Key Elements and Implementation Path of the Counterpart Support Policy: A Case Study of Urban Doctors Servicing in Rural Hospitals in Beijing

Key Elements and Implementation Path of the Counterpart Support Policy: A Case Study of Urban Doctors Servicing in Rural Hospitals in Beijing | 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 Key Elements and Implementation Path of the Counterpart Support Policy: A Case Study of Urban Doctors Servicing in Rural Hospitals in Beijing Chen Lu, Yao Liu, Jiale Sheng, Yurou Zou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5371564/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 Counterpart support policy is a pivotal public management strategy aimed at bridging regional disparities. This paper aims to explore the determinants and execution pathways of this policy, with a focus on enhancing its efficacy. Utilizing the urban doctors serving rural areas (UDSR) policy in Beijing as a case study, the research is grounded in Van Meter and Van Horn’s policy implementation framework and employs fsQCA methods to scrutinize counterpart support hospitals. The findings pinpoint three critical factors shaping the policy's impact: the number of support projects, the diversification of subsidy sources, and the prescription rights for urban doctors working in rural medical institutions. Four distinct implementation pathways are identified: Pathway 1 is driven by goals and cognition, Pathway 2 by professional matching, Pathway 3 by external funding, and Pathway 4 by a combination of comprehensive factors. Furthermore, the study traces two evolutionary trajectories for the UDSR policy's effectiveness. The dominant trajectory demonstrates enduring success through a comprehensive factor-driven approach. In contrast, the transitional trajectory showcases an evolution from initial dependency on external funding to a phase of professional and goal alignment, culminating in internal alignment with organizational attributes and the ideologies of implementers, signifying an adaptive evolution in policy execution. counterpart support policy urban doctors servicing in rural hospitals policy implementation QCA influencing factors implementation path Figures Figure 1 Figure 2 1. Proposing the problem In rapidly developing economies, such as China, India, and Brazil, remarkable economic growth has often been accompanied by pronounced regional development disparities and socioeconomic inequalities. These disparities manifest in various forms, including gaps in income distribution, access to healthcare, education, and infrastructure between urban and rural areas within the same country. The phenomenon of regional development disparities highlights the need for targeted policy interventions to mitigate the socioeconomic inequalities[ 1] . A promising strategy that is gaining attention is the concept of counterpart support, wherein resources, expertise, and services are directed from more developed regions to less developed ones, aiming to narrow the regional disparities. China, with serious regional disparity, usually embrace the implementation of counterpart support policies as an essential governance measures[ 2] . These policies aim to address the imbalances through targeted assistance from more developed regions to less developed ones. When it turns to the medical services field, which play a pivotal role in enhancing the well-being and happiness of a nation's populace, a notable gap persists in the allocation of medical resources between urban and rural areas across many countries[ 3] . For example, rural areas often face a deficiency in medical development and emblematic of imbalanced and insufficient progress[ 4] . In addressing the healthcare challenges prevalent in rural areas, Chinese central government issuses the "Decision of the Central Committee of the Communist Party of China and the State Council on Further Strengthening Rural Health Work", explicitly proposes increasing support for farmers and agriculture through health programs and poverty alleviation efforts, mandating the participation of large and medium-sized medical institutions in cities and the military in the "counterpart support" program[ 5] . In alignment with the directives of the central government, local governments have initiated various measures. In Beijing, for instance, the Municipal Health Bureau and the Municipal Personnel Bureau jointly issued a directive titled "Notice on Relevant Issues Concerning Urban Doctors Serving at Grassroots Medical Institutions before Being Promoted to Professional and Technical Positions." This policy strictly regulates the tenure of urban doctors' service in remote towns and townships, as a prerequisite for their advancement to positions such as associate chief physician or chief physician positions[ 6] . These governmental initiatives aim to ameliorate the health-care conditions in remote areas, bridge the gap in healthcare resources distribution, and promote the medical services in rural areas through the policy of urban-rural medical counterpart support. Effective policies can not only narrow the gap in public health-care services between urban and rural areas and improve the quality of medical services in towns and townships, but also promote urban integration and address the issues of imbalanced and inadequate development in our country. While the reality often falls short of expectations, and the implementation of such policies frequently resembles more of a political mission than a substantive developmental effort[ 7] . The potential impact of a policy aiming at advancing health equity depends both on the design and its implementation, requiring ongoing evaluation and stakeholder engagement[ 8] . There are many roles in policy implementation, which make it hard to harmonize relations among various stakeholders with different social psychological needs[ 9] . Successful policy implementation needs collaborative behaviors of frontline agencies[ 10] . Taking Beijing as an example, doctors and managers of both urban and rural hospitals encountered multiple problems during the policy implementation process. Existing articles on urban-rural medical counterpart support mostly consist of experience summaries and discussions, lacking in-depth scientific and theoretical analysis [ 7 ] . There is relative scarcity of research from the perspective of policy implementation on the effects and influencing factors of urban-rural medical counterpart support, and academia’s understanding of China's urban-rural health-care counterpart support lags behind its practical development. Based on the aforementioned issues, this study aims to address the following questions: From the perspective of policy implementation, how can we identify the influencing factors that affect the effectiveness of counterpart support policies? How do these factors interact to form various pathways that impact policy effectiveness? As policy implementation progresses and the counterpart support initiatives advance, have the main factors influencing policy evolved over time? To address these questions, this paper takes the policy of urban doctors serving in rural grassroots in Beijing as a case study. It constructs an analytical framework for supporting urban and rural medical service personnel, grounded in the perspective of policy implementation analysis. Through on-site interviews and questionnaires with counterpart support hospitals, the study examines 13 pairs of counterpart support cases over three consecutive years. Using the method of fuzzy set qualitative comparative analysis, it delves into the influencing factors and implementation pathways affecting the effectiveness of the policy regarding urban doctors' service in rural areas. The study aims to provide a theoretical basis for further refining and implementing policies for the allocation of medical resources. Furthermore, it seeks to serve as a valuable reference for urban-rural counterpart support endeavors in other regions. 2. Literature review and theoretical analysis (1) Literature review The counterpart support framework represents a collaborative governance mechanism under the auspices of strong coordination from national authorities, generally led by the central government with the active involvement from local authorities[ 11] . The supporting entities mainly provide assistance through personnel and technical deployment, mobile medical services, remote demonstrations and training programs, adoption of new technologies and initiatives, and the professional development of healthcare practitioners[ 12] . The evolution of China's counterpart support system is influenced by various factors, such as macro institutional structure, actor beliefs and strategies, critical junctures, and institutional ambiguity[ 13] . Counterpart support initially rooted in administrative mobilization, but later transforms into a compound value rationality[ 14] . Medical counterpart support has played a crucial role in addressing the problem of regional imbalanced development and responding to sudden events, especially in achieving remarkable results in the prevention and control of the COVID-19 epidemic [ 2 ] . However, within the realm of daily urban-rural medical counterpart support, evaluations of existing policies' implementation effectiveness diverge into two distinct perspectives. Some studies posit that urban-rural counterpart support has facilitated rural residents' access to medical treatment[ 15] , improved the service level of grassroots medical institutions[ 16] , and strengthened the construction of county-level hospitals[ 17] . In Beijing, the medical counterpart support improved rural hospitals in terms of medical safety, and capacity to treat emergency cases and more diverse illnesses[ 18] . However, other research underscores challenges such as heavy reliance on local government funding, leading to an increased local financial burden[ 19] , and impediments in executing policy aimed at transferring doctors from higher-tier hospitals[ 20] . Furthermore, the human resources of medical staff sent by urban hospitals to the counterpart support hospitals have not been effectively and reasonably utilized, and it has caused tension in personnel allocation within the assisted hospitals[ 21] . Research of medical counterpart support mainly revolves around five aspects: the implementation carriers of counterpart support, talent management strategies for counterpart support, related policies governing counterpart support, analysis of the current situation of counterpart support, and case studies illustrating effective counterpart support[ 22] . Within the academic discourse, there lacks consensus on the criteria for evaluating the efficacy of medical counterpart support. Some scholars advocate for an evaluation framework encompassing four dimensions: medical services provision, quality assurance and safety measures, sustainable development, and societal benefits[ 23] . Conversely, others propose a comprehensive evaluation framework for urban-rural hospital counterpart support, comprising five indicators: health-care resource inputs, medical service levels, hospital management, operation management, and social benefits[ 24] .Distinct characteristics and dominant factors emerge across different stages of development, with research themes highly correlated with policy dynamics and grassroots needs[ 25] . Studying medical counterpart support policies through the lens of policy tools unveils stage-specific characteristics. With a diminishing reliance on authoritative and incentive-based policy tools, there is a concomitant rise in the utiliztion of capacity-building policy tools increases. Symbolic admonishment policy tools exhibit relative stability over time [ 26] . In summary, existing literature has analyzed and discussed medical counterpart support policies from multiple perspectives. Nonetheless, the research predominantly leans on empirical generalization and lessons gleaned from past experiences. Relatively few studies discussed on the specific factors influencing policy outcomes in medical counterpart support. There is a lack of research on the implementation path of the composite effect of multiple influencing factors, and exploration of the changes in influencing factors over different time periods is relatively rare. (2) Analytical framework This study adopts the Van Horn-Van Meter model as the theoretical framework for analyzing the policy of sending urban doctors to serve in the rural hospitals (UDSR). The selection of the theoretical model is because that the policy of UDSR is formulated by Beijing Municipal Government in accordance with the requirement of central government. The formulation and implementation of this policy conform to the administrative hierarchical characteristics of the multi-level system and represent a typical top-down approach. The Van Horn-Van Meter model is one of the typical models for policy implementation systems and provides strong guidance for analyzing the grassroots implementation process of policies[ 27] . According to the Horn-Mitt policy implementation model and the practical situation of the UDSR policy, there are six influencing factors. (i) Policy Standards and Goals Policy standards and goals are crucial determinants influencing the efficacy of policy implementation. Following the promulgation of the policy regarding urban doctors serving in rural areas in Beijing, clear assessment indicators were provided for supporting hospitals in each annual evaluation, with the aim of promoting the execution of national rural revitalization work [ 6 ][28] . In the annual assessment of urban hospitals’ support for rural areas, the superior department evaluates the hospital's paired support endeavors using 12 evaluation indicators. These criteria encompass various facets such as the quantity and duration of supporting personnel, the volume of diagnosis and treatment cases, participation in assisted learning activities, completion of surgical cases, demonstration surgeries conducted, consultations for challenging medical cases, academic rounds conduct, health checkups administered, delivery of academic lectures, provision of business training sessions, organization of free clinics, establishment of specialty departments, and the value of donated goods[ 1 ]. (ii) Policy Resources Policy implementation requires the allocation of pertinent resources. In the execution of the UDSR policy in Beijing, the allocation of human and financial resources has been tailored to meet stipulated requirements, ensuring the implementation of the policy and the achievement of its objectives[ 29] . The human resources input in the medical field refers to the urban doctors who participate in rural medical service, while the financial resources are mainly used to subsidize various expenses associated with urban doctors’ service in rural areas. (iii) Implementation Methods Following the clarification of policy goals and evaluation criteria, the implementation of urban doctors to rural service policies involves communication and coordination among multiple organizational institutions and personnel. The first level of communication entails inter-institutional exchanges, such as communication between healthcare institutions and personnel management entities, as well as coordination between urban hospitals and rural hospitals. The second level of communication occurs within institutions, involving interactions between supporting health-care management personnel and various departments, as well as between departments and individual doctors. The processes and outcomes of these information exchanges and transmissions have a significant impact on the effectiveness of policy implementation. (iv) Characteristics of Implementing Institutions The main entities of policy implementation institutions include urban and rural healthcare institutions. These institutions exhibit distinct characteristics, including hospital size, hospital level, medical service capacity, departmental resources, and the configuration of healthcare technical personnel [30] . Because of these varing attributes, different healthcare institutions adopt diverse assistance approaches in urban-rural healthcare counterpart support initiatives [31] . (v) System Environment The system environment of urban doctors serving in rural areas includes several dimensions: the political environment, economic environment, cultural environment, and socio-psychological environment [32] . The political environment primarily relates to the degree of attention from public media to the service of urban doctors in rural areas. This aspect signifies the level of societal awareness and discourse regarding the significance and impact of such initiatives. The economic environment reflects the level of local economic development and the degree of economic incentives provided to urban doctors serving in rural areas. This includes subsidies, financial support mechanisms, and infrastructure investments aimed at facilitating urban-rural healthcare exchanges. The cultural environment encompasses the customs, traditions, and societal norms prevalent in both urban and rural settings. It influences healthcare-seeking behaviors, Cognition of healthcare delivery, and the acceptance of urban doctors within rural communities. The socio-psychologial environment includes the habits, beliefs, and attitudes of various stakeholders, including administrators, doctors, patients, and community members. It encompasses factors such as trust in healthcare providers, Cognition of quality care, and the willingness to engage in collaborative healthcare initiatives. These environmental factors collectively shape the context in which urban doctors serve in rural areas, impacting the success and sustainability of such programs. (vi) Values of policy implementers All aspects of the policy implementation process are dependent on different implementers. In this study, the principal implementers of the policy are managers and doctors in urban and rural hospitals. Significantly divergent perspectives and interest exist among these personnel [33] . The Cognition and preferences of managers and doctors in urban hospitals wield substantial influence over the effectiveness of policy implementation [2] . With "urban doctors serving rural areas" as the focal policy implementation process, this study adopts the Horn-Mitt policy implementation model as a theoretical analysis framework, served as a guiding framework for the theoretical analysis and shown in Figure 1. 3. Data and method (1) Case selection and data collection Since the introduction of relevant policies titled the "Notice on Issues Concerning Urban Doctors Serving at Grassroots Level before Promotion of Professional and Technical Positions" in Beijing, it has become compulsory for level 2 and level 3 public hospitals in the city center to send medical personnel to support medical institutions in remote suburban areas like Mentougou, Fangshan, and Shunyi annually. The case selection in this study adheres to the following criteria: (1) Typicality of the case. Cases were selected where the supporting hospital is located in the core urban area of Beijing, while the recipient rural hospital is located in the outskirts. These cases represent common practices and prevalent issues encountered in the implementation of the UDSR policy. (2) Heterogeneity of the case. Given the disparity in available medical resources and political leverage among hospitals of different levels, cases from hospitals at varying levels were included in the selection process. (3) Scientific and continuous data. To mitigate the influence of unforeseen public health crises like the COVID-19 pandemic in 2020 on the analysis results, data spanning three consecutive years (2017-2019) were collected. The dataset include a total of 13 hospitals, with 6 hospitals of level 2 and 7 hospitals of level 3, amounting to 39 case samples over the three-year period. The data in this study comes from two sources: Firstly, the "Statistical Form of Workload for Urban and Rural Counterpart Support in Beijing", issued by the Beijing Municipal Health Commission and completed by hospitals; Secondly, adhering to the research theoretical framework, data collection was conducted based on the logical structure encompassing policy standards and goals, policy resources, implementation methods, characteristics of implementing institutions, and value orientations of implementers. Due to the subjective and challenging-to-measure nature of environmental factors in the theoretical framework—such as the level of media attention to the policy and the beliefs and attitudes of doctors and patients in urban and rural hospitals—these factors were not included in the questionnaire survey. The questionnaire comprises various types of questions, including basic information questions, multiple-choice questions, fill-in-the-blank questions, multiple-response questions, and subjective questions regarding policy cognition. Additionally, validation questions were designed for subjective questions to ensure effective content acquisition. Details of the questionnaire are shown in the appendix. (2) Research methods The implementation of UDSR policy is a complex process influenced by multiple factors, and the policy effectiveness results from the interplay of these factors. To comprehensively analyze causal conditions and outcome, this study incorporates the qualitative comparative analysis (QCA) method. QCA is particularly suitable for examining complex relationships characterized by "multiple causes for a single outcome," a common scenario in the subject of this study. However, traditional static QCA methods often overlook the temporal dimension and dynamic group evolution problems[ 34] . To address these limitations, this study introduces the concept of "time" into QCA analysis to illuminate the nature, causes, and consequences of policy implementation, thereby mitigating the "temporal blind spot" issue of traditional QCA[ 35] . Time-series QCA[ 36] includes three subclasses: (1) Summary QCA consolidates observations of each case at different time points for calibration and configuration analysis. (2) Fixed-effect QCA calibrates each case separately using their respective means to fix the effects brought by each individual case. (3) Temporal-difference QCA focuses on the changes between the beginning and end of the observation period (or other specific time points) for each case and calibrates the differences to explain the fluctuations in the outcome variable [ 35 ] . The selection of fsQCA is mainly based on the following reasons: Firstly, the outcomes of policy implementation in this study are not binary variables (0 or 1), but rather ordered categorical variables with multiple levels. And fsQCA is well-suited for analyzing such ordinal outcomes. Secondly, this study focuses on a relatively small sample size of 13 hospitals, with a total of 39 samples over three consecutive years. And fsQCA excels in analyzing small-sample cases. Third, the multi-period QCA method can effectively address the temporal (sequential), addressing a limitation often encountered by traditional QCA approaches. Moreover, considering the limited number of secondary and tertiary hospitals participating in the paired support work in X district of Beijing, the QCA software is used to simplify the analysis by setting the consistency threshold to 0.8 [37] . Following the selection of the outcome variable, the configuration analysis yields three types of solutions: complex solutions, intermediate solutions, and simple solutions. The intermediate solution with reasonable evidence and moderate complexity is usually the first choice for reporting and interpretation in QCA research [ 31 ] . Therefore, this study selects the intermediate solution to explain the implementation effectiveness of the UDSR policy. By reporting the intermediate solutions and integrating them with the simple solutions, the core conditions and marginal conditions can be discerned effectively. Boolean minimization techniques were applied to obtain the configuration outcomes. Utilizing Fiss's (2011) methodological framework for classifying conditions, we distinguish between core and marginal conditions.The preceding conditions that simultaneously appear in the parsimonious solution and intermediate solution are defined as core conditions, while the conditions that appear in the intermediate solution but are excluded in the parsimonious solution are defined as marginal conditions[ 38] . This approach enables a nuanced understanding of the factors influencing policy outcomes. All the data were encoded and analyzed using the QCA software. The 75%, 50%, and 25% quantiles were employed to represent belonging point, crossover point, and non-belonging point, respectively, transforming them into fuzzy set membership scores. (3) Variable coding Outcome Variable: The evaluation of policy effectiveness in this study revolves around technological inputs as the outcome variable, aligning with the overarching goal of the UDSR policy. The cumulative total of various technological inputs is used to measure the outcome variable. Explanatory Variables: Corresponding to the six aspects of the policy implementation model, this study selects explanatory variables. Considering the minimal disparities in the institutional background of policy implementation among different organizations in Beijing, the following factors have been chosen to align with the targeted support program: policy standards is represented by the number of matched-support projects. Policy resources is indicated by the diversification of subsidy funding sources. Implementation methods are assessed based on the smoothness of communication channels during the policy execution process. Characteristics of implementing institutions is evaluated by the professional alignment of doctors deployed to rural areas, and their prescription authority. Value orientation of implementer is measured through policy awareness. The measurement methods for these specific indicators are presented in Table 1. Table 1 Variable setting and coding based on policy implementation model Variables of Policy implementation model evaluating indicator Measurement of variables Outcome variable Improvement of rural medical technology The total count encompasses a range of educational and operational metrics, including: Training sessions focused on diagnostic and treatment technologies. Instances of outpatient diagnostic and treatment teachings. Surgical procedure demonstrations conducted. Consultations held for complex and challenging medical conditions. Teaching rounds facilitated to enhance clinical knowledge. Academic lectures delivered to broaden medical understanding. Workshops and trainings provided by professional business trainers. These cumulative figures reflect the comprehensive educational efforts and service capacities of the medical institution in question. Policy Standards and Objectives Number of counterpart support projects Accumulation of the number of counterpart support projects provided by urban medical institutions. Policy resources Diversification of subsidy sources If both the superior department and the urban hospital pay the subsidy, it is recorded as 1. If only one of the departments pays the subsidy, it is recorded as 0.5 If neither department pays the subsidy, it is recorded as 0. Mode of implementation Whether the communication in the policy implementation prosess is smooth Smooth communication channels, indicating effective and unhindered communication between relevant stakeholders, is recorded as 1. Otherwise, it is recorded as 0. Implementers’ features The expertise of urban doctors matching the needs of rural hospitals The count of individuals without counterparts is 1, while a handful of professional counterparts are rated as 2. Some professional counterparts are rated as 3, the majority of professional counterparts are rated as 4, and all professional counterparts are rated as 5. Urban doctors’ prescription right in rural hospital When urban doctors have prescription rights, it is recorded as 1; otherwise, it is recorded as 0. Value orientation Hospital department managers ' awareness of the policy Based on the subjective question and the validation question, the degree of cognition is scored, with 0 indicating low policy awareness and attitude, and increasing values reflecting higher levels of policy cognition and attitude. 4. Results and Discussion The analysis focused on examining the necessity of individual conditions and the sufficiency of condition combinations. (1) Necessity Test of Single Factor Conduct a univariate analysis to determine the necessary condition for all variables, with the findings shown in Table 2. Based on Ragin's criterion which posits that consistency must exceed 0.9 to be deemed a necessary condition for an outcome[ 39] , none of the six individual variables exhibit consistency above this threshold. This suggests that no single variable could sufficiently account for the advancement of rural medical technology. However, the consistency values for the number of support projects, the number of subsidy funding departments, and the prescription authority of doctors deployed to rural areas exceeded the threshold of 0.8. This indicates that these three variables play important roles in improving rural hospital technology, yet they do not constitute sufficient conditions for the desired outcome. Hence, a more nuanced exploration of variable interactions is imperative to gain a deeper understanding. A synergistic combination of multiple factors is essential to achieve the goal of UDSR policy. Table 2 Necessity analysis of individual condition variable Variable consistency coverage Variable consistency coverage Number of Support Projects 0.817 0.803 ~Number of Support Projects 0.294 0.288 professional matching 0.629 0.614 ~professional matching 0.496 0.490 policy comprehension 0.782 0.802 ~policy comprehension 0.325 0.306 diversification of subsidy sources 0.864 0.580 ~diversification of subsidy sources 0.194 0.354 communication channel 0.769 0.566 ~communication channel 0.231 0.339 prescription right 0.825 0.607 ~prescription right 0.175 0.258 Note: The symbol “~” stands for “absence of”. (2) Sufficiency Analysis of Condition Combinations Table 3 reports the result of the conditions necessary for the effectiveness of the UDSR policy and emphasizes the importance of condition combinations and the need for further research. The overall consistency score was 0.972, exceeding the threshold of 0.75, indicating a strong alignment between the identified condition configurations and the effectiveness of the policy. This suggests that these configurations are likely necessary conditions for achieving the policy's outcomes. The overall coverage rate of 0.682 indicates that the condition configurations explain approximately two-thirds of the cases. This means that about 68.2% of the policy's effectiveness can be accounted for by these configurations. "Number of Support Projects," "Professional Matching," "Diversification of Subsidy Sources," and "Prescription Rights" appear in multiple configurations, suggesting they may be core conditions for achieving policy effectiveness. "Communication Channel" only appears in configurations 3 and 4, while "Policy Comprehension" is present in configurations 2 and 5. This indicates they may be peripheral conditions, influencing policy outcomes but perhaps not being the primary drivers. Configuration 2 has the highest original coverage (0.416), indicating it is the most common configuration. However, it also has the highest unique coverage (0.317), suggesting that this combination alone can explain a substantial portion of the policy's effectiveness. No single condition can alone explain most of the policy's effectiveness, suggesting that combinations of conditions are crucial for achieving the desired outcomes of the policy. Although certain conditions appear in multiple configurations, further qualitative or quantitative research may be needed to gain a deeper understanding of how these conditions interact and specifically impact policy effectiveness. Table 3 The condition configuration of the effect of the UDSR policy Conditional configuration 1 2 3 4 5 Number of Support Projects ● ● ⊗ ⊗ ● professional matching • ⊗ • ⊗ policy comprehension ● ● ⊗ ● ⊗ diversification of subsidy sources ● ● ⊗ ● communication channel ● ● ⊗ ⊗ ● prescription right ⊗ ● ● ● ● Original coverage 0.127 0.416 0.054 0.052 0.132 Unique coverage 0.127 0.317 0.054 0.052 0.033 Overall consistency 0.972 Overall Coverage 0.682 Note: ' ● ' indicates that the core condition exists, ' • ' indicates that the edge condition exists, ' ⊗ ' indicates that the core causal condition is missing, and the condition does not exist, ' ○ ' indicates that the edge causal condition is missing, and ' blank ' indicates that the condition may or may not appear in the configuration.[ 40][41] (3) Implementation Pathway of Factors Influencing USDR policy Effectiveness Through the analysis of conditional configurations, combined with theoretical research, the mechanism of the UDSR policy can be distilled into four distinct pathways. Pathway 1 is Goal and Cognition driven. Configurations 1 and 2 highlight the significance of ample project support, clear policy comprehension, and efficient communication channels as core variables. While professional matching in Configuration 2 influences technological investment, it's not a central factor. The presence of multiple subsidy sources and clear prescription rights are key but aren't required to coexist. This pathway emphasizes the proactive engagement of urban hospital managers and doctors in policy execution, thus being labeled as Goal and Cognition-driven. Pathway 2 is Professional Matching driven. Since both Configurations 2 and 4 underscore the importance of prescription rights for urban doctors who serve in rural hospital, aligning with professional matching. The heightened awareness positively propels policy implementation. While Configuration 2 shows greater hospital support than Configuration 4, the latter is more universally applicable, making it the Professional Matching-Driven pathway. Pathway 3 is External Funding driven. Prescription rights are a constant core variable across Configurations 2, 3, 4, and 5. Configurations 3 and 5, however, prioritize the number of subsidy-granting departments, a central factor in these setups. The Van Horn and Van Meter model indicates that the diversity of subsidy sources signifies policy resource investment, particularly in covering rural doctor costs. This pathway focuses on the magnitude of external funding, thus being categorized as External Funding-Driven. Pathway 4 is Comprehensive Factors driven. Configuration 2 stands out with project support, prescription rights, communication channels, subsidy source, and professional matching as core or marginal variables. It encapsulates the synergistic effect of multiple factors. Configuration 2, with its core conditions of subsidy sources, policy awareness, project support, communication, and prescription rights, is instrumental in advancing the policy's rural service agenda. The marginal condition of professional matching also exerts an influence. This pathway demonstrates the highest adaptability and stability, making it the most effective in facilitating the policy's successful execution. (4) Robustness test This study employed two methods to test the robustness of the results. Firstly, the analysis was refined by increasing the consistency threshold to 0.85, which streamlined the process and revealed a complete alignment in the configuration of technical inputs and non-technical inputs within the experimental group. Secondly, due to the causal asymmetry principle of qualitative comparative analysis, the conditions for high technical input cannot explain the generation of low technical input. To address this, the study conducted a directional test on the outcome variable, which provided reverse analytical results. Therefore, the configuration results have been substantiated as robust, withstanding scrutiny through both intensified methodological rigor and directional testing. (5) Multi-period comparative analysis This paper delves deeper into the dynamics of factor configurations and their evolution impacting the UDSR policy by conducting a comparative analysis that spans from 2017-2019. With uniformly applied standardized calibration criteria and percentile calibration anchors, this study presents a time-segmented analysis of the UDSR policy configuration. The result showed that there were three configurations for each year, and culminating in a total of nine condition configurations across the three periods. Each individual condition configurations, as well as the overall consistency for each time period, surpass the threshold of 0.75. The overall coverage rates are 0.672, 0.709, and 0.541 for the respective periods, shown in Table 4. In examining the evolution of the condition configurations affecting policy effectiveness over multiple time periods, this study discerns two trajectories: the "transitional trajectory" and the "dominant trajectory", according to Litrico's framework[ 42] . The configuration driven by comprehensive factors appeared consistently across all three periods and had a significant impact on the UDSR policy effectiveness. This is characterized as the dominant trajectory. Conversely, the external funding-driven configuration appeared in 2017 and 2018, but was notably absent in 2019. The practice match-driven configuration emerged in both 2018 and 2019, yet was not detected in 2017. The goal and cognitive-driven configuration only appeared in 2019, signifying a clear transitional trajectory. The efficacy of the UDSR policy's configurational effects evolves through time, illustrating a narrative of change and adaptation. The comprehensive factor-driven configuration epitomizes the dominant trajectory, underscoring that the policy's successful outcomes are contingent upon the synergistic engagement of a multitude of factors. This persistent pattern suggests that a holistic approach, where various elements work in concert, is essential for the policy's sustained impact. On the other hand, the external funding-driven, professional matching-driven, and goal and cognitive-driven configurations embody the "transitional trajectory". These configurations mirror the dynamic nature of policy implementation, highlighting a progression in the policy's operationalization. Initially focused on the distribution of policy resources, the emphasis gradually shifts towards aligning with the organization's inherent attributes. Eventually, the focus matures to encompass the policy's standard objectives and the ideological direction set by the implementers themselves. Table 4 The condition configuration of the effect of the UDSR policy in multiple periods Conditional configuration 2017 2018 2019 configuration 1 configuration 2 configuration 3 configuration 4 configuration 5 configuration 6 configuration 7 configuration 8 configuration 9 All-factor-driven type All-factor-driven type External capital-driven Practice matching driven type All-factor-driven type External capital-driven Practice matching driven type Goal and Cognitive Driven All-factor-driven type Number of Support Projects ● ● ⊗ ⊗ ● ● ⊗ ● ● professional matching ● ● ⊗ ● ● ⊗ ● ⊗ ● policy comprehension ● ● ⊗ ● ● ⊗ ● ● ● diversification of subsidy sources ● ● ● ● ○ • • communication channel ● ● ⊗ ⊗ ● ● ⊗ ● ● prescription right ● ● • • ● ⊗ ● Original coverage 0.966 0.965 1 1 0.973 0.991 1 1 0.995 Unique coverage 0.507 0.500 0.090 0.090 0.530 0.190 0.079 0.157 0.305 Overall consistency 0.083 0.075 0.090 0.090 0.429 0.089 0.079 0.157 0.305 Overall Coverage 0.974 0.977 0.997 Number of Support Projects 0.672 0.709 0.541 Note: ' ● ' indicates that the core condition exists, ' • ' indicates that the edge condition exists, ' ⊗ ' indicates that the core causal condition is missing, the condition does not exist, ' ○ ' indicates that the edge causal condition is missing, and ' blank ' indicates that the condition may or may not appear in the configuration.[ 40 ][ 41 ] 5. Conclusions and policy implications (1) Conclusions The urban-rural medical counterpart support policy aims to promote the integrated development of urban and rural areas, thereby reducing the disparity in medical service quality. Implementing this policy involves multiple complex factors that local governments must navigate to improve rural medical services through the deployment of urban doctors. This study, based on the Horn-Mitt policy implementation framework and enriched with interview questionnaires and fsQCA analysis, explores the factors that influence the policy’s effectiveness and the pathways through which it operates. Firstly, the univariate analysis revealed that no single variable with sufficient consistency to be a necessary condition for the improvement of the UDSR policy effectiveness, highlighting the complexity of the UDSR policy's implementation. However, three variables—the number of support projects, diversification of subsidy sources, and the prescribing rights of urban doctors working in rural hospitals—showed high consistency (>0.8), indicating their significant roles.in influencing the policy effectiveness. Core conditions like number of support projects and prescription rights appear across configurations, while communication channel and policy comprehension seem peripheral. The findings underscore the need for a nuanced understanding of variable interactions and further research to clarify the synergistic impacts on policy outcomes. Secondly, the UDSR policy unfolds through four pathways. Pathway 1, Goal and Cognition-driven, underscores the importance of project support and policy comprehension, with communication enhancing proactive engagement. Pathway 2, Professional Matching-driven, aligns prescription rights with professional standards, driving policy implementation. Pathway 3, External Funding-driven, emphasizes the impact of diverse subsidy sources on resource investment for rural doctors. Finally, Pathway 4, Comprehensive Factors-driven, integrates project support, prescription rights, communication, subsidy diversity, and professional matching as key to the policy's broad adaptability and stability. Finally, our study reveals two distinct evolutionary trajectories in the UDSR policy's effectiveness over time: the "dominant trajectory" and the "transitional trajectory." The dominant trajectory is marked by consistently comprehensive factor-driven configurations across all periods, emphasizing the necessity of a synergistic, multifaceted approach for sustained policy success. In contrast, the transitional trajectory reflects a shift from external funding reliance in earlier years to an emphasis on professional matching and goal alignment in later periods. By 2019, the policy shows a maturation towards internal alignment with organizational attributes and implementers' ideologies, indicating an adaptive evolution in policy implementation. (2) Inspiration An effective counterpart support policy is multifaceted, encompassing a myriad of elements across various dimensions. Well-crafted scientific policy objectives can promote the formation of positive policy outcomes. In terms of policy formulation objectives, urban and rural hospitals should fully understand each other's fundamental conditions and requirements. They should strategically assign relevant medical staff with diverse levels of expertise and specialties, refine the evaluation mechanism, and establish supporting supervision and management mechanisms. Timely feedback and improvement should be provided based on the assessment of work effectiveness. In terms of policy resources, it is essential to fully utilize human resources and with a focus on the appropriate professional placement of medical personnel. Adjusting job requirements based on grassroots needs should be prioritized to minimize talent wastage. Financial allocations should reflect a balanced governmental investment between urban and rural medical facilities. Efforts should be directed towards improving the infrastructure of rural health centers, upgrading medical facilities, and cultivating a better healthcare environment. Practical, user-friendly, and teaching-friendly medical equipment is vital to equip urban doctors with the necessary hardware support when serving in rural settings. In terms of implementation methods, it is important to strengthen communication channels between urban and rural medical institutions. This includes enhancing coordination and communication between different hospital levels and nurturing interactions among medical institution administrators, departmental staff, and individuals.. Exploring effective communication mechanisms and professional exchange platforms is crucial to establish a good communication and feedback loop. Additionally, it's important to grant sufficient prescribing authority to doctors serving in rural areas to ensure the efficacy of their support work. Considering the implementation environment, regional health commissions should fully understand the unique needs of rural areas and adjust policy execution accordingly. By integrating urban and rural medical institutions through a medical referral platform system, the service scope of medical alliances should be expanded to enrich medical resources and enhance comprehensive diagnostic and treatment capabilities. Furthermore, the values and orientation of the implementers are pivotal to its success. It is important to encourage deeper engagement of urban doctors in rural support work, enabling urban doctors to fully understand and address the distinct characteristics and needs of rural medical institutions, thereby actively provide professional assistance suitable for the local context. Declarations Author Contribution Yao Liu （First author）：Data curation；Investigation；Methodology；Software Validation；Writing -original draftChen Lu（corresponding author） ：Conceptualization；Funding acquisition；Methodology Supervision；Writing -original draftJiale Sheng （second author）：Writing-review & editingYurou Zou（third author）：Formal analysis Visualization References Liu, L., & Wang, Y. How Street-Level Bureaucrats Collaborate for Policy Entrepreneurship: Insights From Anti-Poverty Policy Implementation in China[J]. Administration & Society, 2023,55(9), 1791-1818. Wang Yuhao. Counterpart Support Mechanism with Chinese Characteristics : Achievements, Experiences and Values [J]. 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Zhang, Efficiency of medical service systems in the rural areas of Mainland China: a comparative study from 2013 to 2017[J].Public Health, 2019,171:139-147. Zhang Heming, Lu Qingjun, Yin Lin and so on. Bibliometric analysis of research hotspots and evolution trends of counterpart support in public hospitals [J]. Chinese Hospitals, 2023,27(03):63-65.DOI:10.19660/j.issn.1671-0592.2023.03.17. Fu Hang, etc.Analysis on the policy content of partner assistance in urban and rural hospitals based on policy tools in China [J]. Soft Science of Health, 2021, 35(4): 27-32 https://doi.org/10.3969/j.issn.1003-2800.2021.04.007. Chai Baoyong, Zhou Junyu. Research on the Implementation of Rural Grid Management Policy - An Empirical Analysis Based on the Theory of Policy Implementation System [J]. Chinese Public Administration, 2020,No.415(01):114-120.DOI:10.19735/j.issn.1006-0863.2020.01.17. 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The comparison and policy suggestions of five kinds of support modes in our hospital 's counterpart support to primary hospitals [J].Chinese Hospitals, 2015,19(06):36-38. Daniel Romero-Alvarez,Daniel F López-Cevallos,Irene Torres.Doctors for the people? The problematic distribution of rural service doctors in Ecuador[J].Health policy and planning,2023. Xu Mingjiang, Zhang Xinhua, Huang Fen.Research on the incentive mechanism of counterpart support in urban and rural hospitals [J].Health Economics Research, 2014(6):25-28. Du Yunzhou, Li Jiaxin, Liu Qiuchen, and other configuration theories and QCA methods from the perspective of complex dynamics : research progress and future directions [J]. Management World,2021(3):180-197. Munch, Wei Bi.Reflecting on the ' time blind spot ' of QCA method : finding ' time ' for public management research' [J]. Chinese Public Administration, 2023(1):96-104. Hino A. Time-Series QCA: Studying Temporal Change Through Boolean Analysis. 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Footnotes [1] Contents, data from the Beijing Municipal Health Commission documents : ' Attachment. Notice on the submission of relevant data on urban-rural counterpart support ' [2] Based on the author 's interview survey research. Additional Declarations No competing interests reported. Supplementary Files Appendix.doc 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-5371564","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372913942,"identity":"a006d6ae-7e01-40e5-a397-ac894b197126","order_by":0,"name":"Chen Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBACPgYGNiBlQ4IWNoiWNNK1HCZFi0Tyswcfd5xP7G8//oDhRw2DvDlhLWnmhjPP3E6ccSbHgLHnGIPhzgZCWnjOsEnztt1O3MCQw8DA28CQYHCAGC1/284lbuB//oDxL1Fa2HvYpBnbDiRukEgwYCbOFvY2M8netmTjGTfeGByWOSZhuIGQFn5m5mcSP9vsZPv70x8+fFNjI0/QFhQAVCxBivpRMApGwSgYBbgAAHiIOh1Aw/VoAAAAAElFTkSuQmCC","orcid":"","institution":"University of Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Lu","suffix":""},{"id":372913943,"identity":"2b17cec5-e068-49ab-b8d5-7c08304165bc","order_by":1,"name":"Yao Liu","email":"","orcid":"","institution":"Beijing Xicheng District Zhanlan Road Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Liu","suffix":""},{"id":372913944,"identity":"140ff923-ccc6-4654-b788-90d352ae76d9","order_by":2,"name":"Jiale Sheng","email":"","orcid":"","institution":"University of Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jiale","middleName":"","lastName":"Sheng","suffix":""},{"id":372913945,"identity":"f9a33bbb-0681-45e4-a971-c7e5840a3387","order_by":3,"name":"Yurou Zou","email":"","orcid":"","institution":"University of Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yurou","middleName":"","lastName":"Zou","suffix":""}],"badges":[],"createdAt":"2024-11-01 07:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5371564/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5371564/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69463591,"identity":"3d2bdcb1-e5d8-425e-b742-92dc3a73bd57","added_by":"auto","created_at":"2024-11-20 15:10:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130634,"visible":true,"origin":"","legend":"\u003cp\u003eAnalytical framework of UDSR policy in Beijing\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5371564/v1/ae640eb13a066ece8af98560.png"},{"id":69462333,"identity":"8c0c6e5c-68c8-47ab-98b2-e99d76796367","added_by":"auto","created_at":"2024-11-20 15:02:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129346,"visible":true,"origin":"","legend":"\u003cp\u003eInfluencing factors and implementation path of the UDSR policy in Beijing\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5371564/v1/8ead0d7231a84c5b95e7fd8b.png"},{"id":69464766,"identity":"f99166dc-335f-4e3a-8448-3d13623ed201","added_by":"auto","created_at":"2024-11-20 15:26:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":840355,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5371564/v1/58d7bc63-9034-4e0e-a766-8305827d06f2.pdf"},{"id":69462335,"identity":"2842f778-d7df-413b-b395-c656fb61a606","added_by":"auto","created_at":"2024-11-20 15:02:54","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":54784,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.doc","url":"https://assets-eu.researchsquare.com/files/rs-5371564/v1/8138d06786eab69f173e6975.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Key Elements and Implementation Path of the Counterpart Support Policy: A Case Study of Urban Doctors Servicing in Rural Hospitals in Beijing","fulltext":[{"header":"1.\tProposing the problem","content":"\u003cp\u003eIn rapidly developing economies, such as China, India, and Brazil, remarkable economic growth has often been accompanied by pronounced regional development disparities and socioeconomic inequalities. These disparities manifest in various forms, including gaps in income distribution, access to healthcare, education, and infrastructure between urban and rural areas within the same country. The phenomenon of regional development disparities highlights the need for targeted policy interventions to mitigate the socioeconomic inequalities[\u003csup\u003e1]\u003c/sup\u003e. A promising strategy that is gaining attention is the concept of counterpart support, wherein resources, expertise, and services are directed from more developed regions to less developed ones, aiming to narrow the regional disparities. China, with serious regional disparity, usually embrace the implementation of counterpart support policies as an essential governance measures[\u003csup\u003e2]\u003c/sup\u003e. These policies aim to address the imbalances through targeted assistance from more developed regions to less developed ones.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen it turns to the medical services field, which play a pivotal role in enhancing the well-being and happiness of a nation\u0026apos;s populace, a notable gap persists in the allocation of medical resources between urban and rural areas across many countries[\u003csup\u003e3]\u003c/sup\u003e. For example, rural areas often face a deficiency in medical development and emblematic of imbalanced and insufficient progress[\u003csup\u003e4]\u003c/sup\u003e. In addressing the healthcare challenges prevalent in rural areas, Chinese central government issuses the \u0026quot;Decision of the Central Committee of the Communist Party of China and the State Council on Further Strengthening Rural Health Work\u0026quot;, explicitly proposes increasing support for farmers and agriculture through health programs and poverty alleviation efforts, mandating the participation of large and medium-sized medical institutions in cities and the military in the \u0026quot;counterpart support\u0026quot; program[\u003csup\u003e5]\u003c/sup\u003e. In alignment with the directives of the central government, local governments have initiated various measures. In Beijing, for instance, the Municipal Health Bureau and the Municipal Personnel Bureau jointly issued a directive titled \u0026quot;Notice on Relevant Issues Concerning Urban Doctors Serving at Grassroots Medical Institutions before Being Promoted to Professional and Technical Positions.\u0026quot; This policy strictly regulates the tenure of urban doctors\u0026apos; service in remote towns and townships, as a prerequisite for their advancement to positions such as associate chief physician or chief physician positions[\u003csup\u003e6]\u003c/sup\u003e. These governmental initiatives aim to ameliorate the health-care conditions in remote areas, bridge the gap in healthcare resources distribution, and promote the medical services in rural areas through the policy of urban-rural medical counterpart support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEffective policies can not only narrow the gap in public health-care services between urban and rural areas and improve the quality of medical services in towns and townships, but also promote urban integration and address the issues of imbalanced and inadequate development in our country. While the reality often falls short of expectations, and the implementation of such policies frequently resembles more of a political mission than a substantive developmental effort[\u003csup\u003e7]\u003c/sup\u003e. The potential impact of a policy aiming at advancing health equity depends both on the design and its\u0026nbsp;implementation, requiring ongoing evaluation and stakeholder engagement[\u003csup\u003e8]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThere are many roles in policy implementation, which make it hard to harmonize relations among various stakeholders with different social psychological needs[\u003csup\u003e9]\u003c/sup\u003e. Successful policy implementation needs collaborative behaviors of frontline agencies[\u003csup\u003e10]\u003c/sup\u003e. Taking Beijing as an example, doctors and managers of both urban and rural hospitals encountered multiple problems during the policy implementation process. Existing articles on urban-rural medical counterpart support mostly consist of experience summaries and discussions, lacking in-depth scientific and theoretical analysis\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e7\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. There is relative scarcity of research from the perspective of policy implementation on the effects and influencing factors of urban-rural medical counterpart support, and academia\u0026rsquo;s understanding of China\u0026apos;s urban-rural health-care counterpart support lags behind its practical development.\u003c/p\u003e\n\u003cp\u003eBased on the aforementioned issues, this study aims to address the following questions: From the perspective of policy implementation, how can we identify the influencing factors that affect the effectiveness of counterpart support policies? How do these factors interact to form various pathways that impact policy effectiveness? As policy implementation progresses and the counterpart support initiatives advance, have the main factors influencing policy evolved over time?\u003c/p\u003e\n\u003cp\u003eTo address these questions, this paper takes the policy of urban doctors serving in rural grassroots in Beijing as a case study. It constructs an analytical framework for supporting urban and rural medical service personnel, grounded in the perspective of policy implementation analysis. Through on-site interviews and questionnaires with counterpart support hospitals, the study examines 13 pairs of counterpart support cases over three consecutive years. Using the method of fuzzy set qualitative comparative analysis, it delves into the influencing factors and implementation pathways affecting the effectiveness of the policy regarding urban doctors\u0026apos; service in rural areas. The study aims to provide a theoretical basis for further refining and implementing policies for the allocation of medical resources. Furthermore, it seeks to serve as a valuable reference for urban-rural counterpart support endeavors in other regions.\u003c/p\u003e"},{"header":"2.\tLiterature review and theoretical analysis ","content":"\u003cp\u003e\u003cstrong\u003e(1) Literature review\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe counterpart support framework represents a collaborative governance mechanism under the auspices of strong coordination from national authorities, generally led by the central government with the active involvement from local authorities[\u003csup\u003e11]\u003c/sup\u003e. The supporting entities mainly provide assistance through personnel and technical deployment, mobile medical services, remote demonstrations and training programs, adoption of new technologies and initiatives, and the professional development of healthcare practitioners[\u003csup\u003e12]\u003c/sup\u003e.\u0026nbsp;The evolution of China\u0026apos;s counterpart support system is influenced by various factors, such as macro institutional structure, actor beliefs and strategies, critical junctures, and institutional ambiguity[\u003csup\u003e13]\u003c/sup\u003e. Counterpart support initially rooted in administrative mobilization, but later transforms into a compound value rationality[\u003csup\u003e14]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMedical counterpart support has played a crucial role in addressing the problem of regional imbalanced development and responding to sudden events, especially in achieving remarkable results in the prevention and control of the COVID-19 epidemic\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. However, within the realm of daily urban-rural medical counterpart support, evaluations of existing policies\u0026apos; implementation effectiveness diverge into two distinct perspectives. Some studies posit that urban-rural counterpart support has facilitated rural residents\u0026apos; access to medical treatment[\u003csup\u003e15]\u003c/sup\u003e, improved the service level of grassroots medical institutions[\u003csup\u003e16]\u003c/sup\u003e, and strengthened the construction of county-level hospitals[\u003csup\u003e17]\u003c/sup\u003e. In Beijing, the medical counterpart support improved rural hospitals in terms of medical safety, and capacity to treat emergency cases and more diverse illnesses[\u003csup\u003e18]\u003c/sup\u003e. However, other research underscores challenges such as heavy reliance on local government funding, leading to an increased local financial burden[\u003csup\u003e19]\u003c/sup\u003e, and impediments in executing policy aimed at transferring doctors from higher-tier hospitals[\u003csup\u003e20]\u003c/sup\u003e. Furthermore, the human resources of medical staff sent by urban hospitals to the counterpart support hospitals have not been effectively and reasonably utilized, and it has caused tension in personnel allocation within the assisted hospitals[\u003csup\u003e21]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eResearch of medical counterpart support mainly revolves around five aspects: the implementation carriers of counterpart support, talent management strategies for counterpart support, related policies governing counterpart support, analysis of the current situation of counterpart support, and case studies illustrating effective counterpart support[\u003csup\u003e22]\u003c/sup\u003e. Within the academic discourse, there lacks consensus on the criteria for evaluating the efficacy of medical counterpart support. Some scholars advocate for an evaluation framework encompassing four dimensions: medical services provision, quality assurance and safety measures, sustainable development, and societal benefits[\u003csup\u003e23]\u003c/sup\u003e. Conversely, others propose a comprehensive evaluation framework for urban-rural hospital counterpart support, comprising five indicators: health-care resource inputs, medical service levels, hospital management, operation management, and social benefits[\u003csup\u003e24]\u003c/sup\u003e.Distinct characteristics and dominant factors emerge across different stages of development, with research themes highly correlated with policy dynamics and grassroots needs[\u003csup\u003e25]\u003c/sup\u003e. Studying medical counterpart support policies through the lens of policy tools unveils stage-specific characteristics. With a diminishing reliance on authoritative and incentive-based policy tools, there is a concomitant rise in the utiliztion of capacity-building policy tools increases. Symbolic admonishment policy tools exhibit relative stability over time [\u003csup\u003e26]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn summary, existing literature has analyzed and discussed medical counterpart support policies from multiple perspectives. Nonetheless, the research predominantly leans on empirical generalization and lessons gleaned from past experiences. \u0026nbsp;Relatively few studies discussed on the specific factors influencing policy outcomes in medical counterpart support. There is a lack of research on the implementation path of the composite effect of multiple influencing factors, and exploration of the changes in influencing factors over different time periods is relatively rare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(2) Analytical framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopts the Van Horn-Van Meter model as the theoretical framework for analyzing the policy of sending urban doctors to serve in the rural hospitals (UDSR). The selection of the theoretical model is because that the policy of UDSR is formulated by Beijing Municipal Government in accordance with the requirement of \u0026nbsp;central government. The formulation and implementation of this policy conform to the administrative hierarchical characteristics of the multi-level system and represent a typical top-down approach. The Van Horn-Van Meter model is one of the typical models for policy implementation systems and provides strong guidance for analyzing the grassroots implementation process of policies[\u003csup\u003e27]\u003c/sup\u003e. According to the Horn-Mitt policy implementation model and the practical situation of the UDSR policy, there are six influencing factors.\u003c/p\u003e\n\u003cp\u003e(i) Policy Standards and Goals\u003c/p\u003e\n\u003cp\u003ePolicy standards and goals are crucial determinants influencing the efficacy of policy implementation. Following the promulgation\u0026nbsp;of\u0026nbsp;the policy\u0026nbsp;regarding\u0026nbsp;urban doctors serving in rural areas in Beijing, clear assessment indicators were provided for supporting hospitals in each annual evaluation, with the aim of promoting the execution of national rural revitalization work\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e][28]\u003c/sup\u003e. In the annual assessment of \u0026nbsp;urban hospitals\u0026rsquo; support for rural areas, the superior department evaluates the hospital\u0026apos;s paired support endeavors using 12 evaluation indicators. These criteria encompass various facets such as the quantity and duration of supporting personnel, the volume of diagnosis and treatment cases, participation in assisted learning activities, completion of surgical cases, demonstration surgeries conducted, consultations for challenging medical cases, academic rounds conduct, health checkups administered, delivery of academic lectures, provision of business training sessions, organization of free clinics, establishment of specialty departments, and the value of donated goods[\u003csup\u003e1\u003c/sup\u003e].\u003c/p\u003e\n\u003cp\u003e(ii) Policy Resources\u003c/p\u003e\n\u003cp\u003ePolicy implementation requires the allocation of pertinent resources. In the\u0026nbsp;execution\u0026nbsp;of the\u0026nbsp;UDSR\u0026nbsp;policy in Beijing, the\u0026nbsp;allocation\u0026nbsp;of human and financial resources has been tailored to\u0026nbsp;meet\u0026nbsp;stipulated requirements, ensuring\u0026nbsp;the implementation of the policy and the achievement of its objectives[\u003csup\u003e29]\u003c/sup\u003e. The human resources\u0026nbsp;input\u0026nbsp;in the medical field refers to the urban doctors who participate in rural\u0026nbsp;medical\u0026nbsp;service, while the financial resources are mainly used to subsidize various expenses associated with urban doctors\u0026rsquo;\u0026nbsp;service\u0026nbsp;in rural areas.\u003c/p\u003e\n\u003cp\u003e(iii) Implementation Methods\u003c/p\u003e\n\u003cp\u003eFollowing the clarification of policy goals and evaluation criteria, the implementation of urban doctors to rural service policies involves communication and coordination among multiple organizational institutions and personnel. The first level of communication\u0026nbsp;entails inter-institutional exchanges, such as communication between\u0026nbsp;healthcare\u0026nbsp;institutions and personnel\u0026nbsp;management entities,\u0026nbsp;as well as\u0026nbsp;coordination between urban hospitals and rural hospitals. The second level of communication occurs within institutions, involving interactions between supporting\u0026nbsp;health-care\u0026nbsp;management personnel and various departments,\u0026nbsp;as well as\u0026nbsp;between departments and\u0026nbsp;individual\u0026nbsp;doctors. The processes and\u0026nbsp;outcomes of these information exchanges and transmissions have a significant impact on the effectiveness of policy implementation.\u003c/p\u003e\n\u003cp\u003e(iv) Characteristics of Implementing Institutions\u003c/p\u003e\n\u003cp\u003eThe main entities of policy implementation institutions include urban and rural\u0026nbsp;healthcare\u0026nbsp;institutions. These institutions exhibit distinct characteristics,\u0026nbsp;including\u0026nbsp;hospital size, hospital level, medical service capacity, departmental resources, and \u0026nbsp;the\u0026nbsp;configuration\u0026nbsp;of healthcare\u0026nbsp;technical personnel\u003csup\u003e[30]\u003c/sup\u003e.\u0026nbsp;Because of these varing attributes, different\u0026nbsp;healthcare\u0026nbsp;institutions\u0026nbsp;adopt\u0026nbsp;diverse\u0026nbsp;assistance approaches in urban-rural\u0026nbsp;healthcare\u0026nbsp;counterpart support\u0026nbsp;initiatives\u003csup\u003e[31]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(v) System Environment\u003c/p\u003e\n\u003cp\u003eThe system environment of urban doctors serving in rural areas includes\u0026nbsp;several dimensions:\u0026nbsp;the political environment, economic environment, cultural environment, and socio-psychological environment\u003csup\u003e[32]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe political environment\u0026nbsp;primarily relates to\u0026nbsp;the degree of\u0026nbsp;attention\u0026nbsp;from\u0026nbsp;public media\u0026nbsp;to the service of urban doctors in rural areas.\u0026nbsp;This aspect signifies the level of societal awareness and discourse regarding the significance and impact of such initiatives.\u003c/p\u003e\n\u003cp\u003eThe economic environment reflects the level of local economic development and the degree of economic incentives provided\u0026nbsp;to\u0026nbsp;urban doctors serving\u0026nbsp;in rural areas. This\u0026nbsp;includes subsidies, financial support mechanisms, and infrastructure investments aimed at facilitating urban-rural healthcare exchanges.\u003c/p\u003e\n\u003cp\u003eThe cultural environment encompasses the customs, traditions, and societal norms prevalent in both urban and rural settings. It influences healthcare-seeking behaviors,\u0026nbsp;Cognition\u0026nbsp;of healthcare delivery, and the acceptance of urban doctors within rural communities.\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;socio-psychologial\u0026nbsp;environment includes the habits, beliefs, and attitudes of various stakeholders, including\u0026nbsp;administrators,\u0026nbsp;doctors, patients, and community members. It encompasses factors such as trust in healthcare providers,\u0026nbsp;Cognition\u0026nbsp;of quality care, and the willingness to engage in collaborative healthcare initiatives.\u003c/p\u003e\n\u003cp\u003eThese environmental factors collectively shape the context in which urban doctors serve in rural areas, impacting the success and sustainability of such programs.\u003c/p\u003e\n\u003cp\u003e(vi) Values of policy implementers\u003c/p\u003e\n\u003cp\u003eAll aspects of the policy implementation process are dependent on different implementers. In this study, the principal implementers of the policy are managers and doctors in urban and rural hospitals. Significantly divergent perspectives and interest exist among these personnel\u003csup\u003e[33]\u003c/sup\u003e. The Cognition and preferences of managers and doctors in urban hospitals wield substantial influence over the effectiveness of policy implementation\u003csup\u003e[2]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWith \u0026quot;urban doctors serving rural areas\u0026quot; as the focal policy implementation process, this study adopts the Horn-Mitt policy implementation model as a theoretical analysis framework, served as a guiding framework for the theoretical analysis and shown in Figure 1.\u003c/p\u003e"},{"header":"3.\tData and method","content":"\u003cp\u003e\u003cstrong\u003e(1) Case selection and data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince the introduction of relevant policies titled the \u0026quot;Notice on Issues Concerning Urban Doctors Serving at Grassroots Level before Promotion of Professional and Technical Positions\u0026quot; in Beijing, it has become compulsory for level 2 and level 3 public hospitals in the city center to send medical personnel to support medical institutions in remote suburban areas like Mentougou, Fangshan, and Shunyi \u0026nbsp;annually. The case selection in this study adheres to the following criteria: (1) Typicality of the case. Cases were selected where the supporting hospital is located in the core urban area of Beijing, while the recipient rural hospital is located in the outskirts. These cases represent common practices and prevalent issues encountered in the implementation of the UDSR policy. (2) Heterogeneity of the case.\u0026nbsp;Given the disparity in available medical resources and political leverage among\u0026nbsp;hospitals of different levels, cases from hospitals at varying levels were included in the selection process. (3) Scientific and continuous data. To mitigate the influence of unforeseen public health crises like the COVID-19 pandemic in 2020 on the analysis results, data spanning three consecutive years (2017-2019) were collected. The dataset include a total of 13 hospitals, with 6 hospitals of level 2 and 7 hospitals of level 3, amounting to 39 case samples over the three-year period.\u003c/p\u003e\n\u003cp\u003eThe data in this study comes from two sources: Firstly, the \u0026quot;Statistical Form of Workload for Urban and Rural Counterpart Support in Beijing\u0026quot;, issued by the Beijing Municipal Health Commission and completed by hospitals; Secondly, adhering to the research theoretical framework, data collection was conducted based on the logical structure encompassing policy standards and goals, policy resources, implementation methods, characteristics of implementing institutions, and value orientations of implementers. Due to the subjective and challenging-to-measure nature of \u0026nbsp;environmental factors in the theoretical framework\u0026mdash;such as the level of media attention to the policy and the beliefs and attitudes of doctors and patients in urban and rural hospitals\u0026mdash;these factors were not included in the questionnaire survey. The questionnaire comprises various types of questions, including basic information questions, multiple-choice questions, fill-in-the-blank questions, multiple-response questions, and subjective questions regarding policy cognition. Additionally, validation questions were designed for subjective questions to ensure effective content acquisition. Details of the questionnaire are shown in the appendix.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(2) Research methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe implementation of UDSR policy is a complex process influenced by multiple factors, and the policy effectiveness results from the interplay of these factors. To comprehensively analyze causal conditions and outcome, this study incorporates the qualitative comparative analysis (QCA) method. QCA is particularly suitable for examining complex relationships characterized by \u0026quot;multiple causes for a single outcome,\u0026quot; a common scenario in the subject of this study. However, traditional static QCA methods often overlook the temporal dimension and dynamic group evolution problems[\u003csup\u003e34]\u003c/sup\u003e. To address these limitations, this study introduces the concept of \u0026quot;time\u0026quot; into QCA analysis to illuminate the nature, causes, and consequences of policy implementation, thereby mitigating the \u0026quot;temporal blind spot\u0026quot; issue of traditional QCA[\u003csup\u003e35]\u003c/sup\u003e. Time-series QCA[\u003csup\u003e36]\u003c/sup\u003e includes three subclasses: (1) Summary QCA consolidates observations of each case at different time points for calibration and configuration analysis. (2) Fixed-effect QCA calibrates each case separately using their respective means to fix the effects brought by each individual case. (3) Temporal-difference QCA focuses on the changes between the beginning and end of the observation period (or other specific time points) for each case and calibrates the differences to explain the fluctuations in the outcome variable\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e35\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe selection of fsQCA is mainly based on the following reasons: Firstly, the outcomes of policy implementation in this study are not binary variables (0 or 1), but rather ordered categorical variables with multiple levels. And\u0026nbsp;fsQCA is well-suited for analyzing such ordinal outcomes. Secondly, this study focuses on a relatively small sample size of 13 hospitals, with a total of 39 samples over three consecutive years. And fsQCA excels in analyzing small-sample cases. Third, the multi-period QCA method can effectively address the temporal (sequential), addressing a limitation often encountered by traditional QCA approaches. Moreover, considering the limited number of secondary and tertiary hospitals participating in the paired support work in X district of Beijing, the QCA software is used to simplify the analysis by setting the consistency threshold to 0.8\u003csup\u003e[37]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing the selection of the outcome variable, the configuration analysis yields three types of solutions: complex solutions, intermediate solutions, and simple solutions. The intermediate solution with reasonable evidence and moderate complexity is usually the first choice for reporting and interpretation in QCA research\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e31\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Therefore, this study selects the intermediate solution to explain the implementation effectiveness of the UDSR policy. By reporting the intermediate solutions and integrating them with the simple solutions, the core conditions and marginal conditions can be discerned effectively.\u0026nbsp;Boolean minimization techniques were applied to obtain the configuration outcomes. Utilizing Fiss\u0026apos;s (2011) methodological framework for classifying conditions, we distinguish between core and marginal conditions.The preceding conditions that simultaneously appear in the parsimonious solution and intermediate solution are defined as core conditions, while the conditions that appear in the intermediate solution but are excluded in the parsimonious solution are defined as marginal conditions[\u003csup\u003e38]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis approach enables a nuanced understanding of the factors influencing policy outcomes. All the data were encoded and analyzed using the QCA software. The 75%, 50%, and 25% quantiles were employed to represent belonging point, crossover point, and non-belonging point, respectively, transforming them into fuzzy set membership scores. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(3) Variable coding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOutcome Variable: The evaluation of policy effectiveness in this study revolves around technological inputs as the outcome variable, aligning with the overarching goal of the UDSR policy. The cumulative total of various technological inputs is used to measure the outcome variable.\u003c/p\u003e\n\u003cp\u003eExplanatory Variables: Corresponding to the six aspects of the policy implementation model, this study selects explanatory variables. Considering the minimal disparities in the institutional background of policy implementation among different organizations in Beijing, the following factors have been chosen to align with the targeted support program: policy standards is represented by the number of matched-support projects. Policy resources is indicated by the diversification of subsidy funding sources. Implementation methods are assessed based on the smoothness of communication channels during the policy execution process. Characteristics of implementing institutions is evaluated by the professional alignment of doctors deployed to rural areas, and their prescription authority. Value orientation of implementer is measured through policy awareness. The measurement methods for these specific indicators are presented in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1 Variable setting and coding based on policy implementation model\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eVariables of Policy implementation model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eevaluating indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eMeasurement of variables\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eOutcome variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eImprovement of rural medical technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eThe total count encompasses a range of educational and operational metrics, including: Training sessions focused on diagnostic and treatment technologies. Instances of outpatient diagnostic and treatment teachings. Surgical procedure demonstrations conducted. Consultations held for complex and challenging medical conditions. Teaching rounds facilitated to enhance clinical knowledge. Academic lectures delivered to broaden medical understanding. Workshops and trainings provided by professional business trainers.\u003c/p\u003e\n \u003cp\u003eThese cumulative figures reflect the comprehensive educational efforts and service capacities of the medical institution in question.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003ePolicy Standards and Objectives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eNumber of counterpart support projects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eAccumulation of the number of counterpart support projects provided by urban medical institutions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003ePolicy resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eDiversification of subsidy sources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eIf both the superior department and the urban hospital pay the subsidy, it is recorded as 1. If only one of the departments pays the subsidy, it is recorded as 0.5 If neither department pays the subsidy, it is recorded as 0.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eMode of implementation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eWhether the communication in the policy implementation prosess is smooth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eSmooth communication channels, indicating effective and unhindered communication between relevant stakeholders, is recorded as 1. Otherwise, it is recorded as 0.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 160px;\"\u003e\n \u003cp\u003eImplementers\u0026rsquo; features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eThe expertise of urban doctors matching the needs of rural hospitals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eThe count of individuals without counterparts is 1, while a handful of professional counterparts are rated as 2. Some professional counterparts are rated as 3, the majority of professional counterparts are rated as 4, and all professional counterparts are rated as 5.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eUrban doctors\u0026rsquo; prescription right in rural hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eWhen urban doctors have prescription rights, it is recorded as 1; otherwise, it is recorded as 0.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eValue orientation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eHospital department managers \u0026apos; awareness of the policy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eBased on the subjective question and the validation question, the degree of cognition is scored, with 0 indicating low policy awareness and attitude, and increasing values reflecting higher levels of policy cognition and attitude.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4.\tResults and Discussion","content":"\u003cp\u003eThe analysis focused on examining the necessity of individual conditions and the sufficiency of condition combinations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(1) Necessity Test of Single Factor\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConduct a univariate analysis to determine the necessary condition for all variables, with the findings shown in Table 2. Based on Ragin\u0026apos;s criterion which posits that consistency must exceed 0.9 to be deemed a necessary condition for an outcome[\u003csup\u003e39]\u003c/sup\u003e, none of the six individual variables exhibit consistency above this threshold. This suggests that no single variable could sufficiently account for the advancement of rural medical technology. However, the consistency values for the number of support projects, the number of subsidy funding departments, and the prescription authority of doctors deployed to rural areas exceeded the threshold of 0.8. This indicates that these three variables play important roles in improving rural hospital technology, yet they do not constitute sufficient conditions for the desired outcome. Hence, a more nuanced exploration of variable interactions is imperative to gain a deeper understanding. A synergistic combination of multiple factors is essential to achieve the goal of UDSR policy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Necessity analysis of\u0026nbsp;individual condition variable\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4545%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6364%;\"\u003e\n \u003cp\u003econsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003ecoverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6364%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4545%;\"\u003e\n \u003cp\u003econsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003ecoverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4545%;\"\u003e\n \u003cp\u003eNumber of Support Projects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6364%;\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6364%;\"\u003e\n \u003cp\u003e~Number of Support Projects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4545%;\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4545%;\"\u003e\n \u003cp\u003eprofessional matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6364%;\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6364%;\"\u003e\n \u003cp\u003e~professional matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4545%;\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4545%;\"\u003e\n \u003cp\u003epolicy comprehension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6364%;\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6364%;\"\u003e\n \u003cp\u003e~policy comprehension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4545%;\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4545%;\"\u003e\n \u003cp\u003ediversification of subsidy sources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6364%;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6364%;\"\u003e\n \u003cp\u003e~diversification of subsidy sources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4545%;\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4545%;\"\u003e\n \u003cp\u003ecommunication channel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6364%;\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6364%;\"\u003e\n \u003cp\u003e~communication channel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4545%;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.4545%;\"\u003e\n \u003cp\u003eprescription right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6364%;\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6364%;\"\u003e\n \u003cp\u003e~prescription right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4545%;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.8182%;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe symbol\u0026nbsp;\u0026ldquo;~\u0026rdquo;\u0026nbsp;stands for\u0026nbsp;\u0026ldquo;absence of\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(2) Sufficiency Analysis of Condition Combinations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 reports the result of the conditions necessary for the effectiveness of the UDSR policy and emphasizes the importance of condition combinations and the need for further research. The overall consistency score was 0.972, exceeding the threshold of 0.75, indicating a strong alignment between the identified condition configurations and the effectiveness of the policy. This suggests that these configurations are likely necessary conditions for achieving the policy\u0026apos;s outcomes. The overall coverage rate of 0.682 indicates that the condition configurations explain approximately two-thirds of the cases. This means that about 68.2% of the policy\u0026apos;s effectiveness can be accounted for by these configurations. \u0026quot;Number of Support Projects,\u0026quot; \u0026quot;Professional Matching,\u0026quot; \u0026quot;Diversification of Subsidy Sources,\u0026quot; and \u0026quot;Prescription Rights\u0026quot; appear in multiple configurations, suggesting they may be core conditions for achieving policy effectiveness.\u003c/p\u003e\n\u003cp\u003e\u0026quot;Communication Channel\u0026quot; only appears in configurations 3 and 4, while \u0026quot;Policy Comprehension\u0026quot; is present in configurations 2 and 5. This indicates they may be peripheral conditions, influencing policy outcomes but perhaps not being the primary drivers. Configuration 2 has the highest original coverage (0.416), indicating it is the most common configuration. However, it also has the highest unique coverage (0.317), suggesting that this combination alone can explain a substantial portion of the policy\u0026apos;s effectiveness. No single condition can alone explain most of the policy\u0026apos;s effectiveness, suggesting that combinations of conditions are crucial for achieving the desired outcomes of the policy. Although certain conditions appear in multiple configurations, further qualitative or quantitative research may be needed to gain a deeper understanding of how these conditions interact and specifically impact policy effectiveness.\u003c/p\u003e\n\u003cp\u003eTable 3 The condition configuration of the effect of the UDSR policy\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eConditional configuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eNumber of Support Projects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eprofessional matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026bull;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026bull;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003epolicy comprehension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ediversification of subsidy sources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ecommunication channel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eprescription right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eOriginal coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.127\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.416\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.054\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.052\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.132\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eUnique coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.127\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.317\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.054\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.052\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.033\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eOverall consistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 427px;\"\u003e\n \u003cp\u003e0.972\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eOverall Coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 427px;\"\u003e\n \u003cp\u003e0.682\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e \u0026apos; ● \u0026apos; indicates that the core condition exists, \u0026apos; \u0026bull; \u0026apos; indicates that the edge condition exists, \u0026apos; \u0026otimes; \u0026apos; indicates that the core causal condition is missing, and the condition does not exist, \u0026apos; ○ \u0026apos; indicates that the edge causal condition is missing, and \u0026apos; blank \u0026apos; indicates that the condition may or may not appear in the configuration.[\u003csup\u003e40][41]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(3) Implementation Pathway of Factors Influencing USDR policy Effectiveness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough the analysis of conditional configurations, combined with theoretical research, the mechanism of the UDSR policy can be distilled into four distinct pathways.\u003c/p\u003e\n\u003cp\u003ePathway 1 is Goal and Cognition driven. Configurations 1 and 2 highlight the significance of ample project support, clear policy comprehension, and efficient communication channels as core variables. While professional matching in Configuration 2 influences technological investment, it\u0026apos;s not a central factor. The presence of multiple subsidy sources and clear prescription rights are key but aren\u0026apos;t required to coexist. This pathway emphasizes the proactive engagement of urban hospital managers and doctors in policy execution, thus being labeled as Goal and Cognition-driven.\u003c/p\u003e\n\u003cp\u003ePathway 2 is Professional\u0026nbsp;Matching driven. Since both Configurations 2 and 4 underscore the importance of prescription rights for urban doctors who serve in rural hospital, aligning with professional matching. The heightened awareness positively propels policy implementation. While Configuration 2 shows greater hospital support than Configuration 4, the latter is more universally applicable, making it the Professional\u0026nbsp;Matching-Driven pathway.\u003c/p\u003e\n\u003cp\u003ePathway 3 is External Funding driven. Prescription rights are a constant core variable across Configurations 2, 3, 4, and 5. Configurations 3 and 5, however, prioritize the number of subsidy-granting departments, a central factor in these setups. The Van Horn and Van Meter model indicates that the diversity of subsidy sources signifies policy resource investment, particularly in covering rural doctor costs. This pathway focuses on the magnitude of external funding, thus being categorized as External Funding-Driven.\u003c/p\u003e\n\u003cp\u003ePathway 4 is Comprehensive Factors driven. Configuration 2 stands out with project support, prescription rights, communication channels, subsidy source, and professional matching as core or marginal variables. It encapsulates the synergistic effect of multiple factors. Configuration 2, with its core conditions of subsidy sources, policy awareness, project support, communication, and prescription rights, is instrumental in advancing the policy\u0026apos;s rural service agenda. The marginal condition of professional matching also exerts an influence. This pathway demonstrates the highest adaptability and stability, making it the most effective in facilitating the policy\u0026apos;s successful execution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(4) Robustness test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed two methods to test the robustness of the results. Firstly, the analysis was refined by increasing the consistency threshold to 0.85, which streamlined the process and revealed a complete alignment in the configuration of technical inputs and non-technical inputs within the experimental group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecondly, due to the causal asymmetry principle of qualitative comparative analysis, the conditions for high technical input cannot explain the generation of low technical input. To address this, the study conducted a directional test on the outcome variable, which provided reverse analytical results. Therefore, the configuration results have been substantiated as robust, withstanding scrutiny through both intensified methodological rigor and directional testing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(5) Multi-period comparative analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper delves deeper into the dynamics of factor configurations and their evolution impacting the UDSR policy by conducting a comparative analysis that spans from 2017-2019. With uniformly applied standardized calibration criteria and percentile calibration anchors, this study presents a time-segmented analysis of the UDSR policy configuration. The result showed that there were three configurations for each year, and culminating in a total of nine condition configurations across the three periods. Each individual condition configurations, as well as the overall consistency for each time period, surpass the threshold of 0.75. The overall coverage rates are 0.672, 0.709, and 0.541 for the respective periods, shown in Table 4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn examining the evolution of the condition configurations affecting policy effectiveness over multiple time periods, this study discerns two trajectories: the \u0026nbsp;\u0026quot;transitional trajectory\u0026quot; and the \u0026quot;dominant trajectory\u0026quot;, according to Litrico\u0026apos;s framework[\u003csup\u003e42]\u003c/sup\u003e. The configuration driven by comprehensive factors appeared consistently across all three periods and had a significant impact on the UDSR policy effectiveness. This is characterized as the dominant trajectory. Conversely, the external funding-driven configuration appeared in 2017 and 2018, but was notably absent in 2019. The practice match-driven configuration emerged in both 2018 and 2019, yet was not detected in 2017. The goal and cognitive-driven configuration only appeared in 2019, signifying a clear transitional trajectory.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe efficacy of the UDSR policy\u0026apos;s configurational effects evolves through time, illustrating a narrative of change and adaptation. The comprehensive factor-driven configuration epitomizes the dominant trajectory, underscoring that the policy\u0026apos;s successful outcomes are contingent upon the synergistic engagement of a multitude of factors. This persistent pattern suggests that a holistic approach, where various elements work in concert, is essential for the policy\u0026apos;s sustained impact.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, the external funding-driven, professional matching-driven, and goal and cognitive-driven configurations embody the \u0026quot;transitional trajectory\u0026quot;. These configurations mirror the dynamic nature of policy implementation, highlighting a progression in the policy\u0026apos;s operationalization. Initially focused on the distribution of policy resources, the emphasis gradually shifts towards aligning with the organization\u0026apos;s inherent attributes. Eventually, the focus matures to encompass the policy\u0026apos;s standard objectives and the ideological direction set by the implementers themselves.\u003c/p\u003e\n\u003cp\u003eTable 4 The condition configuration of the effect of the UDSR policy in multiple periods\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 100px;\"\u003e\n \u003cp\u003eConditional configuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 151px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 154px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 157px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003econfiguration 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003econfiguration 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003econfiguration 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003econfiguration 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003econfiguration 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003econfiguration 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003econfiguration 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003econfiguration 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003econfiguration 9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eAll-factor-driven type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eAll-factor-driven type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eExternal capital-driven\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003ePractice matching driven type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eAll-factor-driven type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eExternal capital-driven\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003ePractice matching driven type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eGoal and Cognitive Driven\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eAll-factor-driven type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eNumber of Support Projects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eprofessional matching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003epolicy comprehension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003ediversification of subsidy sources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e○\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026bull;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026bull;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003ecommunication channel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eprescription right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026bull;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026bull;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eOriginal coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eUnique coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.507\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0.500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.090\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.090\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.530\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.190\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.079\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.157\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.305\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eOverall consistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.083\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0.075\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.090\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.090\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.429\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.089\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.079\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.157\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.305\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eOverall Coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 157px;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eNumber of Support Projects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 154px;\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 157px;\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u0026nbsp; \u0026apos; ● \u0026apos; indicates that the core condition exists, \u0026apos; \u0026bull; \u0026apos; indicates that the edge condition exists, \u0026apos; \u0026otimes; \u0026apos; indicates that the core causal condition is missing, the condition does not exist, \u0026apos; ○ \u0026apos; indicates that the edge causal condition is missing, and \u0026apos; blank \u0026apos; indicates that the condition may or may not appear in the configuration.[\u003csup\u003e40\u003c/sup\u003e\u003csup\u003e][\u003c/sup\u003e\u003csup\u003e41\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e\u003c/p\u003e"},{"header":"5.\tConclusions and policy implications","content":"\u003cp\u003e\u003cstrong\u003e(1) Conclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe urban-rural medical counterpart support policy aims to promote the integrated development of urban and rural areas, thereby reducing the disparity in medical service quality. Implementing this policy involves multiple complex factors that local governments must navigate to improve rural medical services through the deployment of urban doctors. This study, based on the Horn-Mitt policy implementation framework and enriched with interview questionnaires and fsQCA analysis, explores the factors that influence the policy\u0026rsquo;s effectiveness and the pathways through which it operates.\u003c/p\u003e\n\u003cp\u003eFirstly, the univariate analysis revealed that no single variable with sufficient consistency to be a necessary condition for the improvement of the UDSR policy effectiveness, highlighting the complexity of the UDSR policy\u0026apos;s implementation. However, three variables\u0026mdash;the number of support projects, diversification of subsidy sources, and the prescribing rights of urban doctors working in rural hospitals\u0026mdash;showed high consistency (\u0026gt;0.8), indicating their significant roles.in influencing the policy effectiveness. Core conditions like number of support projects and prescription rights appear across configurations, while communication channel and policy comprehension seem peripheral. The findings underscore the need for a nuanced understanding of variable interactions and further research to clarify the synergistic impacts on policy outcomes.\u003c/p\u003e\n\u003cp\u003eSecondly, the UDSR policy unfolds through four pathways. Pathway 1, Goal and Cognition-driven, underscores the importance of project support and policy comprehension, with communication enhancing proactive engagement. Pathway 2, Professional Matching-driven, aligns prescription rights with professional standards, driving policy implementation. Pathway 3, External Funding-driven, emphasizes the impact of diverse subsidy sources on resource investment for rural doctors. Finally, Pathway 4, Comprehensive Factors-driven, integrates project support, prescription rights, communication, subsidy diversity, and professional matching as key to the policy\u0026apos;s broad adaptability and stability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, our study reveals two distinct evolutionary trajectories in the UDSR policy\u0026apos;s effectiveness over time: the \u0026quot;dominant trajectory\u0026quot; and the \u0026quot;transitional trajectory.\u0026quot; The dominant trajectory is marked by consistently comprehensive factor-driven configurations across all periods, emphasizing the necessity of a synergistic, multifaceted approach for sustained policy success. In contrast, the transitional trajectory reflects a shift from external funding reliance in earlier years to an emphasis on professional matching and goal alignment in later periods. By 2019, the policy shows a maturation towards internal alignment with organizational attributes and implementers\u0026apos; ideologies, indicating an adaptive evolution in policy implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(2) Inspiration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn effective counterpart support policy is multifaceted, encompassing a myriad of elements across various dimensions. Well-crafted scientific policy objectives can promote the formation of positive policy outcomes. In terms of policy formulation objectives, urban and rural hospitals should fully understand each other\u0026apos;s fundamental conditions and requirements. They should strategically assign relevant medical staff with diverse levels of expertise and specialties, refine the evaluation mechanism, and establish supporting supervision and management mechanisms. Timely feedback and improvement should be provided based on the assessment of work effectiveness.\u003c/p\u003e\n\u003cp\u003eIn terms of policy resources, it is essential to fully utilize human resources and with a focus on the appropriate professional placement of medical personnel. Adjusting job requirements based on grassroots needs should be prioritized to minimize talent wastage. Financial allocations should reflect a balanced governmental investment between urban and rural medical facilities. Efforts should be directed towards improving the infrastructure of rural health centers, upgrading medical facilities, and cultivating a better healthcare environment. Practical, user-friendly, and teaching-friendly medical equipment is vital to equip urban doctors with the necessary hardware support when serving in rural settings.\u003c/p\u003e\n\u003cp\u003eIn terms of implementation methods, it is important to strengthen communication channels between urban and rural medical institutions. This includes enhancing coordination and communication between different hospital levels and nurturing interactions among medical institution administrators, departmental staff, and individuals.. Exploring effective communication mechanisms and professional exchange platforms is crucial to establish a good communication and feedback loop. Additionally, it\u0026apos;s important to grant sufficient prescribing authority to doctors serving in rural areas to ensure the efficacy of their support work.\u003c/p\u003e\n\u003cp\u003eConsidering the implementation environment, regional health commissions should fully understand the unique needs of rural areas and adjust policy execution accordingly. By integrating urban and rural medical institutions through a medical referral platform system, the service scope of medical alliances should be expanded to enrich medical resources and enhance comprehensive diagnostic and treatment capabilities. Furthermore, the values and orientation of the implementers are pivotal to its success. It is important to encourage deeper engagement of urban doctors in rural support work, enabling urban doctors to fully understand and address the distinct characteristics and needs of rural medical institutions, thereby actively provide professional assistance suitable for the local context.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYao Liu （First author）：Data curation；Investigation；Methodology；Software Validation；Writing -original draftChen Lu（corresponding author） ：Conceptualization；Funding acquisition；Methodology Supervision；Writing -original draftJiale Sheng （second author）：Writing-review \u0026amp; editingYurou Zou（third author）：Formal analysis Visualization\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu, L., \u0026amp; Wang, Y. How Street-Level Bureaucrats Collaborate for Policy Entrepreneurship: Insights From Anti-Poverty Policy Implementation in China[J]. 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Notice on the submission of relevant data on urban-rural counterpart support \u0026apos;\u003c/p\u003e\n\u003cp\u003e[2] Based on the author \u0026apos;s interview survey research.\u003c/p\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":"counterpart support policy, urban doctors servicing in rural hospitals, policy implementation, QCA, influencing factors, implementation path","lastPublishedDoi":"10.21203/rs.3.rs-5371564/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5371564/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCounterpart support policy is a pivotal public management strategy aimed at bridging regional disparities. This paper aims to explore the determinants and execution pathways of this policy, with a focus on enhancing its efficacy. Utilizing the urban doctors serving rural areas (UDSR) policy in Beijing as a case study, the research is grounded in Van Meter and Van Horn\u0026rsquo;s policy implementation framework and employs fsQCA methods to scrutinize counterpart support hospitals. The findings pinpoint three critical factors shaping the policy's impact: the number of support projects, the diversification of subsidy sources, and the prescription rights for urban doctors working in rural medical institutions. Four distinct implementation pathways are identified: Pathway 1 is driven by goals and cognition, Pathway 2 by professional matching, Pathway 3 by external funding, and Pathway 4 by a combination of comprehensive factors. Furthermore, the study traces two evolutionary trajectories for the UDSR policy's effectiveness. The dominant trajectory demonstrates enduring success through a comprehensive factor-driven approach. In contrast, the transitional trajectory showcases an evolution from initial dependency on external funding to a phase of professional and goal alignment, culminating in internal alignment with organizational attributes and the ideologies of implementers, signifying an adaptive evolution in policy execution.\u003c/p\u003e","manuscriptTitle":"Key Elements and Implementation Path of the Counterpart Support Policy: A Case Study of Urban Doctors Servicing in Rural Hospitals in Beijing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 15:02:49","doi":"10.21203/rs.3.rs-5371564/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":"caa4b9ff-f384-4780-9ded-6fcaf573feee","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-20T15:02:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 15:02:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5371564","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5371564","identity":"rs-5371564","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}