-related interactions, thereby undermining efficiency and role clarity. In such cases, employees may be more likely to adhere to informal rules arising from the FN rather than following the formal processes and standards of the workflow network, leading to non-standard work practices, decision-making errors, and reduced work efficiency, all of which negatively affect overall work performance. Additionally, a higher degree of overlap may foster groupthink, in which team members tend to conform to shared views within the FN, disregarding or resisting differing opinions or new ideas relevant to their work. This reinforces information homogeneity and constrains the generation of innovative perspectives, thereby inhibiting the improvement of innovation performance.\u003c/p\u003e \u003cp\u003eIt is also possible that, given that our research subjects are community-based hypertension management personnel, their core professional norms\u0026mdash;prioritizing human-centered care and the provision of the best possible healthcare to patients\u0026mdash;may not be substantially influenced by the formation of friendship ties, unlike competitive dynamics observed in corporate settings, and therefore may not impede medical work.\u003c/p\u003e \u003cp\u003eThe consistency between the RWFN and AN positively influenced the overall work performance (β\u0026thinsp;=\u0026thinsp;0.195, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), learning performance (β\u0026thinsp;=\u0026thinsp;0.187, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and innovation performance (β\u0026thinsp;=\u0026thinsp;0.187, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of hypertension management personnel. Therefore, hypotheses H2, H2c, and H2d were supported.\u003c/p\u003e \u003cp\u003eHypertension management personnel must engage in frequent communication within the reciprocal workflow, and the complexity of their tasks encourages the development of an extensive advisory network connections. Higher network consistency strengthens relational ties among network members[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], facilitating the formation of cooperative relationships, increasing familiarity and closeness, enhancing the willingness to exchange knowledge, broadening channels of information flow, and promoting the transfer and acquisition of tacit knowledge[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. It also reduces the costs associated with searching for and acquiring resources, thereby improving work performance. The consistency between RWFN and AN further positively influenced learning performance and innovation performance. When this consistency is high, the shared behavioral norms and language established in the formal RWFN can be transferred to the informal AN[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Through interactions within the AN, employees are exposed to diverse perspectives and ideas, broadening their horizons and stimulating innovation in the workplace. This process enhances their ability to understand and apply the latest knowledge, as well as to better utilize existing knowledge and generate new knowledge [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe effect of consistency between the SWFN and the FN on work performance was not statistically significant. However, consistency between the SWFN and the AN had a significant negative impact on the overall work performance of hypertension management personnel (β = \u0026minus;\u0026thinsp;0.196, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), supporting H4. This negative association may be attributed to the fact that excessive consultation and discussion can disrupt task execution within the workflow network, thereby reducing efficiency. A high degree of overlap between the SWFN and the AN can lead to knowledge redundancy and elevated communication costs, ultimately hindering improvements in work performance. Furthermore, cultural and communication differences among members may exacerbate these inefficiencies and weaken the effectiveness of the AN.\u003c/p\u003e \u003cp\u003eA robustness analysis was performed by varying the sample size and randomly selecting subsamples. Li employed three randomly selected subsamples (90%, 80%, and 70%) for their robustness analysis, and this approach was adapted in the present study[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Specifically, 80% of the observations were randomly drawn from the full sample to form a subsample, and empirical testing was conducted using this subsample.The results demonstrated that the relationships between the core explanatory variables and the dependent variables remained statistically significant, with the direction of the coefficients consistent with the regression results obtained from the full sample, further supporting the validity of the study\u0026rsquo;s hypotheses. The details of these supplementary analyses are provided online.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":" \u003cp\u003eA significant body of social network research has demonstrated substantial effects of networks on performance [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In hypertension management, personnel work within formal workflows and informal relationships. In this study, we employed network consistency indicators to capture the interactions between these formal and informal networks and examined their impact on work performance in integrated healthcare [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings indicate that RWFN\u0026ndash;AN consistency positively influences work performance, supporting the proposed hypothesis. When tasks require interactive collaboration, overlap between formal workflows and informal advisory ties can strengthen coordination and accelerate information exchange. Shared norms and language from the formal network can be transferred to the advice network, exposing personnel to diverse perspectives and ideas [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. We found that SWFN\u0026ndash;AN consistency negatively influenced work performance. When tasks are sequential and process-driven, excessive informal consultation can interrupt handoffs and slow execution, suggesting that overlap between informal and formal is not always beneficial for care delivery efficiency. The impact of RWFN\u0026ndash;FN consistency on work performance was also negative. When reciprocal workflow ties become intertwined with friendship relations, informal norms may crowd out formal standards, leading to non‑compliance, decision errors, and reduced efficiency. These help explain heterogeneous conclusions on formal\u0026ndash;informal interactions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur use of a network approach is valuable for examining how hypertension care teams communicate [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and acquire, exchange, and integrate resources to support patient pathways [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. By modeling formal workflow networks alongside informal advice and friendship ties, we describe interactions that span clinical coordination and everyday collaboration in integrated care settings. Specifically, we demonstrate that the impact of network consistency on organizational outcomes is neither uniform nor fixed, and research must simultaneously account for both formal and informal network types. For teams primarily structured around reciprocal workflows, team members should be encouraged to establish advisory relationships through formal or informal channels to facilitate the exchange of knowledge and experience. Conversely, for teams primarily based on sequential workflows, informal communication channels should be minimized, and a simplified formal work structure should be maintained to prevent efficiency losses due to excessive consultation.\u003c/p\u003e \u003cp\u003eTaken together, these findings motivate a practice-oriented calibration. Organizational operations in hypertension care are shaped by network‑based mechanisms that structure collaboration among clinicians, nurses, and public health personnel [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Teams should cultivate both formal and informal networks. Practical measures include multidisciplinary case huddles and joint clinics, and spaces and routines that promote brief, purposeful interaction\u0026mdash;e.g., shared workrooms in community health centers, colocated offices across partner institutions, or short pre‑/post‑clinic debriefs.\u003c/p\u003e \u003cp\u003eAdditionally, this study underscores the unique challenges associated with interorganizational collaboration. In today's highly interconnected [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and knowledge-intensive society [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], cross-organizational and cross-professional collaboration has become a critical approach for fostering innovation and addressing complex problems. By adopting the perspective of interorganizational and interdisciplinary teams, this study analyzes the specific context of primary care for chronic disease management. This study illuminates the complex mechanisms through which network consistency influences work performance and provides theoretical and empirical support for organizational management in interorganizational collaboration settings. Our fine-grained differentiation addresses this gap and is well suited to evaluating performance in integrated, cross-organizational hypertension care.\u003c/p\u003e \u003cp\u003eThis study utilized a widely adopted measurement tool for assessing healthcare professionals\u0026rsquo; individual work performance\u0026mdash;the four-dimensional performance scale\u0026mdash;which categorizes organizational performance into task performance, relationship performance, learning performance, and innovation performance [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].Through empirical analysis, we found that the consistency between formal and informal networks plays distinct roles in task execution, relationship maintenance, knowledge acquisition, and innovation. From the perspective of team performance evaluation in hypertension management, previous research has largely emphasized the completion of project-related tasks. However, team performance can also be assessed through the lens of social networks by examining team structure, cohesion, and relationships among members. Resources are often concentrated among members with higher centrality in the social network, who wield greater power and trust. These individuals have access to richer social capital and occupy key positions within their networks. Members who effectively leverage these relationships are more likely to receive support from resource holders, thereby enhancing the overall team performance.\u003c/p\u003e \u003cp\u003eThe conclusions of this study offer practical guidance for team-based management in hypertension care, especially for leaders coordinating cross-organizational collaboration across primary care, hospitals, and public health units. From a normative perspective, the study deepens understanding of care pathway design by showing how informal social ties can be purposefully incorporated into formal clinical workflows and governance processes. Such integration helps hypertension management teams convert diverse networks into value-creating capabilities\u0026mdash;improving coordination, continuity of care, and ultimately patient outcomes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHypertension management personnel operate within intertwined formal workflows and informal social ties, and our findings show that the consistency between these networks has heterogeneous effects on work performance. Specifically, RWFN\u0026ndash;AN consistency is associated with higher performance\u0026mdash;supporting clinical learning, knowledge recombination, and coordinated problem-solving. By contrast, SWFN\u0026ndash;AN consistency is linked to lower performance, suggesting that extensive consultation can impede efficiency when tasks require standardized, sequential execution. Likewise, excessive overlap between RWFN and FN is negatively associated with performance, indicating that strong affective ties can crowd out formal procedures when they become overly dominant. These results underscore that network overlap is not uniformly beneficial; its value depends on the type of formal workflow and the content of informal ties. Practically, teams organized around reciprocal workflows should cultivate advice relationships to accelerate learning and innovation, whereas teams operating under sequential workflows should streamline informal exchanges to preserve task efficiency.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNot applicable.\u003c/span\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe authors declare no conflict of interest.\u003c/span\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThis study was approved by the Research Ethics Committee of Hangzhou Normal University (No. 2019-032), and all methods were carried out in accordance with the Declaration of Helsinki. Informed consent was fully obtained before the survey on the subjects.\u003c/span\u003e\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.Z. designed the study. G.J., Y.X. and L.Y. collected data. G.J. and Y.X. coded and analysed data. G.J. and Y.X. wrote the first draft of the manuscript. Y. F., Z.H. and X.Z. revised the manuscript. G.J., Y.X. and M.Z. intensively revised and prepared the final manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Contact information: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBodenheimer T, Sinsky C. From Triple to Quadruple Aim: Care of the Patient Requires Care of the Provider. 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Front Psychol 2022, 13.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hypertension management, Social networks, Network consistency, Job performance","lastPublishedDoi":"10.21203/rs.3.rs-8190499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8190499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIntegrated care is essential for managing noncommunicable diseases, with cross-organizational and multidisciplinary collaboration playing a key role. However, while structural and functional integration in healthcare have been widely studied, the impact of process-level and interpersonal integration remains underexplored. This study applies social network analysis to examine how the overlap between formal and informal networks influences the job performance of hypertension management personnel. Using China\u0026rsquo;s primary care\u0026ndash;based hypertension management model as a case, we explore how network congruence shapes team effectiveness in a multidisciplinary, cross-organizational setting.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional survey of hypertension management personnel from nine community health service centers in Hangzhou, China, between September 25 and October 25, 2023. A total of 436 questionnaires were distributed, and 401 valid responses were obtained. The survey included validated instruments to measure formal networks and informal networks. Work performance was assessed across four dimensions: task, relational, learning, and innovation. Network consistency was calculated based on the overlap between formal and informal networks. Data were analyzed using correlation analysis and multilevel linear regression analysis, controlling for network size and demographics (gender, age, work experience, education).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCorrelation analysis revealed significant relationships between formal and informal networks, with network consistency influencing overall performance. Specifically, the consistency between the reciprocal workflow network and the advice network positively influences hypertension management personnel\u0026rsquo;s overall work performance, learning performance, and innovation performance. In contrast, the consistency between the reciprocal workflow network and the friendship network negatively influences overall work performance and innovation performance among hypertension management personnel. Finally, the consistency between the sequential workflow network and the advice network negatively influences hypertension management personnel\u0026rsquo;s overall work performance.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings elucidate how interactions between formal and informal networks affect job performance, offer a new perspective for improving the performance of hypertension management team members, and enriche and expands the substantive scope of research on human resources for health management.\u003c/p\u003e","manuscriptTitle":"Exploring the Impact of Formal and Informal Network Consistency on Job Performance in Hypertension Management Teams: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 08:53:46","doi":"10.21203/rs.3.rs-8190499/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0ebd7073-b32c-4a5e-a9e2-b9f4fb18e11c","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T09:41:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 08:53:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8190499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8190499","identity":"rs-8190499","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}