Assessing Social Support's Mediating Effects on Rational Antimicrobial Prescribing: A Structural Equation Modeling Study of Healthcare Professionals' Behavior

Assessing Social Support's Mediating Effects on Rational Antimicrobial Prescribing: A Structural Equation Modeling Study of Healthcare Professionals' Behavior | 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 Article Assessing Social Support's Mediating Effects on Rational Antimicrobial Prescribing: A Structural Equation Modeling Study of Healthcare Professionals' Behavior Le Han, Xiao Liu, Yue Chang, Ying Liu, Lijun Ma, Jie Ren, Yunshuang Liu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4140928/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Importance: The study addresses the critical issue of antimicrobial resistance, focusing on the role of social support in influencing healthcare professionals' antimicrobial prescribing behavior, a key factor in clinical practice and public health. Objective: The primary objective is to examine the mediating effects of social support on the decision-making processes of healthcare professionals regarding antimicrobial drug use, emphasizing the impact on rational prescribing within a healthcare setting. Evidence Review: The study employed a cross-sectional survey design, analyzing data from 720 healthcare professionals using Structural Equation Modeling. It reviewed how variables such as self-efficacy, knowledge and skills, and health beliefs, informed by theories like RAT, TPB, and HBM, mediate the influence of social support. Findings: The SEM analysis demonstrated significant mediating effects of social support on prescribing intentions through various psychosocial factors. The results offer quantitative insights into the relationships between social support and critical psychological determinants of prescribing behavior. Conclusions and Relevance: The findings elucidate the nuanced impact of social support on antimicrobial prescribing decisions, providing evidence-based insights for enhancing antimicrobial stewardship. This study informs clinicians and policymakers about the significance of social support in promoting rational antimicrobial use. Health sciences/Diseases/Infectious diseases Biological sciences/Psychology/Human behaviour Introduction The discovery and widespread use of antimicrobial drugs marks a pivotal milestone in modern medicine, significantly improving the treatment of infectious diseases and saving millions of lives worldwide. However, the misuse and improper use of these drugs have led to the rapid development of antimicrobial resistance (AMR), now one of the gravest challenges to the global public health system. [ 1 ][ 2 ] The escalation of AMR not only renders previously manageable infections difficult to control but also significantly escalates healthcare costs and poses a substantial threat to global public health security. [ 3 ][ 4 ] In response, the World Health Organization (WHO) has launched a global action plan underscoring the critical need for improved regulation and management of antimicrobial drug use, heightened awareness among both the public and healthcare professionals, and advocacy for the judicious use of antimicrobial drugs [ 1 ] . At the national level, many countries have implemented stringent antimicrobial drug management policies, which include, but are not limited to, the development of more precise clinical guidelines, training for medical personnel on rational drug use, monitoring of antimicrobial drug usage, and targeted public health education campaigns [ 5 , 6 ] . Besides macro-level efforts, research focusing on the various factors influencing healthcare professionals' prescribing behaviors aims to enhance their knowledge, attitudes, and practice behaviors towards the rational use of antimicrobial drugs, thereby promoting rational drug use [ 7 , 8 ] . Despite these multifaceted measures, healthcare professionals, particularly in resource-limited developing countries, continue to face numerous challenges in the rational use of antimicrobial drugs. These challenges range from a lack of access to the latest clinical guidelines to pressures from patients for immediate treatment, a dearth of awareness about the consequences of antimicrobial drug resistance, and various systemic limitations within the healthcare infrastructure [ 9 , 10 ] .Thus, effectively addressing the irrational use of antimicrobial drugs requires not only enhancing the professional knowledge and skills of healthcare workers but also a deep understanding and analysis of the psychosocial factors underlying their prescribing behaviors. This study employs social support theory (SST) [ 11 ] . Rational Action Theory (RAT) [ 12 ] . Theory of Planned Behavior (TPB) [ 13 ] . Self-Efficacy Theory (SET)[14], Health Belief Model (HBM) [ 15 ] ,Knowledge and Skills (KS) [ 16 ] , and Cognitive Processing (CP) [ 17 ] models to delve into the psychological motivations behind healthcare professionals' behavior regarding the rational use of antimicrobial drugs. By employing a Likert five-point scale tailored to each theoretical construct and quantitatively analyzing healthcare professionals' behavioral motivations towards the rational use of antimicrobial drugs, this study utilizes Structural Equation Modeling (SEM) [ 18 ] to explore the relationships between these theoretical constructs and the rational use of antimicrobial drugs by healthcare professionals. This comprehensive theoretical framework and quantitative methodology offer a holistic perspective on the behavioral motivations and decision-making processes of healthcare professionals concerning antimicrobial drug use, providing robust theoretical and empirical support for developing effective intervention measures. Methodology 2.1 Scale Development This study employed a multidimensional approach to understand the psychological attitudes and behavioral motivations of healthcare professionals regarding the rational use of antimicrobial drugs. Drawing upon the foundations of Social Support Theory, Rational Action Theory, Theory of Planned Behavior, Cognitive Processing Theory, Health Belief Model, and the constructs of Knowledge and Skills, we designed a comprehensive Likert scale. The scale comprises eight dimensions with four items each, totaling 32 items aimed at evaluating the multifaceted factors influencing healthcare professionals' behaviors. The scale development was a collaborative effort by an interdisciplinary team of experts, including clinical medicine specialists, epidemiologists, health educators, and psychologists. The content validity of the scale was ensured through a rigorous expert review process, evaluating the relevance, representativeness, and clarity of the items. Following necessary revisions, the scale received validation from the expert panel, affirming its effectiveness in reflecting the study constructs and accurately measuring the target population's psychological motivations and behavioral tendencies. 2.2 Data Collection A cross-sectional survey was conducted from January 8 to February 7, 2024, targeting healthcare professionals at a tertiary hospital in Beijing's urban area. Questionnaires were distributed and collected using the hospital's internal survey system, adhering to strict anonymity principles to protect participants' privacy. No identifiable information was collected. Target Population Criteria: Age: Participants must be over 18 years old. Nationality: Participants must hold Chinese citizenship. Employment: Participants must be frontline healthcare professionals employed at the hospital. Language: Participants must possess proficient oral, written, and reading comprehension skills in Chinese. Exclusion Criteria: Cognitive Impairment: Individuals showing signs of cognitive impairment were excluded. Communication Barriers: Participants with significant communication difficulties due to disabilities (e.g., blindness or deafness) were also excluded. 2.3 Sample Size Calculation Utilizing G*Power 3.1 software, the sample size was pre-calculated to ensure sufficient statistical power to detect the expected effects. Assuming a medium effect size (f² = 0.15), a Type I error rate of 0.05, and a power (1-β error probability) of 0.95 [ 19 ] , the minimum required sample size was determined to be 267 participants. Accounting for a potential 20% dropout rate, the adjusted minimum sample size is 290 participants. 2.4 Statistical Analysis Structural Equation Modeling (SEM) using AMOS 23.0 software was employed to test the hypothesized models. The measurement model was first validated through Confirmatory Factor Analysis (CFA) to ensure items accurately reflected their corresponding latent variables. [ 18 ] SEM analysis then assessed the mediating models proposed in the hypotheses, examining path coefficients among independent, mediating, and dependent variables. Model fit was evaluated using various fit indices, including the chi-square (χ²) statistic, degrees of freedom (df), χ²/df ratio, Comparative Fit Index (CFI), Normed Fit Index (NFI), Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), and Root Mean Square Error of Approximation (RMSEA). Research Hypotheses Model 1: Hypothesis 1 (H1): Social support (SS) positively influences healthcare professionals' self-efficacy (SET). Hypothesis 2 (H2): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through self-efficacy (SET). Hypothesis 3 (H3): Social support (SS) positively influences healthcare professionals' rational action (RAT). Hypothesis 4 (H4): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through rational action (RAT). Model 2: Hypothesis 5 (H1): Social support (SS) positively influences healthcare professionals' knowledge and skills (KS). Hypothesis 6 (H2): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through knowledge and skills (KS). Hypothesis 7 (H3): Social support (SS) positively influences healthcare professionals' cognitive processing (CP). Hypothesis 8 (H4): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through cognitive processing (CP). Model 3: Hypothesis 9 (H1): Social support (SS) positively influences healthcare professionals' knowledge and skills (KS). Hypothesis 10 (H2): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through knowledge and skills (KS). Hypothesis 11 (H3): Social support (SS) positively influences healthcare professionals' health beliefs (HBM). Hypothesis 12 (H4): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through health beliefs (HBM). Model 4: Hypothesis 13 (H1): Social support (SS) positively influences healthcare professionals' planned behavior (TPB). Hypothesis 14 (H2): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through planned behavior (TPB). Hypothesis 15 (H3): Social support (SS) positively influences healthcare professionals' cognitive processing (CP). Hypothesis 16 (H4): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through cognitive processing (CP). Results Demographics The survey successfully collected 720 responses from healthcare professionals across various demographics, including gender, age, work experience, education, and departmental affiliation, as illustrated in Table 1 . The gender distribution showed a significant female majority, with women constituting 81.1% and men 18.9%, indicating a higher participation rate among female healthcare professionals in this study. The age group of 31–40 years old was the most represented at 37.4%, followed by the 41–50 age group at 30.8%. The distribution suggests a concentration of participants in the mid-career stage. In terms of work experience, individuals with 11–20 years in the field accounted for the highest proportion at 37.4%, indicating a significant number of participants with considerable professional experience. Those with over 20 years of experience represented 26.9%, while newcomers with less than 5 years accounted for 16.9%. Regarding educational levels, a majority held a bachelor's degree (57.2%), followed by master's degree holders (17.1%), indicating a generally high educational standard among respondents. Participation varied significantly across departments, with internal medicine leading at 32.5%, suggesting higher engagement in the survey from this specialty. Surgery and otorhinolaryngology followed with 21.1% and 16.4%, respectively, while emergency medicine had a participation rate of 13.9%. Departments like ophthalmology (9.7%), pediatrics (6.0%), and obstetrics and gynecology (0.4%) showed lower engagement, possibly reflecting the distribution of manpower resources within the hospital. Confirmatory Factor Analysis (CFA) The proposed hypothetical model was validated using Confirmatory Factor Analysis (CFA) on the 32 items across eight dimensions, as shown in Table 2 . The results demonstrated that all item factor loadings exceeded the recommended threshold of 0.5 set by Awang [ 20 ] , with t-values and corresponding p-values indicating statistical significance. Internal consistency was confirmed, with Composite Reliability (CR) values surpassing the 0.7 benchmark and Average Variance Extracted (AVE) for all constructs exceeding 0.5, indicating strong convergent validity [ 21 ] . The Squared Multiple Correlations (SMC) for all items also surpassed the acceptable threshold of 0.30 [ 22 ] . Discriminant validity was assessed using Fornell and Larcker's (1981) criterion, comparing the square root of the AVE (diagonal values) with the inter-construct correlations (off-diagonal values). [ 23 ] The data in Table 3 confirmed discriminant validity among the model constructs, ensuring that each construct is distinct and independent. The CFA results substantiate the statistical reliability and validity of the developed scale, affirming its suitability for subsequent Structural Equation Modeling analysis. Structural Equation Modeling Analysis Mediation Effect Analysis In this study, we employed Structural Equation Modeling (SEM) via AMOS software, supplemented by a Bootstrap method with 5000 samples, to examine and test the research hypotheses related to four two-factor mediation models(see in Table 4 ). Our objective was to investigate the roles of two specific mediator variables in the relationship between independent and dependent variables and to ascertain which mediator demonstrates a more significant role in mediating the effect of the independent variable on the dependent variable. Model 1 Analysis Results: The SEM analysis revealed that social support (SS) positively influences behavioral intention (BI) through self-efficacy (SET), with a standardized path coefficient of 0.601 (SE = 0.102, Z = 5.892), indicating a statistically significant mediation effect see. This finding is consistent with Bandura's social cognitive theory, which posits that self-efficacy is crucial in influencing individual behavior [ 24 ] . The Bootstrap bias-corrected 95% confidence interval ranged from 0.453 to 0.914, reinforcing the mediation's significance, aligning with contemporary SEM approaches that recommend bootstrapping for more accurate confidence intervals [ 25 ] . In contrast, the mediation path through rational action (RAT) also demonstrated a significant positive effect, which is supported by the theory that rational decision-making processes are integral to behavioral intention [ 26 ] . A comparative analysis underscored the stronger mediating role of SET, highlighting the variable's significant influence on behavior, a finding that aligns with previous research emphasizing the pivotal role of self-efficacy in mediating social influences on behavior [ 26 , 27 ] . Model 2 Analysis Results: The analysis demonstrated a negative mediation effect of social support (SS) on behavioral intention (BI) via self-efficacy (SET), with a point estimate of -0.168 (SE = 0.053, Z = -3.170). This unexpected inverse relationship suggests that under certain conditions, increased social support might paradoxically decrease self-efficacy, potentially due to over-reliance or diminished personal agency [ 28 ] .On the other hand, cognitive processing (CP) showed a strong positive mediation effect (point estimate = 1.112, SE = 0.059, Z = 18.847), resonating with models that emphasize the role of cognitive factors in shaping behavioral intentions [ 29 ] .The significant difference in the mediation effects of SET and CP underscores the complexity of the mechanisms through which social support influences behavioral intentions, suggesting that the cognitive interpretation of social support plays a crucial role [ 30 ] . Model 3 Analysis Results: The analysis of Model 3 underscored the roles of knowledge and skills (KS) and the Health Belief Model (HBM) as significant mediators in the relationship between social support (SS) and behavioral intention (BI). The mediation effect of KS was substantial (point estimate = 0.395, SE = 0.118, Z = 3.347), aligning with theories that highlight the pivotal role of knowledge and skills in behavior change [30]. Similarly, HBM demonstrated a robust mediation effect (point estimate = 0.436, SE = 0.124, Z = 3.516), consistent with its established role in predicting health-related behaviors. [ 15 ] The lack of significant difference in their mediating effects suggests that both knowledge and individual health beliefs are crucial yet comparable determinants of behavioral intentions, echoing findings from previous research [ 31 ] . Model 4 Analysis Results: The analysis of Model 4 highlighted the differential mediation effects of the Theory of Planned Behavior (TPB) and knowledge and skills (KS) on the relationship between social support (SS) and behavioral intention (BI). While TPB did not exhibit a significant mediation effect (point estimate = 0.155, SE = 0.115, Z = 1.348), KS demonstrated a notable positive mediation effect (point estimate = 0.65, SE = 0.131, Z = 4.962). This significant difference underscores the paramount influence of KS in mediating the impact of social support on behavioral intentions, resonating with the literature that emphasizes the critical role of skills and knowledge in behavior change. [ 24 ] The finding also aligns with Ajzen's (1991) TPB, suggesting that while attitudes, subjective norms, and perceived behavioral control are important, the practical aspects of knowledge and skills can be more directly influential in shaping intentions and behaviors [ 13 ][ 26 ] Model fit In this study, we employed a suite of relative or incremental fit indices along with absolute fit goodness indices to comprehensively evaluate and compare the fit of different structural equation models (SEM). Relative or incremental fit indices, such as the ratio of Chi-square to degrees of freedom (Chi-square/df), the Normed Fit Index (NFI), and the Comparative Fit Index (CFI), were utilized to assess the improvement in fit of one model over another alternative model [ 32 ][ 33 ] . Absolute fit indices, including the Chi-square value, Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Root Mean Square Error of Approximation (RMSEA), and Comparative Fit Index (CFI), were used to evaluate the fit of individual models [ 34 ][ 35 ] . Through this approach, we aimed to provide a comprehensive and detailed analysis of model fit to support our research hypotheses and model selection. As illustrated in Table 5 , Model 1 displayed a Chi-square/df ratio of 6.4, suggesting potential overcomplexity in the model, although NFI and CFI values near 1 indicated good relative improvement compared to the baseline model [ 32 ] [ 36 ] . GFI and AGFI values suggested a relatively good fit, and an RMSEA value at the upper limit of the acceptable range further qualifies this assessment [ 37 ] . For Models 2, 3, and 4, the Chi-square/df ratios and RMSEA values indicated less favorable fits, corroborated by the GFI and AGFI values falling below the ideal standard. Despite this, NFI and CFI values showed good relative improvement, emphasizing the nuanced interpretation of fit indices in SEM analysis [ 38 ] . The comparative analysis of the four models' fit indices suggests that Model 1 demonstrates a better fit relative to the others, indicating its suitability for supporting the study's hypotheses and model selection. This nuanced understanding of model fit, supported by a robust framework of fit indices, reinforces the methodological rigor of our SEM analysis in the context of psychological research. [ 39 ] Conclusion and Discussion In this study, we constructed and analyzed four two-factor mediation models based on social support (SS) to delve deeply into how social support influences healthcare professionals' behavioral intentions towards the rational use of antimicrobial drugs. We incorporated Rational Action Theory (RAT), Theory of Planned Behavior (TPB), Cognitive Processing (CP), Health Belief Model (HBM), and Knowledge and Skills (KS) as core mediating variables. Our Structural Equation Modeling (SEM) analysis revealed that social support significantly and positively impacts healthcare professionals' rational prescribing behaviors, aligning with previous studies [ 8 ] [ 7 ] . However, the novelty of our study lies in utilizing a quantitative SEM approach and a comprehensive multi-mediator variable analysis to not only validate the positive effect of social support but also to precisely elucidate how this influence operates through different psychosocial pathways. The negative impact of social support on healthcare professionals' behavioral intentions (BI) through cognitive processing (CP) observed in Model 2 presents a finding contrary to conventional beliefs, suggesting that social support is not always a positive factor in promoting healthy behaviors. This unexpected result could be attributed to several key factors, including the phenomenon of information overload in complex healthcare settings, as discussed by Weick and Sutcliffe [ 40 ] , and potential cognitive conflicts between the information provided by social support and the existing knowledge, beliefs, or experiences of healthcare professionals. When analyzing Models 3 and 4, we observed differing significances in the mediating effect of Knowledge and Skills (KS), which could be interpreted through the lens of model assumptions and construction. The comparison between these models offers insights into how to select and configure mediating variables under different theoretical frameworks and the complex interactions that may exist between them. The assessment of the fit of the four models revealed that Model 1 demonstrated good fit, emphasizing the key roles of Self-Efficacy (SET) and Rational Action (RAT) in mediating the positive impact of social support on healthcare professionals' behavioral intentions. Although Models 2, 3, and 4 did not achieve ideal fit, the significant mediating effects they revealed provide a new perspective on understanding the mechanisms of how social support influences the formation of healthcare professionals' behavioral intentions. In essence, this study highlights the critical need to delve into the complex interplay of social support in encouraging rational antimicrobial use among healthcare professionals. By adopting a nuanced strategy that meticulously balances the quantity and quality of information while aligning with the cognitive capacities of professionals, we ensure that social support initiatives are effectively catalyzing the desired behavioral transformations. These findings are pivotal for healthcare organizations and policymakers, underscoring the necessity of establishing robust social support networks, devising customized intervention strategies, and strategically disseminating information to foster judicious antimicrobial drug usage. Crucially, this research distinguishes itself by employing a quantitative methodology to unravel the psychological determinants influencing healthcare professionals' antimicrobial prescribing behaviors—a departure from the traditional qualitative approaches predominantly observed in this field. This quantitative lens not only affords a more structured and objective analysis but also enables the quantification of relationships between social support and behavioral intentions, offering a more precise understanding of the factors at play. Understanding antimicrobial usage as a behavior influenced by an amalgam of psychological and environmental factors underscores the multifaceted nature of healthcare professionals' decision-making processes. It reaffirms that rational antimicrobial prescribing is not solely an outcome of clinical knowledge or guidelines but is significantly shaped by the social support context and the professionals' psychological landscape. Thus, this study's methodological approach and findings provide a foundational step towards designing more effective, evidence-based interventions that account for the broader spectrum of influences on antimicrobial prescribing behavior, paving the way for a more strategic and holistic approach to antimicrobial stewardship. Declarations Data Availability Statement The raw data supporting the conclusions of this article are available from the corresponding author upon reasonable request. The data are in .sav format (SPSS) and include all relevant information collected during the study. Ethical Approval and Consent: Our research complies with the principles outlined in the Declaration of Helsinki and the U.S. Federal Regulations (45 CFR 46), which state that anonymous surveys may qualify for exemption from IRB review. Additionally, in accordance with the regulations on ethical review of biomedical research involving humans in China, anonymous surveys that do not involve personal privacy or identifiable information are considered exempt from ethical review. According to the Institutional Review Board (IRB) policies of [Harvard University], research involving anonymous data collection is typically considered low-risk and may be exempt from ethical review. An informed consent was also waived by Beijing Tongren Hospital ethics committee review board . Author Contribution Le Han wrote the manuscript and prepared the tables all the other authors reviewed the manuscript References Roca I, Akova M, Baquero F, et al. The global threat of antimicrobial resistance: science for intervention [published correction appears in New Microbes New Infect. 2015;8:175]. New Microbes New Infect . 2015;6:22–29. Published 2015 Apr 16. doi: 10.1016/j.nmni.2015.02.007 Ventola CL. The antibiotic resistance crisis: part 1: causes and threats. P T. 2015;40(4):277–283.. Laxminarayan R, Duse A, Wattal C, et al. Antibiotic resistance-the need for global solutions [published correction appears in Lancet Infect Dis. 2014;14(1):11] [published correction appears in Lancet Infect Dis. 2014;14(3):182]. Lancet Infect Dis . 2013;13(12):1057–1098. doi: 10.1016/S1473-3099(13)70318-9 Spellberg B, Bartlett JG, Gilbert DN. The future of antibiotics and resistance. 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"Reporting structural equation modeling and confirmatory factor analysis results: A review." The Journal of educational research 99.6 (2006): 323–338. Weick, Karl E., and Kathleen M. Sutcliffe. Managing the unexpected: Sustained performance in a complex world . John Wiley & Sons, 2015. Tables Table 1 Demographic Information of Respondents in the Survey Category Numbers Percent (%) Gender Male 136 18.9 Female 584 81.1 Age 20–30 144 20 31–40 269 37.4 41–50 222 30.8 51–60 85 11.8 Years of Experience 20years 194 26.9 Education Associate degree and below 63 8.8 Bachelor's 412 57.2 Master's 123 17 Doctorate 122 16 Department Internal Medicine 234 32.5 Surgery 152 21.1 Otorhinolaryngology 118 16.4 Emergency 100 13.9 Pediatrics 43 6.0 Ophthalmology 70 9.7 Obstetrics and Gynecology 3 .4 Table 2 Results of Confirmatory Factor Analysis for Study Constructs Construct Items Parameter Significance Estimation Factor Loading Item Reliability Composite Reliability Convergent Validity Unstd. S.E T-value P std SMC CR AVE BI BI1 1.000 .950 .903 .978 .918 BI2 1.023 .017 61.083 *** .963 .927 BI3 1.022 .017 59.598 *** .959 .920 BI4 1.013 .017 59.685 *** .959 .920 CP CP1 1.000 .883 .780 .959 .854 CP2 .989 .026 37.386 *** .911 .830 CP3 1.022 .024 42.180 *** .956 .914 CP4 1.022 .025 40.957 *** .945 .893 HBM HBM1 1.000 .950 .903 .937 .790 HBM2 .970 .021 46.357 *** .919 .845 HBM3 .920 .033 27.930 *** .755 .570 HBM4 .930 .020 46.035 *** .917 .841 KS KS1 1.000 .941 .885 .968 .885 KS2 1.005 .018 56.254 *** .961 .924 KS3 .978 .019 52.741 *** .947 .897 KS4 1.046 .023 45.482 *** .912 .832 RAT RAT1 1.000 .760 .578 .928 .765 RAT2 1.051 .038 27.371 *** .934 .872 RAT3 1.058 .039 27.021 *** .923 .852 RAT4 1.011 .040 25.181 *** .870 .757 SET SET1 1.000 .884 .781 .951 .828 SET2 .993 .028 35.260 *** .888 .789 SET3 1.062 .025 42.658 *** .964 .929 SET4 1.041 .029 36.491 *** .902 .814 TPB TPB1 1.000 .895 .801 .938 .792 TPB2 .963 .026 37.514 *** .915 .837 TPB3 .864 .026 33.169 *** .864 .746 TPB4 .890 .025 35.017 *** .886 .785 SS SS1 1.000 .957 .916 .971 .894 SS2 .972 .018 54.786 *** .938 .880 SS3 .982 .016 62.995 *** .964 .929 SS4 .979 .019 50.646 *** .922 .850 Table 3 Discriminant Validity Results for Study Constructs Construct AVE KS BI CP HBM SET TPB RAT SS KS .885 .941 BI .918 .871 .924 CP .854 .896 .96 .924 HBM .79 .775 .9 .914 .889 SET .828 .891 .856 .94 .859 .91 TPB .792 .836 .783 .861 .819 .901 .89 RAT .765 .634 .745 .738 .806 .685 .747 .875 SS .894 .895 .982 .952 .885 .859 .797 .737 .945 Table 4 Mediation Effect Analysis Results Across Different Models Models SIE point-estimation product of coef bias-corrected 95%CI percentile 95% CI SE Z Lower Upper Lower Upper model1 SS-SET-BI .601 .102 5.892 .453 .914 .442 .902 SS-RAT-BI .202 .075 2.693 .040 .354 .039 .353 SIE diff .399 .170 2.347 .117 .876 .105 .862 model2 SS-SET-BI − .168 .053 -3.170 − .295 − .079 − .277 − .060 SS-CP-BI 1.112 .059 18.847 1.007 1.249 .989 1.227 SIE diff -1.280 .111 -11.532 -1.541 -1.091 -1.501 -1.053 model3 SS-KS-BI .395 .118 3.347 .175 .635 .161 .622 SS-HBM-BI .436 .124 3.516 .212 .690 .217 .699 SIE diff − .041 .238 − .172 − .509 .422 − .524 .399 model4 SS-KS-BI .155 .115 1.348 0.018 .425 .008 .383 SS-TPB-BI .65 .131 4.962 0.39 .868 .396 .87 SIE diff − .495 .242 -2.045 − .843 .019 − .858 − .015 Table 5 Goodness-of-Fit Indices for Comparative Model Evaluation Model Chi-square Chi-square/df NFI GFI AGFI CFI RMSEA Model 1 639.9 6.4 .959 .902 .868 .964 .088(.081-.094) Model 2 1228.1 12.28 .939 .81 .742 .943 .125(.119-.132) Model 3 1304.9 13.5 .937 .808 .739 .942 .129(.123-.136 Model 4 1476.4 14.76 .922 .787 .71 .927 .138(.132-.145) Additional Declarations No competing interests reported. Supplementary Files amos.sav Cite Share Download PDF Status: Published Journal Publication published 10 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Nov, 2024 Reviews received at journal 17 Nov, 2024 Reviewers agreed at journal 17 Nov, 2024 Reviews received at journal 11 Nov, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviewers invited by journal 23 May, 2024 Editor assigned by journal 23 May, 2024 Editor invited by journal 20 May, 2024 Submission checks completed at journal 20 May, 2024 First submitted to journal 21 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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05:42:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4140928/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4140928/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-92357-2","type":"published","date":"2025-03-10T15:57:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78688967,"identity":"69fd6106-4ec1-4b0f-ac19-ca6a49320298","added_by":"auto","created_at":"2025-03-17 16:09:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":924609,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4140928/v1/4dd74ba9-3e7f-46ec-b63f-22365a22fabc.pdf"},{"id":57492027,"identity":"d0cc78a9-526e-414f-997f-22587e073db3","added_by":"auto","created_at":"2024-05-31 11:40:00","extension":"sav","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":133558,"visible":true,"origin":"","legend":"","description":"","filename":"amos.sav","url":"https://assets-eu.researchsquare.com/files/rs-4140928/v1/0cf552705c3b1b80708d60e6.sav"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Social Support's Mediating Effects on Rational Antimicrobial Prescribing: A Structural Equation Modeling Study of Healthcare Professionals' Behavior","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe discovery and widespread use of antimicrobial drugs marks a pivotal milestone in modern medicine, significantly improving the treatment of infectious diseases and saving millions of lives worldwide. However, the misuse and improper use of these drugs have led to the rapid development of antimicrobial resistance (AMR), now one of the gravest challenges to the global public health system.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e The escalation of AMR not only renders previously manageable infections difficult to control but also significantly escalates healthcare costs and poses a substantial threat to global public health security.\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e In response, the World Health Organization (WHO) has launched a global action plan underscoring the critical need for improved regulation and management of antimicrobial drug use, heightened awareness among both the public and healthcare professionals, and advocacy for the judicious use of antimicrobial drugs \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. At the national level, many countries have implemented stringent antimicrobial drug management policies, which include, but are not limited to, the development of more precise clinical guidelines, training for medical personnel on rational drug use, monitoring of antimicrobial drug usage, and targeted public health education campaigns \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Besides macro-level efforts, research focusing on the various factors influencing healthcare professionals' prescribing behaviors aims to enhance their knowledge, attitudes, and practice behaviors towards the rational use of antimicrobial drugs, thereby promoting rational drug use \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite these multifaceted measures, healthcare professionals, particularly in resource-limited developing countries, continue to face numerous challenges in the rational use of antimicrobial drugs. These challenges range from a lack of access to the latest clinical guidelines to pressures from patients for immediate treatment, a dearth of awareness about the consequences of antimicrobial drug resistance, and various systemic limitations within the healthcare infrastructure \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e .Thus, effectively addressing the irrational use of antimicrobial drugs requires not only enhancing the professional knowledge and skills of healthcare workers but also a deep understanding and analysis of the psychosocial factors underlying their prescribing behaviors. This study employs social support theory (SST) \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Rational Action Theory (RAT)\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Theory of Planned Behavior (TPB) \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Self-Efficacy Theory (SET)[14], Health Belief Model (HBM)\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e,Knowledge and Skills (KS)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, and Cognitive Processing (CP)\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e models to delve into the psychological motivations behind healthcare professionals' behavior regarding the rational use of antimicrobial drugs. By employing a Likert five-point scale tailored to each theoretical construct and quantitatively analyzing healthcare professionals' behavioral motivations towards the rational use of antimicrobial drugs, this study utilizes Structural Equation Modeling (SEM)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003eto explore the relationships between these theoretical constructs and the rational use of antimicrobial drugs by healthcare professionals. This comprehensive theoretical framework and quantitative methodology offer a holistic perspective on the behavioral motivations and decision-making processes of healthcare professionals concerning antimicrobial drug use, providing robust theoretical and empirical support for developing effective intervention measures.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Scale Development\u003c/h2\u003e \u003cp\u003eThis study employed a multidimensional approach to understand the psychological attitudes and behavioral motivations of healthcare professionals regarding the rational use of antimicrobial drugs. Drawing upon the foundations of Social Support Theory, Rational Action Theory, Theory of Planned Behavior, Cognitive Processing Theory, Health Belief Model, and the constructs of Knowledge and Skills, we designed a comprehensive Likert scale. The scale comprises eight dimensions with four items each, totaling 32 items aimed at evaluating the multifaceted factors influencing healthcare professionals' behaviors. The scale development was a collaborative effort by an interdisciplinary team of experts, including clinical medicine specialists, epidemiologists, health educators, and psychologists. The content validity of the scale was ensured through a rigorous expert review process, evaluating the relevance, representativeness, and clarity of the items. Following necessary revisions, the scale received validation from the expert panel, affirming its effectiveness in reflecting the study constructs and accurately measuring the target population's psychological motivations and behavioral tendencies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection\u003c/h2\u003e \u003cp\u003eA cross-sectional survey was conducted from January 8 to February 7, 2024, targeting healthcare professionals at a tertiary hospital in Beijing's urban area. Questionnaires were distributed and collected using the hospital's internal survey system, adhering to strict anonymity principles to protect participants' privacy. No identifiable information was collected.\u003c/p\u003e \u003cp\u003eTarget Population Criteria:\u003c/p\u003e \u003cp\u003eAge: Participants must be over 18 years old.\u003c/p\u003e \u003cp\u003eNationality: Participants must hold Chinese citizenship.\u003c/p\u003e \u003cp\u003eEmployment: Participants must be frontline healthcare professionals employed at the hospital.\u003c/p\u003e \u003cp\u003eLanguage: Participants must possess proficient oral, written, and reading comprehension skills in Chinese.\u003c/p\u003e \u003cp\u003eExclusion Criteria:\u003c/p\u003e \u003cp\u003eCognitive Impairment: Individuals showing signs of cognitive impairment were excluded.\u003c/p\u003e \u003cp\u003eCommunication Barriers: Participants with significant communication difficulties due to disabilities (e.g., blindness or deafness) were also excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sample Size Calculation\u003c/h2\u003e \u003cp\u003eUtilizing G*Power 3.1 software, the sample size was pre-calculated to ensure sufficient statistical power to detect the expected effects. Assuming a medium effect size (f\u0026sup2; = 0.15), a Type I error rate of 0.05, and a power (1-β error probability) of 0.95\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, the minimum required sample size was determined to be 267 participants. Accounting for a potential 20% dropout rate, the adjusted minimum sample size is 290 participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStructural Equation Modeling (SEM) using AMOS 23.0 software was employed to test the hypothesized models. The measurement model was first validated through Confirmatory Factor Analysis (CFA) to ensure items accurately reflected their corresponding latent variables.\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e SEM analysis then assessed the mediating models proposed in the hypotheses, examining path coefficients among independent, mediating, and dependent variables. Model fit was evaluated using various fit indices, including the chi-square (χ\u0026sup2;) statistic, degrees of freedom (df), χ\u0026sup2;/df ratio, Comparative Fit Index (CFI), Normed Fit Index (NFI), Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), and Root Mean Square Error of Approximation (RMSEA).\u003c/p\u003e "},{"header":"Research Hypotheses","content":"\u003cp\u003eModel 1:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003e(H1): Social support (SS) positively influences healthcare professionals' self-efficacy (SET).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003e(H2): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through self-efficacy (SET).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003e(H3): Social support (SS) positively influences healthcare professionals' rational action (RAT).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 4\u003c/strong\u003e \u003cp\u003e(H4): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through rational action (RAT).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eModel 2:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 5\u003c/strong\u003e \u003cp\u003e(H1): Social support (SS) positively influences healthcare professionals' knowledge and skills (KS).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 6\u003c/strong\u003e \u003cp\u003e(H2): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through knowledge and skills (KS).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 7\u003c/strong\u003e \u003cp\u003e(H3): Social support (SS) positively influences healthcare professionals' cognitive processing (CP).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 8\u003c/strong\u003e \u003cp\u003e(H4): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through cognitive processing (CP).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eModel 3:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 9\u003c/strong\u003e \u003cp\u003e(H1): Social support (SS) positively influences healthcare professionals' knowledge and skills (KS).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 10\u003c/strong\u003e \u003cp\u003e(H2): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through knowledge and skills (KS).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 11\u003c/strong\u003e \u003cp\u003e(H3): Social support (SS) positively influences healthcare professionals' health beliefs (HBM).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 12\u003c/strong\u003e \u003cp\u003e(H4): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through health beliefs (HBM).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eModel 4:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 13\u003c/strong\u003e \u003cp\u003e(H1): Social support (SS) positively influences healthcare professionals' planned behavior (TPB).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 14\u003c/strong\u003e \u003cp\u003e(H2): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through planned behavior (TPB).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 15\u003c/strong\u003e \u003cp\u003e(H3): Social support (SS) positively influences healthcare professionals' cognitive processing (CP).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 16\u003c/strong\u003e \u003cp\u003e(H4): Social support (SS) indirectly influences healthcare professionals' behavioral intentions (BI) to rationally use antimicrobial drugs through cognitive processing (CP).\u003c/p\u003e \u003c/p\u003e "},{"header":"Results","content":" \u003cp\u003eDemographics\u003c/p\u003e \u003cp\u003eThe survey successfully collected 720 responses from healthcare professionals across various demographics, including gender, age, work experience, education, and departmental affiliation, as illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The gender distribution showed a significant female majority, with women constituting 81.1% and men 18.9%, indicating a higher participation rate among female healthcare professionals in this study. The age group of 31\u0026ndash;40 years old was the most represented at 37.4%, followed by the 41\u0026ndash;50 age group at 30.8%. The distribution suggests a concentration of participants in the mid-career stage. In terms of work experience, individuals with 11\u0026ndash;20 years in the field accounted for the highest proportion at 37.4%, indicating a significant number of participants with considerable professional experience. Those with over 20 years of experience represented 26.9%, while newcomers with less than 5 years accounted for 16.9%. Regarding educational levels, a majority held a bachelor's degree (57.2%), followed by master's degree holders (17.1%), indicating a generally high educational standard among respondents. Participation varied significantly across departments, with internal medicine leading at 32.5%, suggesting higher engagement in the survey from this specialty. Surgery and otorhinolaryngology followed with 21.1% and 16.4%, respectively, while emergency medicine had a participation rate of 13.9%. Departments like ophthalmology (9.7%), pediatrics (6.0%), and obstetrics and gynecology (0.4%) showed lower engagement, possibly reflecting the distribution of manpower resources within the hospital.\u003c/p\u003e \u003cp\u003eConfirmatory Factor Analysis (CFA)\u003c/p\u003e \u003cp\u003eThe proposed hypothetical model was validated using Confirmatory Factor Analysis (CFA) on the 32 items across eight dimensions, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results demonstrated that all item factor loadings exceeded the recommended threshold of 0.5 set by Awang \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, with t-values and corresponding p-values indicating statistical significance. Internal consistency was confirmed, with Composite Reliability (CR) values surpassing the 0.7 benchmark and Average Variance Extracted (AVE) for all constructs exceeding 0.5, indicating strong convergent validity\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The Squared Multiple Correlations (SMC) for all items also surpassed the acceptable threshold of 0.30 \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Discriminant validity was assessed using Fornell and Larcker's (1981) criterion, comparing the square root of the AVE (diagonal values) with the inter-construct correlations (off-diagonal values).\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e The data in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e confirmed discriminant validity among the model constructs, ensuring that each construct is distinct and independent. The CFA results substantiate the statistical reliability and validity of the developed scale, affirming its suitability for subsequent Structural Equation Modeling analysis.\u003c/p\u003e \u003cp\u003eStructural Equation Modeling Analysis\u003c/p\u003e \u003cp\u003eMediation Effect Analysis\u003c/p\u003e \u003cp\u003eIn this study, we employed Structural Equation Modeling (SEM) via AMOS software, supplemented by a Bootstrap method with 5000 samples, to examine and test the research hypotheses related to four two-factor mediation models(see in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Our objective was to investigate the roles of two specific mediator variables in the relationship between independent and dependent variables and to ascertain which mediator demonstrates a more significant role in mediating the effect of the independent variable on the dependent variable.\u003c/p\u003e \u003cp\u003eModel 1 Analysis Results:\u003c/p\u003e \u003cp\u003eThe SEM analysis revealed that social support (SS) positively influences behavioral intention (BI) through self-efficacy (SET), with a standardized path coefficient of 0.601 (SE\u0026thinsp;=\u0026thinsp;0.102, Z\u0026thinsp;=\u0026thinsp;5.892), indicating a statistically significant mediation effect see. This finding is consistent with Bandura's social cognitive theory, which posits that self-efficacy is crucial in influencing individual behavior \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The Bootstrap bias-corrected 95% confidence interval ranged from 0.453 to 0.914, reinforcing the mediation's significance, aligning with contemporary SEM approaches that recommend bootstrapping for more accurate confidence intervals \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. In contrast, the mediation path through rational action (RAT) also demonstrated a significant positive effect, which is supported by the theory that rational decision-making processes are integral to behavioral intention \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. A comparative analysis underscored the stronger mediating role of SET, highlighting the variable's significant influence on behavior, a finding that aligns with previous research emphasizing the pivotal role of self-efficacy in mediating social influences on behavior \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eModel 2 Analysis Results:\u003c/p\u003e \u003cp\u003eThe analysis demonstrated a negative mediation effect of social support (SS) on behavioral intention (BI) via self-efficacy (SET), with a point estimate of -0.168 (SE\u0026thinsp;=\u0026thinsp;0.053, Z = -3.170). This unexpected inverse relationship suggests that under certain conditions, increased social support might paradoxically decrease self-efficacy, potentially due to over-reliance or diminished personal agency \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.On the other hand, cognitive processing (CP) showed a strong positive mediation effect (point estimate\u0026thinsp;=\u0026thinsp;1.112, SE\u0026thinsp;=\u0026thinsp;0.059, Z\u0026thinsp;=\u0026thinsp;18.847), resonating with models that emphasize the role of cognitive factors in shaping behavioral intentions \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.The significant difference in the mediation effects of SET and CP underscores the complexity of the mechanisms through which social support influences behavioral intentions, suggesting that the cognitive interpretation of social support plays a crucial role \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eModel 3 Analysis Results:\u003c/p\u003e \u003cp\u003eThe analysis of Model 3 underscored the roles of knowledge and skills (KS) and the Health Belief Model (HBM) as significant mediators in the relationship between social support (SS) and behavioral intention (BI). The mediation effect of KS was substantial (point estimate\u0026thinsp;=\u0026thinsp;0.395, SE\u0026thinsp;=\u0026thinsp;0.118, Z\u0026thinsp;=\u0026thinsp;3.347), aligning with theories that highlight the pivotal role of knowledge and skills in behavior change [30]. Similarly, HBM demonstrated a robust mediation effect (point estimate\u0026thinsp;=\u0026thinsp;0.436, SE\u0026thinsp;=\u0026thinsp;0.124, Z\u0026thinsp;=\u0026thinsp;3.516), consistent with its established role in predicting health-related behaviors.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e The lack of significant difference in their mediating effects suggests that both knowledge and individual health beliefs are crucial yet comparable determinants of behavioral intentions, echoing findings from previous research \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eModel 4 Analysis Results:\u003c/p\u003e \u003cp\u003eThe analysis of Model 4 highlighted the differential mediation effects of the Theory of Planned Behavior (TPB) and knowledge and skills (KS) on the relationship between social support (SS) and behavioral intention (BI). While TPB did not exhibit a significant mediation effect (point estimate\u0026thinsp;=\u0026thinsp;0.155, SE\u0026thinsp;=\u0026thinsp;0.115, Z\u0026thinsp;=\u0026thinsp;1.348), KS demonstrated a notable positive mediation effect (point estimate\u0026thinsp;=\u0026thinsp;0.65, SE\u0026thinsp;=\u0026thinsp;0.131, Z\u0026thinsp;=\u0026thinsp;4.962). This significant difference underscores the paramount influence of KS in mediating the impact of social support on behavioral intentions, resonating with the literature that emphasizes the critical role of skills and knowledge in behavior change.\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e The finding also aligns with Ajzen's (1991) TPB, suggesting that while attitudes, subjective norms, and perceived behavioral control are important, the practical aspects of knowledge and skills can be more directly influential in shaping intentions and behaviors \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eModel fit\u003c/p\u003e \u003cp\u003eIn this study, we employed a suite of relative or incremental fit indices along with absolute fit goodness indices to comprehensively evaluate and compare the fit of different structural equation models (SEM). Relative or incremental fit indices, such as the ratio of Chi-square to degrees of freedom (Chi-square/df), the Normed Fit Index (NFI), and the Comparative Fit Index (CFI), were utilized to assess the improvement in fit of one model over another alternative model \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e][\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Absolute fit indices, including the Chi-square value, Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Root Mean Square Error of Approximation (RMSEA), and Comparative Fit Index (CFI), were used to evaluate the fit of individual models \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e][\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Through this approach, we aimed to provide a comprehensive and detailed analysis of model fit to support our research hypotheses and model selection. As illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Model 1 displayed a Chi-square/df ratio of 6.4, suggesting potential overcomplexity in the model, although NFI and CFI values near 1 indicated good relative improvement compared to the baseline model \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. GFI and AGFI values suggested a relatively good fit, and an RMSEA value at the upper limit of the acceptable range further qualifies this assessment \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. For Models 2, 3, and 4, the Chi-square/df ratios and RMSEA values indicated less favorable fits, corroborated by the GFI and AGFI values falling below the ideal standard. Despite this, NFI and CFI values showed good relative improvement, emphasizing the nuanced interpretation of fit indices in SEM analysis \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe comparative analysis of the four models' fit indices suggests that Model 1 demonstrates a better fit relative to the others, indicating its suitability for supporting the study's hypotheses and model selection. This nuanced understanding of model fit, supported by a robust framework of fit indices, reinforces the methodological rigor of our SEM analysis in the context of psychological research.\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e"},{"header":"Conclusion and Discussion","content":" \u003cp\u003eIn this study, we constructed and analyzed four two-factor mediation models based on social support (SS) to delve deeply into how social support influences healthcare professionals' behavioral intentions towards the rational use of antimicrobial drugs. We incorporated Rational Action Theory (RAT), Theory of Planned Behavior (TPB), Cognitive Processing (CP), Health Belief Model (HBM), and Knowledge and Skills (KS) as core mediating variables. Our Structural Equation Modeling (SEM) analysis revealed that social support significantly and positively impacts healthcare professionals' rational prescribing behaviors, aligning with previous studies \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. However, the novelty of our study lies in utilizing a quantitative SEM approach and a comprehensive multi-mediator variable analysis to not only validate the positive effect of social support but also to precisely elucidate how this influence operates through different psychosocial pathways.\u003c/p\u003e \u003cp\u003eThe negative impact of social support on healthcare professionals' behavioral intentions (BI) through cognitive processing (CP) observed in Model 2 presents a finding contrary to conventional beliefs, suggesting that social support is not always a positive factor in promoting healthy behaviors. This unexpected result could be attributed to several key factors, including the phenomenon of information overload in complex healthcare settings, as discussed by Weick and Sutcliffe \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e, and potential cognitive conflicts between the information provided by social support and the existing knowledge, beliefs, or experiences of healthcare professionals.\u003c/p\u003e \u003cp\u003eWhen analyzing Models 3 and 4, we observed differing significances in the mediating effect of Knowledge and Skills (KS), which could be interpreted through the lens of model assumptions and construction. The comparison between these models offers insights into how to select and configure mediating variables under different theoretical frameworks and the complex interactions that may exist between them.\u003c/p\u003e \u003cp\u003eThe assessment of the fit of the four models revealed that Model 1 demonstrated good fit, emphasizing the key roles of Self-Efficacy (SET) and Rational Action (RAT) in mediating the positive impact of social support on healthcare professionals' behavioral intentions. Although Models 2, 3, and 4 did not achieve ideal fit, the significant mediating effects they revealed provide a new perspective on understanding the mechanisms of how social support influences the formation of healthcare professionals' behavioral intentions.\u003c/p\u003e \u003cp\u003eIn essence, this study highlights the critical need to delve into the complex interplay of social support in encouraging rational antimicrobial use among healthcare professionals. By adopting a nuanced strategy that meticulously balances the quantity and quality of information while aligning with the cognitive capacities of professionals, we ensure that social support initiatives are effectively catalyzing the desired behavioral transformations. These findings are pivotal for healthcare organizations and policymakers, underscoring the necessity of establishing robust social support networks, devising customized intervention strategies, and strategically disseminating information to foster judicious antimicrobial drug usage. Crucially, this research distinguishes itself by employing a quantitative methodology to unravel the psychological determinants influencing healthcare professionals' antimicrobial prescribing behaviors\u0026mdash;a departure from the traditional qualitative approaches predominantly observed in this field. This quantitative lens not only affords a more structured and objective analysis but also enables the quantification of relationships between social support and behavioral intentions, offering a more precise understanding of the factors at play. Understanding antimicrobial usage as a behavior influenced by an amalgam of psychological and environmental factors underscores the multifaceted nature of healthcare professionals' decision-making processes. It reaffirms that rational antimicrobial prescribing is not solely an outcome of clinical knowledge or guidelines but is significantly shaped by the social support context and the professionals' psychological landscape. Thus, this study's methodological approach and findings provide a foundational step towards designing more effective, evidence-based interventions that account for the broader spectrum of influences on antimicrobial prescribing behavior, paving the way for a more strategic and holistic approach to antimicrobial stewardship.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article are available from the corresponding author upon reasonable request. The data are in .sav format (SPSS) and include all relevant information collected during the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical Approval and Consent:\u003c/p\u003e\n\u003cp\u003eOur research complies with the principles outlined in the Declaration of Helsinki and the U.S. Federal Regulations (45 CFR 46), which state that anonymous surveys may qualify for exemption from IRB review. Additionally, in accordance with the regulations on ethical review of biomedical research involving humans in China, anonymous surveys that do not involve personal privacy or identifiable information are considered exempt from ethical review. According to the Institutional Review Board (IRB) policies of [Harvard University], research involving anonymous data collection is typically considered low-risk and may be exempt from ethical review.\u003c/p\u003e\n\u003cp\u003eAn informed consent was also waived by Beijing Tongren Hospital ethics committee review board .\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLe Han wrote the manuscript and prepared the tables all the other authors reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoca I, Akova M, Baquero F, et al. 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John Wiley \u0026amp; Sons, 2015.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eDemographic Information of Respondents in the Survey\u003c/div\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCategory\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eNumbers\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003ePercent (%)\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eGender\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMale\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e136\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e18.9\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eFemale\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e584\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e81.1\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAge\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e20\u0026ndash;30\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e144\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e20\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e31\u0026ndash;40\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e269\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e37.4\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e41\u0026ndash;50\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e222\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e30.8\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e51\u0026ndash;60\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e85\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e11.8\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eYears of Experience\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;5years\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e122\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e16.9\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5-10years\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e134\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e18.6\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e11-20years\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e269\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e37.4\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026gt;\u0026thinsp;20years\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e194\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e26.9\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eEducation\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAssociate degree and below\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e63\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e8.8\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBachelor's\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e412\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e57.2\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMaster's\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e123\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e17\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eDoctorate\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e122\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e16\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"7\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eDepartment\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eInternal Medicine\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e234\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e32.5\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSurgery\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e152\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e21.1\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eOtorhinolaryngology\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e118\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e16.4\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eEmergency\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e100\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e13.9\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003ePediatrics\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e43\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e6.0\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eOphthalmology\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e70\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e9.7\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eObstetrics and Gynecology\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.4\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eResults of Confirmatory Factor Analysis for Study Constructs\u003c/div\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eConstruct\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eItems\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eParameter Significance Estimation\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eFactor Loading\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eItem Reliability\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eComposite Reliability\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eConvergent Validity\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eUnstd.\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eS.E\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eT-value\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eP\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003estd\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSMC\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCR\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAVE\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBI\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBI1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.000\u003c/div\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\n\u003cdiv class=\"SimplePara\"\u003e.950\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.903\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.978\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.918\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBI2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.023\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.017\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e61.083\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.963\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.927\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBI3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.022\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.017\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e59.598\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.959\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.920\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBI4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.013\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.017\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e59.685\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.959\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.920\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCP\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCP1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.000\u003c/div\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\n\u003cdiv class=\"SimplePara\"\u003e.883\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.780\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.959\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.854\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCP2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.989\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.026\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e37.386\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.911\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.830\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCP3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.022\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.024\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e42.180\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.956\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.914\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCP4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.022\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.025\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e40.957\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.945\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.893\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHBM\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHBM1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.000\u003c/div\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\n\u003cdiv class=\"SimplePara\"\u003e.950\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.903\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.937\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.790\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHBM2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.970\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.021\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e46.357\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.919\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.845\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHBM3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.920\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.033\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e27.930\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.755\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.570\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHBM4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.930\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.020\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e46.035\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.917\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.841\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eKS\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eKS1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.000\u003c/div\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\n\u003cdiv class=\"SimplePara\"\u003e.941\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.885\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.968\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.885\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eKS2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.005\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.018\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e56.254\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.961\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.924\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eKS3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.978\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.019\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e52.741\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.947\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.897\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eKS4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.046\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.023\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e45.482\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.912\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.832\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eRAT\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eRAT1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.000\u003c/div\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\n\u003cdiv class=\"SimplePara\"\u003e.760\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.578\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.928\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.765\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eRAT2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.051\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.038\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e27.371\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.934\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.872\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eRAT3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.058\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.039\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e27.021\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.923\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.852\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eRAT4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.011\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.040\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e25.181\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.870\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.757\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSET\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSET1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.000\u003c/div\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\n\u003cdiv class=\"SimplePara\"\u003e.884\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.781\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.951\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.828\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSET2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.993\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.028\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e35.260\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.888\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.789\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSET3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.062\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.025\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e42.658\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.964\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.929\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSET4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.041\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.029\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e36.491\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.902\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.814\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTPB\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTPB1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.000\u003c/div\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\n\u003cdiv class=\"SimplePara\"\u003e.895\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.801\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.938\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.792\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTPB2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.963\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.026\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e37.514\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.915\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.837\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTPB3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.864\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.026\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e33.169\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.864\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.746\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTPB4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.890\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.025\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e35.017\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.886\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.785\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSS\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSS1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.000\u003c/div\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\n\u003cdiv class=\"SimplePara\"\u003e.957\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.916\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.971\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.894\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSS2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.972\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.018\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e54.786\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.938\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.880\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSS3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.982\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.016\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e62.995\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.964\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.929\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSS4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.979\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.019\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e50.646\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e***\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.922\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.850\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eDiscriminant Validity Results for Study Constructs\u003c/div\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eConstruct\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAVE\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eKS\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBI\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eCP\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHBM\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSET\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTPB\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eRAT\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSS\u003c/div\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\u003cdiv class=\"SimplePara\"\u003eKS\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.885\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e.941\u003c/span\u003e\u003c/div\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\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\u003cdiv class=\"SimplePara\"\u003eBI\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.918\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.871\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e.924\u003c/span\u003e\u003c/div\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\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\u003cdiv class=\"SimplePara\"\u003eCP\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.854\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.896\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.96\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e.924\u003c/span\u003e\u003c/div\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\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eHBM\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.79\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.775\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.9\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.914\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e.889\u003c/span\u003e\u003c/div\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\u003cdiv class=\"SimplePara\"\u003eSET\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.828\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.891\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.856\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.94\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.859\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e.91\u003c/span\u003e\u003c/div\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\u003cdiv class=\"SimplePara\"\u003eTPB\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.792\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.836\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.783\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.861\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.819\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.901\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e.89\u003c/span\u003e\u003c/div\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\u003cdiv class=\"SimplePara\"\u003eRAT\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.765\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.634\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.745\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.738\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.806\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.685\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.747\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e.875\u003c/span\u003e\u003c/div\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\u003cdiv class=\"SimplePara\"\u003eSS\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.894\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.895\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.982\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.952\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.885\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.859\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.797\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.737\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Bold\"\u003e.945\u003c/span\u003e\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eMediation Effect Analysis Results Across Different Models\u003c/div\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eModels\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSIE\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epoint-estimation\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eproduct of coef\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003ebias-corrected 95%CI\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003epercentile 95% CI\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSE\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eZ\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLower\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eUpper\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eLower\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eUpper\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003emodel1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSS-SET-BI\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.601\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.102\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e5.892\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.453\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.914\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.442\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.902\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSS-RAT-BI\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.202\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.075\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.693\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.040\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.354\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.039\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.353\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSIE diff\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.399\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.170\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e2.347\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.117\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.876\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.105\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.862\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003emodel2\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSS-SET-BI\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026minus;\u0026thinsp;.168\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.053\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-3.170\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026minus;\u0026thinsp;.295\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026minus;\u0026thinsp;.079\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026minus;\u0026thinsp;.277\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026minus;\u0026thinsp;.060\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSS-CP-BI\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.112\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.059\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e18.847\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.007\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.249\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.989\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e1.227\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSIE diff\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.280\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.111\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-11.532\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.541\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.091\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.501\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e-1.053\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003emodel3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSS-KS-BI\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.395\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.118\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.347\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.175\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.635\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.161\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.622\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSS-HBM-BI\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.436\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.124\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e3.516\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.212\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.690\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.217\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.699\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSIE diff\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026minus;\u0026thinsp;.041\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.238\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026minus;\u0026thinsp;.172\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026minus;\u0026thinsp;.509\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e.422\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv 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class=\"SimplePara\"\u003e.138(.132-.145)\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4140928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4140928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImportance: The study addresses the critical issue of antimicrobial resistance, focusing on the role of social support in influencing healthcare professionals' antimicrobial prescribing behavior, a key factor in clinical practice and public health.\u003c/p\u003e \u003cp\u003eObjective: The primary objective is to examine the mediating effects of social support on the decision-making processes of healthcare professionals regarding antimicrobial drug use, emphasizing the impact on rational prescribing within a healthcare setting.\u003c/p\u003e \u003cp\u003e Evidence Review: The study employed a cross-sectional survey design, analyzing data from 720 healthcare professionals using Structural Equation Modeling. It reviewed how variables such as self-efficacy, knowledge and skills, and health beliefs, informed by theories like RAT, TPB, and HBM, mediate the influence of social support.\u003c/p\u003e \u003cp\u003eFindings: The SEM analysis demonstrated significant mediating effects of social support on prescribing intentions through various psychosocial factors. The results offer quantitative insights into the relationships between social support and critical psychological determinants of prescribing behavior.\u003c/p\u003e \u003cp\u003eConclusions and Relevance: The findings elucidate the nuanced impact of social support on antimicrobial prescribing decisions, providing evidence-based insights for enhancing antimicrobial stewardship. This study informs clinicians and policymakers about the significance of social support in promoting rational antimicrobial use.\u003c/p\u003e","manuscriptTitle":"Assessing Social Support's Mediating Effects on Rational Antimicrobial Prescribing: A Structural Equation Modeling Study of Healthcare Professionals' Behavior","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-31 11:39:55","doi":"10.21203/rs.3.rs-4140928/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-21T06:57:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-17T14:04:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254629419532755350855949342715928870949","date":"2024-11-17T12:26:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-11T09:13:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253734237510605291954463382711420212957","date":"2024-10-29T09:43:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-23T11:23:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-23T11:22:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-20T11:13:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-20T11:07:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-21T05:41:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a3a9f632-14a8-42d7-a4fe-19b494358e07","owner":[],"postedDate":"May 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32498229,"name":"Health sciences/Diseases/Infectious diseases"},{"id":32498230,"name":"Biological sciences/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2025-03-17T16:01:51+00:00","versionOfRecord":{"articleIdentity":"rs-4140928","link":"https://doi.org/10.1038/s41598-025-92357-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-03-10 15:57:40","publishedOnDateReadable":"March 10th, 2025"},"versionCreatedAt":"2024-05-31 11:39:55","video":"","vorDoi":"10.1038/s41598-025-92357-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-92357-2","workflowStages":[]},"version":"v1","identity":"rs-4140928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4140928","identity":"rs-4140928","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}