A Cross-Sectional Study on Healthcare Professionals' Knowledge, Attitudes, and Practices Regarding the CHS-DRG Payment Policy in Yunnan Province: A Structural Equation Modeling Analysis

A Cross-Sectional Study on Healthcare Professionals' Knowledge, Attitudes, and Practices Regarding the CHS-DRG Payment Policy in Yunnan Province: A Structural Equation Modeling Analysis | 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 A Cross-Sectional Study on Healthcare Professionals' Knowledge, Attitudes, and Practices Regarding the CHS-DRG Payment Policy in Yunnan Province: A Structural Equation Modeling Analysis Jian Yang, Dan Qin, Fan Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4767628/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This cross-sectional study aimed to examine the knowledge, attitudes, and practices of healthcare professionals in Yunnan Province, China, regarding the China Healthcare Security Diagnosis Related Groups (CHS-DRG) payment policy in the context of value-based healthcare using a structural equation model. Methods The study was conducted among healthcare professionals in Yunnan Province from March 2023 to May 2023. A self-administered questionnaire was used to collect data on knowledge, attitudes, and practices regarding the CHS-DRG payment policy. Structural equation modeling (SEM) was used to test the hypothesized relationships among the variables. Data analysis and statistical description were performed using SPSS 27.0, and structural equation modeling (SEM) was constructed and analyzed using AMOS 26.0. Results A total of 357 healthcare professionals from 30 medical institutions participated in the study. The results of the SEM analysis showed that knowledge and attitudes significantly influenced the practices regarding the CHS-DRG payment policy (β = 0.36, p < 0.001 and β = 0.46, p < 0.001 respectively), and the direct effect of attitudes on practices was stronger than that of knowledge on practices. Moreover, knowledge not only directly affected practices (β = 0.934, p = 0.001) but also indirectly influenced practices through attitudes (β = 0.462, p = 0.001), with a total effect of 1.396. In addition, demographic characteristics had a positive impact on healthcare professionals' CHS-DRG payment policy knowledge (p < 0.001). Conclusion This study reveals that healthcare professionals in Yunnan Province show moderate knowledge, positive attitudes, and influenced practices toward the CHS-DRG payment policy. Challenges and barriers exist, requiring collaborative efforts from policymakers and healthcare professionals to ensure effective implementation. Targeted interventions and longitudinal studies are recommended to improve understanding, promote value-based healthcare, and evaluate outcomes and costs. CHS-DRG payment policy value-based healthcare knowledge attitudes and practices (KAP) structural equation modeling healthcare professionals Yunnan Province Figures Figure 1 Figure 2 Figure 3 Introduction China has been undergoing significant healthcare reforms to enhance quality and efficiency while reducing patient financial burden. As part of this reform, the government introduced the Diagnosis-Related Group (DRG) payment system, which aims to promote value-based healthcare by linking payments to care quality and outcomes rather than service volume [ 1 – 2 ] . The CHS-DRG payment policy is a vital component of broader healthcare system reform in China, emphasizing quality, safety, and efficiency. The traditional fee-for-service model, which reimburses hospitals based on service volume, has led to excessive healthcare service utilization without incentivizing providers to improve quality and outcomes. To address these issues, the CHS-DRG payment policy links payment to the value of care provided rather than the volume of services rendered [ 3 – 4 ] . In 2019, the Chinese government launched the China Hospital Substance DRG (CHS-DRG) payment policy, tailored to the unique characteristics of the Chinese healthcare system. Under this system, hospitals receive reimbursement based on standardized codes reflecting the severity of patients' conditions and the complexity of treatment [ 5 – 6 ] . However, the implementation of the CHS-DRG payment policy has encountered challenges, particularly limited understanding and acceptance among healthcare professionals. Many providers are unfamiliar with the coding and documentation requirements of the CHS-DRG system, and resistance to changing established practices and procedures exists. Additionally, concerns about the policy's financial implications, such as potential reimbursement rate reductions for certain services, further hinder implementation [ 7 ] . The success of the CHS-DRG payment policy hinges on healthcare professionals' knowledge, attitudes, and practices (KAP) in its implementation. As one of the pioneering provinces to implement the CHS-DRG payment policy, Yunnan Province is the focus of this investigation. The study aims to examine the KAP of healthcare professionals in Yunnan Province medical institutions regarding the CHS-DRG payment policy while identifying factors influencing their acceptance and implementation [ 8 ] . Utilizing a cross-sectional design and structural equation modeling, data collected from healthcare professionals in Yunnan Province medical institutions will be analyzed. The study findings will contribute to a better understanding of the challenges and opportunities associated with implementing the CHS-DRG payment policy in China. Policymakers and healthcare professionals in other regions will benefit from these valuable insights. Ultimately, the study aims to facilitate the successful implementation of the CHS-DRG payment policy, improving the quality and efficiency of healthcare delivery across China. Methods 2.1 Research subjects and sampling 2.1.1 Inclusion and Exclusion Criteria Healthcare professionals eligible for this cross-sectional study in Yunnan Province, China, conducted between May 2023 and June 2023, met specific criteria. The inclusion criteria focused on healthcare professionals with a minimum of six months' experience in hospitals who actively participated in the CHS-DRG payment policy implementation. Exclusion criteria were applied to exclude healthcare professionals who were on vacation or sick leave during the study period. 2.1.2 Sampling Methodology A meticulously designed sampling methodology was implemented to ensure the robustness and representativeness of the study sample. To account for regional variations in healthcare practices, six cities within Yunnan Province were selected, ensuring geographical diversity by choosing hospitals from various regions within the province. The sample included hospitals of different sizes, encompassing both large tertiary hospitals and smaller community healthcare centers, which facilitated capturing perspectives from healthcare professionals across various types of healthcare institutions. To maintain sample representativeness, random sampling was conducted within each stratum, considering both geographical region and hospital size category. This rigorous sampling strategy culminated in the inclusion of a total of 357 healthcare professionals from 30 hospitals in the study. This diverse and systematically obtained sample provides comprehensive insights into healthcare professionals' knowledge, attitudes, and practices concerning the CHS-DRG payment policy. The adherence to such a rigorous and systematic sampling approach enhances the academic and practical value of the study findings. 2.1.3 Sample Size Determination To determine the final sample size, single population pro portion formula was employed by taking an assumption of 95% CI, 5% margin of error, and 54.3% prevalence of CHS-DRG payment policy knowledge from a study done in Chongqing, Shaanxi and Xinjiang province [ 9 ] . $$\:n=\frac{\left({{Z}_{a/2}}^{2}\right)\times\:P\times\:\left(1-P\right)}{{d}^{2}}$$ Where, n = required sample size, Zα/₂ is a critical value at 95% CI(1.96), and at 5% margin of error (d = 0.05), and p is the prevalence of CHS-DRG payment policy knowledge = 54.3%. Accordingly, \(\:n=\frac{{1.96}^{2}\times\:0.543\times\:(1-0.543)}{{0.05}^{2}}\) =381 However, the source population is less than 10,000 adjusted formulas were used. $$\:nf=\frac{ni}{[1+\frac{ni}{N}]}$$ Where nf = final sample size, ni = initial sample size = 381, and N = the source population = 550. \(\:ni=\frac{381}{1+\frac{381}{550}}\) =225 By adding 10% of the non-response rate, it gives a final sample size of 245 people. The final sampled size is above 245. 2.2 Research hypothesis The KAP theory is often applied in health-related practices and public health fields. This theory believes that cognition is the basis for establishing positive and correct attitudes, which in turn is the driving force for changing related practices. Based on KAP theory and demographic characteristics, this paper proposes the following hypotheses: Hypothesis 1 (H1 and H1Δ): Knowledge of the CHS-DRG payment policy has a positive impact on attitudes. Hypothesis 2 (H2 and H2Δ): Attitudes toward the CHS-DRG payment policy has a positive impact on practices. Hypothesis 3 (H3 and H3Δ): Knowledge of the CHS-DRG payment policy has a positive impact on practices. Hypothesis 4 (H4 and H4Δ): Demographic characteristics have a positive impact on knowledge of CHS-DRG payment policy. Hypothesis 5 (H5 and H5Δ): Demographic characteristics have a positive impact on attitudes toward the CHS-DRG payment policy. Hypothesis 6 (H6 and H6Δ): Demographic characteristics have a positive impact on practices toward the CHS-DRG payment policy. 2.3 Data collection Data on healthcare professionals' knowledge, attitudes, and practices toward the CHS-DRG payment policy were collected using a self-administered questionnaire. The questionnaire was developed based on reviewing the literature and consulting with experts in the field. The questionnaire consisted of four parts: (1) demographic information, (2) knowledge of the CHS-DRG payment policy, (3) attitudes toward the CHS-DRG payment policy, and (4) practices regarding the CHS-DRG payment policy. The knowledge section consisted of six questions with one point awarded for each correct answer and zero points for incorrect or unknown answers, with a total score range of 0–6 points. The attitudes section consisted of six questions, with one point awarded for "negative" or "do not know", two points for "neutral" or "maybe", and three points for "positive" or "definitely", with a total score range of 6–18 points. The practices section consisted of seven questions, with scores assigned based on option levels and a total score range of 7–28 points. 2.4 Data analysis The data analysis and statistical description were conducted using SPSS 27.0, while the structural equation model (SEM) was constructed and analyzed using AMOS 26.0. Descriptive statistics were used to analyze the demographic characteristics of the participants. The mean and standard deviation of knowledge, attitudes, and practices were calculated. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to construct the SEM and test the hypothesized relationships between variables. Model fit was evaluated using the chi-square test, comparative fit index (CFI), Tucker‒Lewis index (TLI), incremental fit index (IFI), and root mean square error of approximation (RMSEA). 2.5 Factor Analysis and Validity Measures in Research Exploratory factor analysis (EFA) is used to test whether the number of factors extracted from the scale is consistent with the scale dimensions, and whether the question items corresponding to each common factor are consistent with the question items included in each dimension of the scale. Confirmatory factor analysis (CFA) is a research method used to measure whether the correspondence between factors and items is consistent with the researcher's prediction. CFA mainly validates the structural validity, convergent validity, and discriminant validity of the model. AVE is the Average of variance extracted, and CR is the composite reliability, both of them are used to validate the convergent validity, and in general, if the AVE is greater than 0.5 or CR is greater than 0.7, the convergent validity of factors and variables is indicated. 2.6 Structural equation modeling principles Structural equation modelling (SEM) is a widely used multivariate technique for investigating the direct and indirect effect of relationships between observed and latent variables. In SEM, an observed variable is a variable that has been directly measured and latent variables are unobserved variables that cannot be measured directly. The theory of SEM simplifies complex relationships between variables by utilizing a path model or analysis for explaining effects resulting from observed and latent variables [ 10 ] . The SEM summarises a linear structural relationship into a measurement model (CFA model) and structural model. The measurement model is defined as: $$\:{x}_{1}={{\Lambda\:}}_{1}\eta\:+{\epsilon\:}_{1}$$ $$\:{x}_{2}={\varLambda\:}_{2}\xi\:+{\epsilon\:}_{2}$$ where \(\:{x}_{1}(r\times\:1)\) and \(\:{x}_{2}(s\times\:1)\) are random vectors of the observed variables that explains endogenous (dependent) η and exogenous (independent) ξ latent variables, \(\:{\varLambda\:}_{1}(r\times\:{q}_{1})\) and \(\:{\varLambda\:}_{2}(s\times\:{q}_{2})\) are loading matrices, and \(\:{\epsilon\:}_{1}(r\times\:1)\) , and \(\:{\epsilon\:}_{2}(s\times\:1)\) are random vectors of measurement errors. The measurement model can also be expressed in matrix form as: $$\:y=\left(\begin{array}{c}{x}_{1}\\\:{x}_{2}\end{array}\right)=\left(\begin{array}{cc}{\varLambda\:}_{1}&\:0\\\:0&\:{\varLambda\:}_{2}\end{array}\right)\left(\begin{array}{c}\eta\:\\\:\xi\:\end{array}\right)+\left(\begin{array}{c}{\epsilon\:}_{1}\\\:{\epsilon\:}_{2}\end{array}\right)$$ On the other hand, the structural model can be defined as: $$\:\eta\:=B\eta\:+\varGamma\:\xi\:+\delta\:$$ Where \(\:B({q}_{1}\times\:{q}_{2})\) and \(\:\varGamma\:({q}_{1}\times\:{q}_{2})\) are unknown matrices of regression coefficients that describes the cause-and-effect relationship between η and ξ, and \(\:\delta\:({q}_{1}\times\:1)\) is a random vector of residuals or error of endogenous variables. Thus, the SEM is expressed as a CFA model whose latent factors represent a linear structural equation. The model aids in finding a direct and indirect effect among variables and the measurement errors. Statistically, SEM depicts an extended form of a generalized linear model like the multiple regression model and analysis of variance [ 11 ] . The performance of the measurement and structural model was evaluated using root mean square error approximation (RMSEA), comparative fit index (CFI), tucker-lewis index (TLI), and incremental fit index (IFI). The RMSEA, CFI, TLI and IFI can be expressed as : $$\:RMSEA=\sqrt{\frac{\text{max}\left({\chi\:}_{k}^{2}-d{f}_{k},0\right)}{d{f}_{k}\left(N-1\right)}}$$ $$\:CFI=\frac{\text{max}\left({\chi\:}_{0}^{2}-d{f}_{0},0\right)-\text{max}\left({\chi\:}_{k}^{2}-d{f}_{k},0\right)}{\text{max}\left({\chi\:}_{0}^{2}-d{f}_{0},0\right)}$$ $$\:TLI=\frac{\frac{{\chi\:}_{0}^{2}}{d{f}_{0}}-\frac{{\chi\:}_{k}^{2}}{d{f}_{k}}}{\frac{{\chi\:}_{0}^{2}}{df}-1}$$ $$\:IFI=(Tb-Tm)/(Tb-dfm)$$ Results 3.1 Demographic characteristics The Cronbach's α coefficient of the self-developed questionnaire in this study was 0.838(Cronbach's alpha, Internal consistency. α ≥ 0.9, Excellent. 0.9 > α ≥ 0.8, Good. 0.8 > α ≥ 0.7, Acceptable. 0.7 > α ≥ 0.6, Questionable. 0.6 > α ≥ 0.5), and the KMO value was 0.894༈KMO values > 0.9, highly suitable; > 0.8, suitable; >0.7, acceptable; >0.6 marginally usable; > 0.5 not suitable༉, indicating good reliability and validity of the questionnaire. The majority of the study participants were from medical institutions in Yunnan Province (Fig. 1 ), and their demographic characteristics are shown in Table 1 . Among the 357 healthcare professionals who participated in the study, 68.6% were female, and 49.3% were aged between 26 and 35 years old. The majority of the participants were clinical pharmacists (39.5%), followed by physicians (26.1%) and nurses (21.3%). All participants had a college degree or higher, with 74.8% being undergraduate students. In terms of professional and technical titles, 31.1% were intermediate-level, 29.1% were associate/senior assistants, and 17.4% were deputy directors or above. A total of 59.4% of the participants were from tertiary comprehensive hospitals (including traditional Chinese medicine hospitals), followed by secondary comprehensive hospitals, accounting for 34.7%. Table 1 Demographic characteristics of the participants Variables Variables n % Gender Male 112 31.4 Female 245 68.6 Age (years) 45 57 16.0 Professional title Positive height 16 4.5 Subtropical high 46 12.9 Intermediate 111 31.1 Division level/Assistant 104 29.1 Scholar level 39 10.9 Other 41 11.5 Position Doctor 93 26.1 Pharmacist 141 39.5 Nurse 76 21.3 Technician 20 5.6 Medical insurance department staff 14 3.9 Other 13 3.6 Education level Master's degree and above 37 10.4 Undergraduate 267 74.8 Specialist 53 14.8 Hospital nature Third level comprehensive hospitals (including traditional Chinese medicine hospitals) 212 59.4 Secondary comprehensive hospitals (including traditional Chinese medicine hospitals) 124 34.7 Traditional Chinese Medicine Specialized Hospital 12 3.4 Community Health Service Center (including service stations) 2 0.6 Township health centers (including clinics) 6 1.7 Other 1 0.3 Years of work 0–1 years 9 2.5 2–5 years 82 23.0 6–10 years 110 30.8 11–15 years 62 17.4 16 years and above 94 26.3 3.2 Scores of knowledge, attitudes, and practices Table 2 presents the mean scores and standard deviations for knowledge, attitudes, and practices regarding the CHS-DRG payment policy. The mean knowledge score was 4.01 (SD = 2.80), with an overall awareness rate of 66.83%, indicating insufficient understanding of the CHS-DRG payment policy among the participants. The mean attitudes score was 13.39 (SD = 3.38), with an overall positive attitudes rate of 74.39%, indicating that the participants held a positive attitudes toward the CHS-DRG payment policy. The mean practices score was 18.59 (SD = 6.41), with an overall compliance rate of 66.39%, indicating a low level of compliance with the CHS-DRG payment policy among the participants. Table 2. Mean scores and standard deviations of knowledge, attitudes, and practices Variables Mean SD Knowledge 4.01 2.80 Attitudes 13.39 3.38 Practices 18.59 6.41 Abbreviations list: SD: Standard deviation; 3.3 EFA and CFA analysis In this study, we employed both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to construct a Knowledge, Attitudes, and Practices (KAP) model to gauge healthcare professionals' perspectives on the CHS-DRG payment policy. Table 3 delineates the variables to be assessed in this study, encompassing three latent variables: knowledge, attitudes, practices, demographic characteristics, along with 24 observational variables. These observational variables are denoted as K1-K6, A1-A6, P1-P6、P8, and Q2、Q4-Q7 [12] . Exploratory Factor Analysis (EFA) revealed the presence of four common factors, denoted as F1, F2, F3, and F4. The cumulative variance contribution exceeded the 50% threshold, reaching 62.1%, indicating that these four factors adequately encapsulated the underlying information. With the exception of items A3 and Q4, all other items exhibited factor loadings exceeding 0.4, highlighting their significant association with their respective factors. Furthermore, the factor congruence of part K exceeded 0.7, signifying robust internal consistency within this subset (refer to Table4). Confirmatory Factor Analysis (CFA) results, as presented in Table 5, showcased that standardized loadings were generally within the range of 0.3 to 0.9 (p < 0.001). To evaluate construct validity, it's essential that the Average Variance Extracted (AVE) exceeds 0.5, and the Composite Reliability (CR) surpasses 0.7 (Hair et al., 2014). For the knowledge and practices factors related to the CHS-DRG payment policy, both AVE and CR surpassed these thresholds, indicating strong measurement indicator extraction. However, the AVE for the attitudes and demographic characteristics factors fell below 0.5, but their CR values exceeded 0.7, indicating good extraction of measurement indicators within these factors (refer to Table 5). Table 3 Latent variables and observed variables latent variables Items Observed Variables Knowdelge K1 Ever understood of CHS-DRG payment policy K2 Principal characteristics of CHS-DRG payment policy K3 Knowledge regarding the scope of applicability of CHS-DRG payment policy K4 Utilization methods of CHS-DRG payment K5 CHS-DRG payment policy impacts medical practices K6 Training related to the CHS-DRG payment policy Attitudes A1 Attitudes toward the CHS-DRG payment policy A2 Enhance healthcare quality by CHS-DRG payment policy A3 Increase the workload by CHS-DRG payment policy A4 Reduce healthcare costs by CHS-DRG payment policy A5 Improve patient satisfaction by CHS-DRG payment policy A6 Enhance the efficiency of medical practices by CHS-DRG payment policy Practices P1 Usage of the CHS-DRG payment policy in daily work P2 Consider the CHS-DRG payment policy in decision-making process P3 Provide feedback on the implementation of the CHS-DRG payment policy P4 Communicate with patients and their families regarding issues related to the CHS-DRG payment policy P5 Participate in discussions or training related to the CHS-DRG payment policy P6 Strictly adhere to the CHS-DRG payment policy P8 Confidence and ability to effectively implement the CHS-DRG payment policy Demographic characteristics Q2 Healthcare professional’s age Q4 Healthcare professional’s position Q5 Healthcare professional’s professional and technical qualifications Q6 The hospital nature of healthcare professional’s work Q7 Healthcare professional’s working experience Table 4 Results of exploratory factor analysis Paths F1 F2 F3 F4 K1 <--- Knowledge 0.833 K2 <--- Knowledge 0.877 K3 <--- Knowledge 0.850 K4 <--- Knowledge 0.817 K5 <--- Knowledge 0.770 K6 <--- Knowledge 0.723 A1 <--- Attitudes 0.775 A2 <--- Attitudes 0.790 A3 <--- Attitudes -0.118 A4 <--- Attitudes 0.544 A5 <--- Attitudes 0.806 A6 <--- Attitudes 0.819 P1 <--- Practices 0.778 P2 <--- Practices 0.763 P3 <--- Practices 0.803 P4 <--- Practices 0.807 P5 <--- Practices 0.747 P6 <--- Practices 0.664 P8 <--- Practices 0.441 Q2 <--- Demographic characteristics 0.888 Q4 <--- Demographic characteristics 0.317 Q5 <--- Demographic characteristics 0.826 Q6 <--- Demographic characteristics 0.435 Q7 <--- Demographic characteristics 0.878 Table 5 Results of confirmatory factor analysis Paths Standardized Regression Weights P AVE CR K1 <--- Knowledge 0.773 0.667 0.922 K2 <--- Knowledge 0.874 < 0.001 K3 <--- Knowledge 0.881 < 0.001 K4 <--- Knowledge 0.863 < 0.001 K5 <--- Knowledge 0.771 < 0.001 K6 <--- Knowledge 0.721 < 0.001 A1 <--- Attitudes 0.727 0.453 0.794 A2 <--- Attitudes 0.807 < 0.001 A3 0.05 A4 <--- Attitudes 0.476 < 0.001 A5 <--- Attitudes 0.826 < 0.001 A6 <--- Attitudes 0.870 < 0.001 P1 <--- Practices 0.805 0.564 0.898 P2 <--- Practices 0.763 < 0.001 P3 <--- Practices 0.799 < 0.001 P4 <--- Practices 0.819 < 0.001 P5 <--- Practices 0.793 < 0.001 P6 <--- Practices 0.596 < 0.001 P8 <--- Practices 0.597 < 0.001 Q2 <--- Demographic characteristics 0.916 < 0.001 0.445 0.751 Q4 <--- Demographic characteristics 0.132 < 0.05 Q5 <--- Demographic characteristics 0.659 < 0.001 Q6 <--- Demographic characteristics 0.312 < 0.001 Q7 <--- Demographic characteristics 0.934 < 0.001 Abbreviations list: AVE: Average of variance extracted; CR: composite reliability 3.4 SEM analysis 3.4.1 Fitting Results of the Healthcare Professionals' CHS-DRG Payment Policy KAP Model Drawing from the outcomes of both EFA and CFA, as well as the underlying hypotheses, we constructed a Structural Equation Model (SEM) to elucidate the knowledge, attitudes, and practices of healthcare professionals concerning the CHS-DRG payment policy (see Figure 2). The SEM analysis affirmed the model's appropriateness (refer to Table 6). The χ2/df (chi-square statistics to degrees of freedom) ratio, standing at 3.029, falls within the generally acceptable range, lending substantial support to the model. Furthermore, critical fit indices, including CFI (comparative fit index) at 0.926, IFI (incremental fit index) at 0.927, and TLI (Tucker–Lewis index) at 0.915, all exceeded the requisite minimum threshold of 0.90. While the RMSEA (root mean square error of approximations) slightly surpassed the suggested level of 0.05, it remained below the upper limit of 0.08 (Hair et al., 2014) [13] , reinforcing model adequacy. The results presented in Table 7 indicate the significant influence of both knowledge and attitudes on the practices of the CHS-DRG payment policy (β = 0.36, p < 0.001 and β = 0.46, p < 0.001, respectively), thereby confirming hypotheses H3 and H2. Notably, the direct impact of attitudes on practices outweighed that of knowledge on practices. Additionally, knowledge regarding the CHS-DRG payment policy exhibited a positive influence on attitudes (β = 0.36, p < 0.001), corroborating hypothesis H1. Furthermore, the mediation analysis (refer to Table 8) demonstrated that knowledge had both a direct (β = 0.934, p = 0.001) and an indirect impact on practices through its influence on attitudes (β = 0.462, p = 0.001), resulting in a total effect of 1.396. These findings provide a comprehensive understanding of the relationships between knowledge, attitudes, and practices with respect to the CHS-DRG payment policy among healthcare professionals. Table 6 Fit Index for the Structural Equation Model of CHS-DRG Payment Policy Knowledge, Attitudes, and Practices for healthcare professionals Fit Index χ 2 /df RMSEA IFI TLI CFI Fit Standards <5.00 0.90 >0.90 >0.90 Fit Results 3.029 0.075 0.927 0.915 0.926 Abbreviations list: RMSEA: root mean square error of approximation; IFI: incremental fit index; TLI: Tucker‒Lewis index; CFI: comparative fit index; Table 7 Path Coefficients for the KAP Model of CHS-DRG Payment Policy for healthcare professionals Paths Estimate S.E. C.R. P Label Attitudes <--- Knowledge .385 .078 6.338 *** H1 Practices <--- Knowledge .360 .140 6.693 *** H3 Practices <--- Attitudes .462 .118 7.901 *** H2 Abbreviations list: S.E.: standard error; C.R.: critical ratio; Table 8 Mediating Effects of CHS-DRG Payment Policy Knowledge and Practices for healthcare professionals Parameter Estimate Lower Upper P Indirect effects .462 .314 .649 .001 Direct effects .934 .657 1.262 .001 Total effects 1.396 1.110 1.745 .001 3.4.2 Influence of Demographic Characteristics on Healthcare Professionals' CHS-DRG Payment Policy KAP In order to delve deeper into the influence of demographic characteristics on healthcare professionals' knowledge, attitudes, and practices regarding the CHS-DRG payment policy, we incorporated these characteristics as a latent variable in the CHS-DRG payment policy KAP model. This encompassed age, job position, professional and technical title, nature of employing institution, and years of work as observed variables. The χ²/df stands at 2.661, and the RMSEA registers at 0.068, both indicative of a robust model fit, bolstering overall model support. Additionally, critical fit indices - CFI and IFI at 0.917, and TLI at 0.907 - surpass the requisite threshold of 0.90, affirming the adequacy of the model. The outcomes of the model (depicted in Figure 3, detailed in Table 9) underscore that demographic characteristics wield a significant positive impact on healthcare professionals' knowledge of the CHS-DRG payment policy (p < 0.001), thereby validating hypothesis H4. However, it is noteworthy that demographic characteristics exert a negative influence on attitudes and practices, thereby rejecting hypotheses H5 and H6. With the exception of job position, all other demographic characteristics manifest a substantial positive impact on healthcare professionals' knowledge, attitudes, and practices pertaining to the CHS-DRG payment policy (p < 0.001). Of particular note, Q2 (age) and Q7 (working experience) emerge as the variables with the most pronounced impact values (refer to Table 10) . These findings collectively illuminate the intricate interplay between demographic characteristics and healthcare professionals' comprehension, disposition, and actions regarding the CHS-DRG payment policy. Table 9 Fit Index for the KAP Model of CHS-DRG Payment Policy for Healthcare Professionals and its Correlation with Demographic Characteristics Fit Index χ 2 /df RMSEA IFI TLI CFI Fit Standards <5.00 0.90 >0.90 >0.90 Fit Results 2.661 0.068 0.917 0.907 0.917 Abbreviations list: RMSEA: root mean square error of approximation; IFI: incremental fit index; TLI: Tucker‒Lewis index; CFI: comparative fit index; Table 10 Path Coefficients for the KAP Model of CHS-DRG Payment Policy for Healthcare Professionals and its Correlation with Demographic Characteristics Paths Estimate S.E. C.R. P Label Knowledge <--- Demographic Characteristics .251 .023 4.357 *** H4Δ Attitudes <--- Knowledge .403 .081 6.395 *** H1Δ Attitudes <--- Demographic Characteristics − .069 .029 -1.226 .220 H5Δ Practices <--- Knowledge 0.398 .145 7.101 *** H3Δ Practices <--- Attitudes .452 .117 7.815 *** H2Δ Practices <--- Demographic Characteristics − .135 .048 -2.883 .004 H6Δ Q2 <--- Demographic Characteristics 1.000 Q4 <--- Demographic Characteristics .207 .086 2.418 .016 Q5 <--- Demographic Characteristics 1.077 .074 14.571 *** Q6 <--- Demographic Characteristics .308 .052 5.890 *** Q7 <--- Demographic Characteristics 1.390 .059 23.574 *** Abbreviations list: S.E.: standard error; C.R.: critical ratio; Discussion 4.1 Knowledge about the CHS-DRG payment policy The study found that overall, healthcare professionals in Yunnan Province have a moderate level of knowledge about the CHS-DRG payment policy. This suggests that there is a need for more education and training for healthcare professionals to increase their understanding of the policy. The study also found that healthcare professionals who had received training on the CHS-DRG payment policy had higher levels of knowledge about the policy than those who had not received such training. This highlights the importance of training and education for healthcare professionals to increase their understanding of the policy [14-15] . 4.2 Attitudes toward the CHS-DRG payment policy The study found that healthcare professionals in Yunnan Province generally have positive attitudes toward the CHS-DRG payment policy. This suggests that healthcare professionals are receptive to the idea of value-based healthcare and are willing to adopt new practices and procedures to support the policy. However, the study also found that some healthcare professionals were concerned about the financial implications of the policy, as it may reduce the reimbursement rates for some services. This highlights the need for policymakers to address the financial concerns of healthcare professionals and to ensure that the policy does not adversely affect the financial viability of medical institutions [16-17] . 4.3 Practices related to the CHS-DRG payment policy The study found that healthcare professionals in Yunnan Province had varying levels of practices related to the CHS-DRG payment policy. While some healthcare professionals had already adopted the new practices and procedures required by the policy, others were still using the old practices and procedures. This suggests that there is a need for more education and training for healthcare professionals to ensure that they are using the new practices and procedures required by the policy. The study also found that some healthcare professionals were concerned about the additional workload created by the policy. This highlights the need for policymakers to ensure that the policy is implemented in a way that minimizes the additional workload for healthcare professionals [18-19] . 4.4 Demographic Characteristics and Their Nonsignificant Influence on CHS-DRGs Knowledge, Attitudes, and Practices The present study aimed to investigate the relationship between demographic characteristics and healthcare professionals' KAP regarding the CHS-DRG payment policy using a structural equation modeling (SEM) approach. Surprisingly, the results demonstrated that demographic factors such as education, hospital level, professional title, job position, work experience, and age had no significant impact on attitudes and practices toward CHS-DRGs. Possible reasons for these findings include the current training and education system not adequately addressing the CHS-DRG payment policy and the fact that job position, work experience, and age may not directly influence one's understanding, attitudes, and practices toward the policy [20-21] . Healthcare professionals' KAP might be shaped by a myriad of factors, including personal beliefs, professional values, and organizational culture, which may not necessarily be associated with one's position, experience, or age. 4.5 Barriers to the implementation of the CHS-DRG payment policy The study identified several barriers to the successful implementation of the CHS-DRG payment policy, including a lack of understanding and knowledge, concerns about financial implications, and additional workload. These issues highlight the need for policymakers to address healthcare professionals' concerns and provide education and training to enhance understanding and acceptance of the policy [22] . To improve diagnostic and treatment efficiency and quality, the CHS-DRG payment policy should be integrated into clinical practices and continuously optimized. For example, developing coding for traditional Chinese medicine diagnosis can better meet the healthcare needs of the Chinese population. Strengthening the supervision of medical institutions can prevent misuse of the DRG policy, increasing efficiency and fairness. In optimizing the policy, healthcare professionals' opinions and proposals should be fully considered to ensure reasonable, scientific, and feasible implementation [23-24] . 4.6 Implications for policymakers and healthcare professionals To address the insufficient understanding and implementation of the CHS-DRG payment policy, it is crucial to incorporate CHS-DRG payment policy and value-based healthcare concepts into medical education and continuous professional development programs. This would not only enhance healthcare professionals' understanding of the policy but also promote a more positive attitudes and proactive practices toward its implementation. Additionally, healthcare organizations should encourage employees across all job positions and experience levels to adopt the CHS-DRG payment policy, aiming for a uniform KAP among their workforce. Conclusion The CHS-DRG payment policy aims to promote value-based healthcare in China. This study found that healthcare professionals in Yunnan Province have moderate knowledge and generally positive attitudes toward the policy. Knowledge and attitudes significantly influence their practices. However, several challenges hinder successful implementation. Policymakers and healthcare professionals must collaborate to address these challenges and ensure effective policy implementation. Education and training programs may improve understanding and implementation. Future studies should use longitudinal designs to track changes in knowledge, attitudes, and practices, and to evaluate the policy's impact on healthcare outcomes and costs. Limitations of the study There are several limitations to this study that need to be acknowledged. First, the study was conducted in Yunnan Province, which may limit the generalizability of the findings to other regions of China. Second, the study used a cross-sectional design, which limits the ability to establish causality between the variables. Finally, the study relied on self-reported data, which may be subject to social desirability bias. Abbreviations CHS-DRG: China Healthcare Security Diagnosis Related Groups; KAP: Knowledge, Attitudes, and Practices; SEM: Structural Equation Modeling; ADRs: Adverse drug reactions; RMSEA: Root mean square error of approximation; IFI: Incremental fit index; TLI: Tucker‒Lewis index; CFI: Comparative fit index; CFA: Confirmatory factor analysis; EFA: Exploratory factor analysis; KMO: Kaiser-Meyer-Olkin; CR: Composite reliability; AVE: Average of variance extracted; S.E.: Standard Error; AGFI: Adjusted Goodness-of-Fit Index; χ2/df: Chi-square statistics to degrees of freedom. Declarations Ethics Approval and Consent to Participate This study was conducted with ethical approval from the biomedical ethics committee of Kunming Medical University, Kunming, Yunnan, China[NO.2023–0107]. Information about the study’s objectives and contents, and the right to refuse participation, was provided to all participants before data collection. Written informed consent was obtained from all participants, and for those under 16 years old, consent was obtained from their legal guardians. All methods followed relevant guidelines and regulations. Consent for publication We, the authors of "A Cross-Sectional Study on Healthcare Professionals' Knowledge, Attitudes, and Practices Regarding CHS-DRG Payment Policy in Yunnan Province," consent to its publication. All authors have approved the final manuscript, which is original and not submitted elsewhere. We affirm the accuracy of the data and results and have disclosed any conflicts of interest. Availability of data and materials All data generated or analysed during this study are included in this published article. Competing interests The authors declare that they have no competing interests. Funding This work was supported by [the bureau of Yunnan Healthcare Security] with the subject of "A Study on Strategies to Promote the Development of the Biopharmaceutical Industry in Yunnan Province with a Focus on Health Insurance Policies" entrusted by, project number 202001007. Authors' contributions JY conceived and designed the study; DQ performed the data analyses and survey; DQ wrote the manuscript; JY provided critical revisions; FL approved the final version. Jian Yang and Dan Qin are co-first authors of this study, having made equal contributions to the research and writing of the manuscript. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work. Acknowledgements The authors thank all researchers who participated in the questionnaire survey in Yunnan, China. References Li Y, Feng X, Ma X, et al. 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Empirical study on the relationship between knowledge, attitude and behavior of hypertension patients based on structural equation model. Journal of Central South University (Medical Edition). 2017;42(02):195-201. Zheng S, Zhao L, Ju N, Hua T, Zhang S, Liao S. Relationship between oral health-related knowledge, attitudes, practice, self-rated oral health and oral health-related quality of life among Chinese college students: a structural equation modeling approach. BMC Oral Health. 2021;21(1):99. doi:10.1186/s12903-021-01419-0. PMID: 33676475; PMCID: PMC7936478. Laorujisawat M, Wattanaburanon A, Abdullakasim P, Maharachpong N. Rabies-Related Knowledge, Attitudes, and Practices Among Primary School Students in Chonburi Province, Thailand. Inquiry. 2022;59:469580221087881. doi:10.1177/00469580221087881. PMID: 35410522; PMCID: PMC9008862. Sarma PK, Alam MJ, Begum IA. Farmers' knowledge, attitudes, and practices towards the adoption of hybrid rice production in Bangladesh: a PLS-SEM approach. GM Crops Food. 2022;13(1):327-341. doi:10.1080/21645698.2022.2140678. PMID: 36413007; PMCID: PMC9683048. Yang Q, Chen D, Zhang W, et al. Cognitive Status and Learning Needs of Medical Institution Personnel on CHS-DRG Payment. Journal of Medicine and Society. 2022;35(05):96-101. Zhu WL, Cheng HJ, Yang LB, Lu HM, A KZ, Zhao Q, Xu SF, Wang WB. A model analysis on the knowledge-attitude-practice of children guardians in Jiangxi, Shanghai and Qinghai. Zhonghua Liu Xing Bing Xue Za Zhi. 2021;42(2):309-315. Chinese. doi:10.3760/cma.j.cn112338-20200321-00411. PMID: 33626621. Zhang JY, Guo XR, Wu XW, et al. KAP investigation and influential factor study of medication risk among residents. J China Pharm. 2018;29(11):1445-1448. He Z, Ji L, Tang S, et al. Interactions among knowledge, attitude and practice about malaria prevention and control in Chinese rural residents: a multiple structural equation model analysis. Chin J Public Health. 2020;36(5):826-830. Hair JF Jr, et al. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review. 2014;26(2):106-121. Ma Z, Peng L, Li J, Wu L. The situation analysis of hot dry rock geothermal energy development in China-based on structural equation modeling. Heliyon. 2022;8(12). doi:10.1016/j.heliyon.2022.e12123. PMID: 36544842; PMCID: PMC9761704. Ampofo RT, Aidoo EN. Structural equation modelling of COVID-19 knowledge and attitude as determinants of preventive practices among university students in Ghana. Sci Afr. 2022;16. doi:10.1016/j.sciaf.2022.e01182. PMID: 35434433; PMCID: PMC8993488. Kang H, Ahn JW. Model Setting and Interpretation of Results in Research Using Structural Equation Modeling: A Checklist with Guiding Questions for Reporting. Asian Nurs Res (Korean Soc Nurs Sci). 2021;15(3):157-162. doi:10.1016/j.anr.2021.06.001. PMID: 34144201. Lam TY, Maguire DA. Structural equation modeling: theory and applications in forest management. Int J Forest Res. 2012;2012. Lee SY, Song XY. Structural equation models. 2010. Beran TN, Violato C. Structural equation modeling in medical research: a primer. BMC Res Notes. 2010;3:267. doi:10.1186/1756-0500-3-267. PMID: 20969789; PMCID: PMC2987867. Shi D, Lee T, Maydeu-Olivares A. Understanding the Model Size Effect on SEM Fit Indices. Educ Psychol Meas. 2019;79(2):310-334. doi:10.1177/0013164418783530. PMID: 30911195; PMCID: PMC6425088. Ryu E. Model fit evaluation in multilevel structural equation models. Front Psychol. 2014;5:81. doi:10.3389/fpsyg.2014.00081. PMID: 24550882; PMCID: PMC3913991. Mulaik SA, James LR, Van Alstine J, et al. Evaluation of goodness-of-fit indices for structural equation models. Psychol Bull. 1989;105(3):430. Pogurschi EN, Petcu CD, Mizeranschi AE, Zugravu CA, Cirnatu D, Pet I, Ghimpețeanu OM. Knowledge, Attitudes and Practices Regarding Antibiotic Use and Antibiotic Resistance: A Latent Class Analysis of a Romanian Population. Int J Environ Res Public Health. 2022;19(12):7263. doi:10.3390/ijerph19127263. PMID: 35742513; PMCID: PMC9224212. Rajbhandari S, Devkota N, Khanal G, Mahato S, Paudel UR. Assessing the industrial readiness for adoption of industry 4.0 in Nepal: A structural equation model analysis. Heliyon. 2022;8(2). doi:10.1016/j.heliyon.2022.e08919. PMID: 35243054; PMCID: PMC8866891. Additional Declarations No competing interests reported. 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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-4767628","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330538679,"identity":"3b66e185-b546-4632-9399-440c46116a45","order_by":0,"name":"Jian Yang","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Yang","suffix":""},{"id":330538680,"identity":"bfbcd689-1044-48f3-a1d1-268c7270aeb2","order_by":1,"name":"Dan Qin","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Qin","suffix":""},{"id":330538681,"identity":"45fed02c-acd6-4dfa-a616-ba80fb9c27ba","order_by":2,"name":"Fan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACNobDxz98/GMjJ8/MfIA4LXyMx9IYZzakGRu2syUQp0WO+YwZM2/DocSG8zwGRDqM7YDZA94dB4wZm3k+3njDYCen20BIC8+BdAPJM3fk2Jl5N1vOYUg2NjtASIvEgQMSBmzPgLbwbpPmYTiQuI2gFvmHDRIJbIcTGw7zPCNSC8NhNomDbWAtbMRqOcZs2HAGGMjNbMaWcwyI8It8w/mPj/9UAKOS//DDG28q7OQIakEBEsRGDbIWUnWMglEwCkbBiAAAMudESt+PoNIAAAAASUVORK5CYII=","orcid":"","institution":"Kunming Medical University","correspondingAuthor":true,"prefix":"","firstName":"Fan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-07-19 11:02:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4767628/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4767628/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62482495,"identity":"9fb67017-0468-4b78-bf1e-ee107219bcf4","added_by":"auto","created_at":"2024-08-14 17:16:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":185764,"visible":true,"origin":"","legend":"\u003cp\u003eSurvey object distribution map\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4767628/v1/7fc16056739a8bf427240466.png"},{"id":62482493,"identity":"35bf3546-6186-4cf7-bc7c-0814fbc76fc0","added_by":"auto","created_at":"2024-08-14 17:16:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160189,"visible":true,"origin":"","legend":"\u003cp\u003eFit Results of the Structural Equation Model for healthcare professionals' CHS-DRG Payment Policy Knowledge, Attitudes, and Practices\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4767628/v1/5d4cc818e4d7278022fe6334.png"},{"id":62482494,"identity":"b5a4d40a-d3ca-42c2-b51c-6c4b6b0fbd24","added_by":"auto","created_at":"2024-08-14 17:16:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":219446,"visible":true,"origin":"","legend":"\u003cp\u003eThe KAP Model of CHS-DRG Payment Policy for Healthcare Professionalsand its Correlation with Demographic Characteristics\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4767628/v1/724cd43b36e0b290e9b1addc.png"},{"id":88066786,"identity":"c73b4520-d201-4fc7-a8cc-d5dc759e17a2","added_by":"auto","created_at":"2025-08-01 03:46:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2039591,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4767628/v1/c01a0261-9000-477d-a87b-8d3318a034cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Cross-Sectional Study on Healthcare Professionals' Knowledge, Attitudes, and Practices Regarding the CHS-DRG Payment Policy in Yunnan Province: A Structural Equation Modeling Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChina has been undergoing significant healthcare reforms to enhance quality and efficiency while reducing patient financial burden. As part of this reform, the government introduced the Diagnosis-Related Group (DRG) payment system, which aims to promote value-based healthcare by linking payments to care quality and outcomes rather than service volume\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The CHS-DRG payment policy is a vital component of broader healthcare system reform in China, emphasizing quality, safety, and efficiency. The traditional fee-for-service model, which reimburses hospitals based on service volume, has led to excessive healthcare service utilization without incentivizing providers to improve quality and outcomes. To address these issues, the CHS-DRG payment policy links payment to the value of care provided rather than the volume of services rendered\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn 2019, the Chinese government launched the China Hospital Substance DRG (CHS-DRG) payment policy, tailored to the unique characteristics of the Chinese healthcare system. Under this system, hospitals receive reimbursement based on standardized codes reflecting the severity of patients' conditions and the complexity of treatment\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. However, the implementation of the CHS-DRG payment policy has encountered challenges, particularly limited understanding and acceptance among healthcare professionals. Many providers are unfamiliar with the coding and documentation requirements of the CHS-DRG system, and resistance to changing established practices and procedures exists. Additionally, concerns about the policy's financial implications, such as potential reimbursement rate reductions for certain services, further hinder implementation\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe success of the CHS-DRG payment policy hinges on healthcare professionals' knowledge, attitudes, and practices (KAP) in its implementation. As one of the pioneering provinces to implement the CHS-DRG payment policy, Yunnan Province is the focus of this investigation. The study aims to examine the KAP of healthcare professionals in Yunnan Province medical institutions regarding the CHS-DRG payment policy while identifying factors influencing their acceptance and implementation\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Utilizing a cross-sectional design and structural equation modeling, data collected from healthcare professionals in Yunnan Province medical institutions will be analyzed.\u003c/p\u003e \u003cp\u003eThe study findings will contribute to a better understanding of the challenges and opportunities associated with implementing the CHS-DRG payment policy in China. Policymakers and healthcare professionals in other regions will benefit from these valuable insights. Ultimately, the study aims to facilitate the successful implementation of the CHS-DRG payment policy, improving the quality and efficiency of healthcare delivery across China.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research subjects and sampling\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eHealthcare professionals eligible for this cross-sectional study in Yunnan Province, China, conducted between May 2023 and June 2023, met specific criteria. The inclusion criteria focused on healthcare professionals with a minimum of six months' experience in hospitals who actively participated in the CHS-DRG payment policy implementation. Exclusion criteria were applied to exclude healthcare professionals who were on vacation or sick leave during the study period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Sampling Methodology\u003c/h2\u003e \u003cp\u003eA meticulously designed sampling methodology was implemented to ensure the robustness and representativeness of the study sample. To account for regional variations in healthcare practices, six cities within Yunnan Province were selected, ensuring geographical diversity by choosing hospitals from various regions within the province. The sample included hospitals of different sizes, encompassing both large tertiary hospitals and smaller community healthcare centers, which facilitated capturing perspectives from healthcare professionals across various types of healthcare institutions. To maintain sample representativeness, random sampling was conducted within each stratum, considering both geographical region and hospital size category.\u003c/p\u003e \u003cp\u003eThis rigorous sampling strategy culminated in the inclusion of a total of 357 healthcare professionals from 30 hospitals in the study. This diverse and systematically obtained sample provides comprehensive insights into healthcare professionals' knowledge, attitudes, and practices concerning the CHS-DRG payment policy. The adherence to such a rigorous and systematic sampling approach enhances the academic and practical value of the study findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Sample Size Determination\u003c/h2\u003e \u003cp\u003eTo determine the final sample size, single population pro portion formula was employed by taking an assumption of 95% CI, 5% margin of error, and 54.3% prevalence of CHS-DRG payment policy knowledge from a study done in Chongqing, Shaanxi and Xinjiang province\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:n=\\frac{\\left({{Z}_{a/2}}^{2}\\right)\\times\\:P\\times\\:\\left(1-P\\right)}{{d}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, n\u0026thinsp;=\u0026thinsp;required sample size, Zα/₂ is a critical value at 95% CI(1.96), and at 5% margin of error (d\u0026thinsp;=\u0026thinsp;0.05), and p is the prevalence of CHS-DRG payment policy knowledge\u0026thinsp;=\u0026thinsp;54.3%.\u003c/p\u003e \u003cp\u003eAccordingly,\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:n=\\frac{{1.96}^{2}\\times\\:0.543\\times\\:(1-0.543)}{{0.05}^{2}}\\)\u003c/span\u003e \u003c/span\u003e=381\u003c/p\u003e \u003cp\u003eHowever, the source population is less than 10,000 adjusted formulas were used.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:nf=\\frac{ni}{[1+\\frac{ni}{N}]}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere nf\u0026thinsp;=\u0026thinsp;final sample size, ni\u0026thinsp;=\u0026thinsp;initial sample size\u0026thinsp;=\u0026thinsp;381, and N\u0026thinsp;=\u0026thinsp;the source population\u0026thinsp;=\u0026thinsp;550.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:ni=\\frac{381}{1+\\frac{381}{550}}\\)\u003c/span\u003e \u003c/span\u003e=225\u003c/p\u003e \u003cp\u003eBy adding 10% of the non-response rate, it gives a final sample size of 245 people. The final sampled size is above 245.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research hypothesis\u003c/h2\u003e \u003cp\u003eThe KAP theory is often applied in health-related practices and public health fields. This theory believes that cognition is the basis for establishing positive and correct attitudes, which in turn is the driving force for changing related practices. Based on KAP theory and demographic characteristics, this paper proposes the following hypotheses: \u003cb\u003eHypothesis 1\u003c/b\u003e \u003cem\u003e(H1 and H1Δ): Knowledge of the CHS-DRG payment policy has a positive impact on attitudes.\u003c/em\u003e \u003cb\u003eHypothesis 2\u003c/b\u003e \u003cem\u003e(H2 and H2Δ): Attitudes toward the CHS-DRG payment policy has a positive impact on practices.\u003c/em\u003e \u003cb\u003eHypothesis 3\u003c/b\u003e \u003cem\u003e(H3 and H3Δ): Knowledge of the CHS-DRG payment policy has a positive impact on practices.\u003c/em\u003e \u003cb\u003eHypothesis 4\u003c/b\u003e \u003cem\u003e(H4 and H4Δ): Demographic characteristics have a positive impact on knowledge of CHS-DRG payment policy.\u003c/em\u003e \u003cb\u003eHypothesis 5\u003c/b\u003e \u003cem\u003e(H5 and H5Δ): Demographic characteristics have a positive impact on attitudes toward the CHS-DRG payment policy.\u003c/em\u003e \u003cb\u003eHypothesis 6\u003c/b\u003e \u003cem\u003e(H6 and H6Δ): Demographic characteristics have a positive impact on practices toward the CHS-DRG payment policy.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection\u003c/h2\u003e \u003cp\u003eData on healthcare professionals' knowledge, attitudes, and practices toward the CHS-DRG payment policy were collected using a self-administered questionnaire. The questionnaire was developed based on reviewing the literature and consulting with experts in the field. The questionnaire consisted of four parts: (1) demographic information, (2) knowledge of the CHS-DRG payment policy, (3) attitudes toward the CHS-DRG payment policy, and (4) practices regarding the CHS-DRG payment policy.\u003c/p\u003e \u003cp\u003eThe knowledge section consisted of six questions with one point awarded for each correct answer and zero points for incorrect or unknown answers, with a total score range of 0\u0026ndash;6 points. The attitudes section consisted of six questions, with one point awarded for \"negative\" or \"do not know\", two points for \"neutral\" or \"maybe\", and three points for \"positive\" or \"definitely\", with a total score range of 6\u0026ndash;18 points. The practices section consisted of seven questions, with scores assigned based on option levels and a total score range of 7\u0026ndash;28 points.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cp\u003eThe data analysis and statistical description were conducted using SPSS 27.0, while the structural equation model (SEM) was constructed and analyzed using AMOS 26.0. Descriptive statistics were used to analyze the demographic characteristics of the participants. The mean and standard deviation of knowledge, attitudes, and practices were calculated. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to construct the SEM and test the hypothesized relationships between variables. Model fit was evaluated using the chi-square test, comparative fit index (CFI), Tucker‒Lewis index (TLI), incremental fit index (IFI), and root mean square error of approximation (RMSEA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Factor Analysis and Validity Measures in Research\u003c/h2\u003e \u003cp\u003eExploratory factor analysis (EFA) is used to test whether the number of factors extracted from the scale is consistent with the scale dimensions, and whether the question items corresponding to each common factor are consistent with the question items included in each dimension of the scale.\u003c/p\u003e \u003cp\u003eConfirmatory factor analysis (CFA) is a research method used to measure whether the correspondence between factors and items is consistent with the researcher's prediction. CFA mainly validates the structural validity, convergent validity, and discriminant validity of the model. AVE is the Average of variance extracted, and CR is the composite reliability, both of them are used to validate the convergent validity, and in general, if the AVE is greater than 0.5 or CR is greater than 0.7, the convergent validity of factors and variables is indicated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Structural equation modeling principles\u003c/h2\u003e \u003cp\u003eStructural equation modelling (SEM) is a widely used multivariate technique for investigating the direct and indirect effect of relationships between observed and latent variables. In SEM, an observed variable is a variable that has been directly measured and latent variables are unobserved variables that cannot be measured directly. The theory of SEM simplifies complex relationships between variables by utilizing a path model or analysis for explaining effects resulting from observed and latent variables\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The SEM summarises a linear structural relationship into a measurement model (CFA model) and structural model. The measurement model is defined as:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{x}_{1}={{\\Lambda\\:}}_{1}\\eta\\:+{\\epsilon\\:}_{1}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{x}_{2}={\\varLambda\\:}_{2}\\xi\\:+{\\epsilon\\:}_{2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{1}(r\\times\\:1)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{2}(s\\times\\:1)\\)\u003c/span\u003e\u003c/span\u003e are random vectors of the observed variables that explains endogenous (dependent) η and exogenous (independent) ξ latent variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varLambda\\:}_{1}(r\\times\\:{q}_{1})\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varLambda\\:}_{2}(s\\times\\:{q}_{2})\\)\u003c/span\u003e\u003c/span\u003e are loading matrices, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{1}(r\\times\\:1)\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{2}(s\\times\\:1)\\)\u003c/span\u003e\u003c/span\u003e are random vectors of measurement errors. The measurement model can also be expressed in matrix form as:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:y=\\left(\\begin{array}{c}{x}_{1}\\\\\\:{x}_{2}\\end{array}\\right)=\\left(\\begin{array}{cc}{\\varLambda\\:}_{1}\u0026amp;\\:0\\\\\\:0\u0026amp;\\:{\\varLambda\\:}_{2}\\end{array}\\right)\\left(\\begin{array}{c}\\eta\\:\\\\\\:\\xi\\:\\end{array}\\right)+\\left(\\begin{array}{c}{\\epsilon\\:}_{1}\\\\\\:{\\epsilon\\:}_{2}\\end{array}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOn the other hand, the structural model can be defined as:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:\\eta\\:=B\\eta\\:+\\varGamma\\:\\xi\\:+\\delta\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:B({q}_{1}\\times\\:{q}_{2})\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varGamma\\:({q}_{1}\\times\\:{q}_{2})\\)\u003c/span\u003e\u003c/span\u003e are unknown matrices of regression coefficients that describes the cause-and-effect relationship between η and ξ, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\delta\\:({q}_{1}\\times\\:1)\\)\u003c/span\u003e\u003c/span\u003e is a random vector of residuals or error of endogenous variables. Thus, the SEM is expressed as a CFA model whose latent factors represent a linear structural equation. The model aids in finding a direct and indirect effect among variables and the measurement errors. Statistically, SEM depicts an extended form of a generalized linear model like the multiple regression model and analysis of variance\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe performance of the measurement and structural model was evaluated using root mean square error approximation (RMSEA), comparative fit index (CFI), tucker-lewis index (TLI), and incremental fit index (IFI). The RMSEA, CFI, TLI and IFI can be expressed as :\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:RMSEA=\\sqrt{\\frac{\\text{max}\\left({\\chi\\:}_{k}^{2}-d{f}_{k},0\\right)}{d{f}_{k}\\left(N-1\\right)}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:CFI=\\frac{\\text{max}\\left({\\chi\\:}_{0}^{2}-d{f}_{0},0\\right)-\\text{max}\\left({\\chi\\:}_{k}^{2}-d{f}_{k},0\\right)}{\\text{max}\\left({\\chi\\:}_{0}^{2}-d{f}_{0},0\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$\\:TLI=\\frac{\\frac{{\\chi\\:}_{0}^{2}}{d{f}_{0}}-\\frac{{\\chi\\:}_{k}^{2}}{d{f}_{k}}}{\\frac{{\\chi\\:}_{0}^{2}}{df}-1}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equj\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equj\" name=\"EquationSource\"\u003e\n$$\\:IFI=(Tb-Tm)/(Tb-dfm)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.1 Demographic characteristics\u003c/h2\u003e\n \u003cp\u003eThe Cronbach\u0026apos;s \u0026alpha; coefficient of the self-developed questionnaire in this study was 0.838(Cronbach\u0026apos;s alpha, Internal consistency. \u0026alpha;\u0026thinsp;\u0026ge;\u0026thinsp;0.9, Excellent. 0.9\u0026thinsp;\u0026gt;\u0026thinsp;\u0026alpha;\u0026thinsp;\u0026ge;\u0026thinsp;0.8, Good. 0.8\u0026thinsp;\u0026gt;\u0026thinsp;\u0026alpha;\u0026thinsp;\u0026ge;\u0026thinsp;0.7, Acceptable. 0.7\u0026thinsp;\u0026gt;\u0026thinsp;\u0026alpha;\u0026thinsp;\u0026ge;\u0026thinsp;0.6, Questionable. 0.6\u0026thinsp;\u0026gt;\u0026thinsp;\u0026alpha;\u0026thinsp;\u0026ge;\u0026thinsp;0.5), and the KMO value was 0.894༈KMO values\u0026thinsp;\u0026gt;\u0026thinsp;0.9, highly suitable; \u0026gt; 0.8, suitable; \u0026gt;0.7, acceptable; \u0026gt;0.6 marginally usable; \u0026gt; 0.5 not suitable༉, indicating good reliability and validity of the questionnaire. The majority of the study participants were from medical institutions in Yunnan Province (Fig. \u003cspan\u003e1\u003c/span\u003e), and their demographic characteristics are shown in Table \u003cspan\u003e1\u003c/span\u003e. Among the 357 healthcare professionals who participated in the study, 68.6% were female, and 49.3% were aged between 26 and 35 years old. The majority of the participants were clinical pharmacists (39.5%), followed by physicians (26.1%) and nurses (21.3%). All participants had a college degree or higher, with 74.8% being undergraduate students. In terms of professional and technical titles, 31.1% were intermediate-level, 29.1% were associate/senior assistants, and 17.4% were deputy directors or above. A total of 59.4% of the participants were from tertiary comprehensive hospitals (including traditional Chinese medicine hospitals), followed by secondary comprehensive hospitals, accounting for 34.7%.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDemographic characteristics of the participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026ndash;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u0026ndash;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubtropical high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivision level/Assistant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScholar level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDoctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePharmacist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical insurance department staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUndergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecialist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospital nature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThird level comprehensive hospitals (including traditional Chinese medicine hospitals)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary comprehensive hospitals (including traditional Chinese medicine hospitals)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraditional Chinese Medicine Specialized Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity Health Service Center (including service stations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTownship health centers (including clinics)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u0026ndash;15 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 years and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e3.2 Scores of knowledge, attitudes, and practices\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTable 2 presents the mean scores and standard deviations for knowledge, attitudes, and practices regarding the CHS-DRG payment policy. The mean knowledge score was 4.01 (SD = 2.80), with an overall awareness rate of 66.83%, indicating insufficient understanding of the CHS-DRG payment policy among the participants. The mean attitudes score was 13.39 (SD = 3.38), with an overall positive attitudes rate of 74.39%, indicating that the participants held a positive attitudes toward the CHS-DRG payment policy. The mean practices score was 18.59 (SD = 6.41), with an overall compliance rate of 66.39%, indicating a low level of compliance with the CHS-DRG payment policy among the participants.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eMean scores and standard deviations of knowledge, attitudes, and practices\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e13.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e18.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eAbbreviations list: SD: Standard deviation;\u003c/p\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003e3.3 EFA and CFA analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn this study, we employed both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to construct a Knowledge, Attitudes, and Practices (KAP) model to gauge healthcare professionals\u0026apos; perspectives on the CHS-DRG payment policy.\u003c/p\u003e\n \u003cp\u003eTable 3 delineates the variables to be assessed in this study, encompassing three latent variables: knowledge, attitudes, practices, demographic characteristics, along with 24 observational variables. These observational variables are denoted as K1-K6, A1-A6, P1-P6、P8, and Q2、Q4-Q7\u003csup\u003e[12]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eExploratory Factor Analysis (EFA)\u003c/strong\u003e revealed the presence of four common factors, denoted as F1, F2, F3, and F4. The cumulative variance contribution exceeded the 50% threshold, reaching 62.1%, indicating that these four factors adequately encapsulated the underlying information. With the exception of items A3 and Q4, all other items exhibited factor loadings exceeding 0.4, highlighting their significant association with their respective factors. Furthermore, the factor congruence of part K exceeded 0.7, signifying robust internal consistency within this subset (refer to Table4).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eConfirmatory Factor Analysis (CFA)\u0026nbsp;\u003c/strong\u003eresults, as presented in Table 5, showcased that standardized loadings were generally within the range of 0.3 to 0.9 (p \u0026lt; 0.001). To evaluate construct validity, it\u0026apos;s essential that the Average Variance Extracted (AVE) exceeds 0.5, and the Composite Reliability (CR) surpasses 0.7 (Hair et al., 2014). For the knowledge and practices factors related to the CHS-DRG payment policy, both AVE and CR surpassed these thresholds, indicating strong measurement indicator extraction. However, the AVE for the attitudes and demographic characteristics factors fell below 0.5, but their CR values exceeded 0.7, indicating good extraction of measurement indicators within these factors (refer to Table 5).\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLatent variables and observed variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elatent variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObserved Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eKnowdelge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eK1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEver understood of CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eK2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrincipal characteristics of CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eK3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge regarding the scope of applicability of CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eK4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUtilization methods of CHS-DRG payment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eK5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHS-DRG payment policy impacts medical practices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eK6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining related to the CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttitudes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes toward the CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnhance healthcare quality by CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncrease the workload by CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReduce healthcare costs by CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImprove patient satisfaction by CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnhance the efficiency of medical practices by CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ePractices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsage of the CHS-DRG payment policy in daily work\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsider the CHS-DRG payment policy in decision-making process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProvide feedback on the implementation of the CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunicate with patients and their families regarding issues related to the CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipate in discussions or training related to the CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrictly adhere to the CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConfidence and ability to effectively implement the CHS-DRG payment policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthcare professional\u0026rsquo;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthcare professional\u0026rsquo;s position\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthcare professional\u0026rsquo;s professional and technical qualifications\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe hospital\u0026nbsp;nature of healthcare professional\u0026rsquo;s work\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthcare professional\u0026rsquo;s working experience\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eResults of exploratory factor analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePaths\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eResults of confirmatory factor analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePaths\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandardized Regression Weights\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"6\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"6\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"6\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"6\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eAbbreviations list: AVE: Average of variance extracted; CR: composite reliability\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e3.4 SEM analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.4.1 Fitting Results of the Healthcare Professionals\u0026apos; CHS-DRG Payment Policy KAP Model\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDrawing from the outcomes of both EFA and CFA, as well as the underlying hypotheses, we constructed a Structural Equation Model (SEM) to elucidate the knowledge, attitudes, and practices of healthcare professionals concerning the CHS-DRG payment policy (see Figure 2). The SEM analysis affirmed the model\u0026apos;s appropriateness (refer to Table 6). The \u0026chi;2/df (chi-square statistics to degrees of freedom) ratio, standing at 3.029, falls within the generally acceptable range, lending substantial support to the model. Furthermore, critical fit indices, including CFI (comparative fit index) at 0.926, IFI (incremental fit index) at 0.927, and TLI (Tucker\u0026ndash;Lewis index) at 0.915, all exceeded the requisite minimum threshold of 0.90. While the RMSEA (root mean square error of approximations) slightly surpassed the suggested level of 0.05, it remained below the upper limit of 0.08 (Hair et al., 2014)\u003csup\u003e[13]\u003c/sup\u003e, reinforcing model adequacy.\u003c/p\u003e\n \u003cp\u003eThe results presented in Table 7 indicate the significant influence of both knowledge and attitudes on the practices of the CHS-DRG payment policy (\u0026beta; = 0.36, p \u0026lt; 0.001 and \u0026beta; = 0.46, p \u0026lt; 0.001, respectively), thereby confirming hypotheses H3 and H2. Notably, the direct impact of attitudes on practices outweighed that of knowledge on practices. Additionally, knowledge regarding the CHS-DRG payment policy exhibited a positive influence on attitudes (\u0026beta; = 0.36, p \u0026lt; 0.001), corroborating hypothesis H1.\u003c/p\u003e\n \u003cp\u003eFurthermore, the mediation analysis (refer to Table 8) demonstrated that knowledge had both a direct (\u0026beta; = 0.934, p = 0.001) and an indirect impact on practices through its influence on attitudes (\u0026beta; = 0.462, p = 0.001), resulting in a total effect of 1.396.\u003c/p\u003e\n \u003cp\u003eThese findings provide a comprehensive understanding of the relationships between knowledge, attitudes, and practices with respect to the CHS-DRG payment policy among healthcare professionals.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFit Index for the Structural Equation Model of CHS-DRG Payment Policy Knowledge, Attitudes, and Practices for healthcare professionals\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFit Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e/df\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFit Standards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFit Results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eAbbreviations list: RMSEA: root mean square error of approximation; IFI: incremental fit index; TLI: Tucker‒Lewis index; CFI: comparative fit index;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 7\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePath Coefficients for the KAP Model of CHS-DRG Payment Policy for healthcare professionals\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePaths\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.E.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC.R.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLabel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eAbbreviations list: S.E.: standard error; C.R.: critical ratio;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 8\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMediating Effects of CHS-DRG Payment Policy Knowledge and Practices for healthcare professionals\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndirect effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e3.4.2 Influence of Demographic Characteristics on Healthcare Professionals\u0026apos; CHS-DRG Payment Policy KAP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn order to delve deeper into the influence of demographic characteristics on healthcare professionals\u0026apos; knowledge, attitudes, and practices regarding the CHS-DRG payment policy, we incorporated these characteristics as a latent variable in the CHS-DRG payment policy KAP model. This encompassed age, job position, professional and technical title, nature of employing institution, and years of work as observed variables.\u003c/p\u003e\n \u003cp\u003eThe \u0026chi;\u0026sup2;/df stands at 2.661, and the RMSEA registers at 0.068, both indicative of a robust model fit, bolstering overall model support. Additionally, critical fit indices - CFI and IFI at 0.917, and TLI at 0.907 - surpass the requisite threshold of 0.90, affirming the adequacy of the model.\u003c/p\u003e\n \u003cp\u003eThe outcomes of the model (depicted in Figure 3, detailed in Table 9) underscore that demographic characteristics wield a significant positive impact on healthcare professionals\u0026apos; knowledge of the CHS-DRG payment policy (p \u0026lt; 0.001), thereby validating hypothesis H4. However, it is noteworthy that demographic characteristics exert a negative influence on attitudes and practices, thereby rejecting hypotheses H5 and H6. With the exception of job position, all other demographic characteristics manifest a substantial positive impact on healthcare professionals\u0026apos; knowledge, attitudes, and practices pertaining to the CHS-DRG payment policy (p \u0026lt; 0.001). Of particular note, Q2 (age) and Q7 (working experience) emerge as the variables with the most pronounced impact values (refer to Table 10) .\u003c/p\u003e\n \u003cp\u003eThese findings collectively illuminate the intricate interplay between demographic characteristics and healthcare professionals\u0026apos; comprehension, disposition, and actions regarding the CHS-DRG payment policy.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 9\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFit Index for the KAP Model of CHS-DRG Payment Policy for Healthcare Professionals and its Correlation with Demographic Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFit Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e/df\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFit Standards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026gt;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFit Results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eAbbreviations list: RMSEA: root mean square error of approximation; IFI: incremental fit index; TLI: Tucker‒Lewis index; CFI: comparative fit index;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e \u0026nbsp;\u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 10\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePath Coefficients for the KAP Model of CHS-DRG Payment Policy for Healthcare Professionals and its Correlation with Demographic Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePaths\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.E.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC.R.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLabel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH4\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH1\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH5\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH3\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitudes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH2\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePractices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH6\u0026Delta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eAbbreviations list: S.E.: standard error; C.R.: critical ratio;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Knowledge about the CHS-DRG payment policy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study found that overall, healthcare professionals in Yunnan Province have a moderate level of knowledge about the CHS-DRG payment policy. This suggests that there is a need for more education and training for healthcare professionals to increase their understanding of the policy. The study also found that healthcare professionals who had received training on the CHS-DRG payment policy had higher levels of knowledge about the policy than those who had not received such training. This highlights the importance of training and education for healthcare professionals to increase their understanding of the policy\u003csup\u003e[14-15]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Attitudes toward the CHS-DRG payment policy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study found that healthcare professionals in Yunnan Province generally have positive attitudes toward the CHS-DRG payment policy. This suggests that healthcare professionals are receptive to the idea of value-based healthcare and are willing to adopt new practices and procedures to support the policy. However, the study also found that some healthcare professionals were concerned about the financial implications of the policy, as it may reduce the reimbursement rates for some services. This highlights the need for policymakers to address the financial concerns of healthcare professionals and to ensure that the policy does not adversely affect the financial viability of medical institutions\u003csup\u003e[16-17]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Practices related to the CHS-DRG payment policy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study found that healthcare professionals in Yunnan Province had varying levels of practices related to the CHS-DRG payment policy. While some healthcare professionals had already adopted the new practices and procedures required by the policy, others were still using the old practices and procedures. This suggests that there is a need for more education and training for healthcare professionals to ensure that they are using the new practices and procedures required by the policy. The study also found that some healthcare professionals were concerned about the additional workload created by the policy. This highlights the need for policymakers to ensure that the policy is implemented in a way that minimizes the additional workload for healthcare professionals\u003csup\u003e[18-19]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Demographic Characteristics and Their Nonsignificant Influence on CHS-DRGs Knowledge, Attitudes, and Practices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study aimed to investigate the relationship between demographic characteristics and healthcare professionals\u0026apos; KAP regarding the CHS-DRG payment policy using a structural equation modeling (SEM) approach. Surprisingly, the results demonstrated that demographic factors such as education, hospital level, professional title, job position, work experience, and age had no significant impact on attitudes and practices toward CHS-DRGs. Possible reasons for these findings include the current training and education system not adequately addressing the CHS-DRG payment policy and the fact that job position, work experience, and age may not directly influence one\u0026apos;s understanding, attitudes, and practices toward the policy\u003csup\u003e[20-21]\u003c/sup\u003e. Healthcare professionals\u0026apos; KAP might be shaped by a myriad of factors, including personal beliefs, professional values, and organizational culture, which may not necessarily be associated with one\u0026apos;s position, experience, or age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Barriers to the implementation of the CHS-DRG payment policy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study identified several barriers to the successful implementation of the CHS-DRG payment policy, including a lack of understanding and knowledge, concerns about financial implications, and additional workload. These issues highlight the need for policymakers to address healthcare professionals\u0026apos; concerns and provide education and training to enhance understanding and acceptance of the policy\u003csup\u003e[22]\u003c/sup\u003e . To improve diagnostic and treatment efficiency and quality, the CHS-DRG payment policy should be integrated into clinical practices and continuously optimized. For example, developing coding for traditional Chinese medicine diagnosis can better meet the healthcare needs of the Chinese population. Strengthening the supervision of medical institutions can prevent misuse of the DRG policy, increasing efficiency and fairness. In optimizing the policy, healthcare professionals\u0026apos; opinions and proposals should be fully considered to ensure reasonable, scientific, and feasible implementation\u003csup\u003e[23-24]\u003c/sup\u003e .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6 Implications for policymakers and healthcare professionals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address the insufficient understanding and implementation of the CHS-DRG payment policy, it is crucial to incorporate CHS-DRG payment policy and value-based healthcare concepts into medical education and continuous professional development programs. This would not only enhance healthcare professionals\u0026apos; understanding of the policy but also promote a more positive attitudes and proactive practices toward its implementation. Additionally, healthcare organizations should encourage employees across all job positions and experience levels to adopt the CHS-DRG payment policy, aiming for a uniform KAP among their workforce.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe CHS-DRG payment policy aims to promote value-based healthcare in China. This study found that healthcare professionals in Yunnan Province have moderate knowledge and generally positive attitudes toward the policy. Knowledge and attitudes significantly influence their practices. However, several challenges hinder successful implementation. Policymakers and healthcare professionals must collaborate to address these challenges and ensure effective policy implementation. Education and training programs may improve understanding and implementation. Future studies should use longitudinal designs to track changes in knowledge, attitudes, and practices, and to evaluate the policy\u0026apos;s impact on healthcare outcomes and costs.\u003c/p\u003e\n"},{"header":"Limitations of the study","content":"\u003cp\u003eThere are several limitations to this study that need to be acknowledged. First, the study was conducted in Yunnan Province, which may limit the generalizability of the findings to other regions of China. Second, the study used a cross-sectional design, which limits the ability to establish causality between the variables. Finally, the study relied on self-reported data, which may be subject to social desirability bias.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003eCHS-DRG: China Healthcare Security Diagnosis Related Groups; KAP: Knowledge, Attitudes, and Practices; SEM: Structural Equation Modeling; ADRs: Adverse drug reactions; RMSEA: Root mean square error of approximation; IFI: Incremental fit index; TLI: Tucker‒Lewis index; CFI: Comparative fit index; CFA: Confirmatory factor analysis; EFA: Exploratory factor analysis; KMO: Kaiser-Meyer-Olkin; CR: Composite reliability; AVE: Average of variance extracted; S.E.: Standard Error; AGFI: Adjusted Goodness-of-Fit Index; \u0026chi;2/df: Chi-square statistics to degrees of freedom.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted with ethical approval from the biomedical ethics committee of Kunming Medical University, Kunming, Yunnan, China[NO.2023\u0026ndash;0107]. Information about the study\u0026rsquo;s objectives and contents, and the right to refuse participation, was provided to all participants before data collection. Written informed consent was obtained from all participants, and for those under 16 years old, consent was obtained from their legal guardians. All methods followed relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe, the authors of \u0026quot;A Cross-Sectional Study on Healthcare Professionals\u0026apos; Knowledge, Attitudes, and Practices Regarding CHS-DRG Payment Policy in Yunnan Province,\u0026quot; consent to its publication. All authors have approved the final manuscript, which is original and not submitted elsewhere. We affirm the accuracy of the data and results and have disclosed any conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by [the bureau of Yunnan Healthcare Security] with the subject of \u0026quot;A Study on Strategies to Promote the Development of the Biopharmaceutical Industry in Yunnan Province with a Focus on Health Insurance Policies\u0026quot; entrusted by, project number 202001007.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJY conceived and designed the study; DQ performed the data analyses and survey; DQ wrote the manuscript; JY provided critical revisions; FL approved the final version. Jian Yang and Dan Qin are co-first authors of this study, having made equal contributions to the research and writing of the manuscript. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all researchers who participated in the questionnaire survey in Yunnan, China.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi Y, Feng X, Ma X, et al. Analysis of the current situation of knowledge, belief, and practice in safe medication among women in China using a multi-group structural equation model. Chinese Journal of Pharmacy. 2022;57(12):966-971.\u003c/li\u003e\n\u003cli\u003eHua T, Zhang S, Fu B, et al. 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PMID: 35243054; PMCID: PMC8866891.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CHS-DRG payment policy, value-based healthcare, knowledge attitudes and practices (KAP), structural equation modeling, healthcare professionals, Yunnan Province","lastPublishedDoi":"10.21203/rs.3.rs-4767628/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4767628/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis cross-sectional study aimed to examine the knowledge, attitudes, and practices of healthcare professionals in Yunnan Province, China, regarding the China Healthcare Security Diagnosis Related Groups (CHS-DRG) payment policy in the context of value-based healthcare using a structural equation model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study was conducted among healthcare professionals in Yunnan Province from March 2023 to May 2023. A self-administered questionnaire was used to collect data on knowledge, attitudes, and practices regarding the CHS-DRG payment policy. Structural equation modeling (SEM) was used to test the hypothesized relationships among the variables. Data analysis and statistical description were performed using SPSS 27.0, and structural equation modeling (SEM) was constructed and analyzed using AMOS 26.0.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 357 healthcare professionals from 30 medical institutions participated in the study. The results of the SEM analysis showed that knowledge and attitudes significantly influenced the practices regarding the CHS-DRG payment policy (β\u0026thinsp;=\u0026thinsp;0.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and β\u0026thinsp;=\u0026thinsp;0.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 respectively), and the direct effect of attitudes on practices was stronger than that of knowledge on practices. Moreover, knowledge not only directly affected practices (β\u0026thinsp;=\u0026thinsp;0.934, p\u0026thinsp;=\u0026thinsp;0.001) but also indirectly influenced practices through attitudes (β\u0026thinsp;=\u0026thinsp;0.462, p\u0026thinsp;=\u0026thinsp;0.001), with a total effect of 1.396. In addition, demographic characteristics had a positive impact on healthcare professionals' CHS-DRG payment policy knowledge (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study reveals that healthcare professionals in Yunnan Province show moderate knowledge, positive attitudes, and influenced practices toward the CHS-DRG payment policy. Challenges and barriers exist, requiring collaborative efforts from policymakers and healthcare professionals to ensure effective implementation. Targeted interventions and longitudinal studies are recommended to improve understanding, promote value-based healthcare, and evaluate outcomes and costs.\u003c/p\u003e","manuscriptTitle":"A Cross-Sectional Study on Healthcare Professionals' Knowledge, Attitudes, and Practices Regarding the CHS-DRG Payment Policy in Yunnan Province: A Structural Equation Modeling Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-14 17:16:05","doi":"10.21203/rs.3.rs-4767628/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":"b40ad4aa-0fca-4177-b09d-f6213337aaf9","owner":[],"postedDate":"August 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-01T03:38:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-14 17:16:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4767628","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4767628","identity":"rs-4767628","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}