The Impact of Altruistic Behavior on Occupational Well-being Among Medical Professionals: The Mediating Role of Flow Experience

The Impact of Altruistic Behavior on Occupational Well-being Among Medical Professionals: The Mediating Role of Flow Experience | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Altruistic Behavior on Occupational Well-being Among Medical Professionals: The Mediating Role of Flow Experience Yunxia Zhong, Xiaohan Sun, Yuqing Zhang, Jun Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7598155/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This research was designed to explore the impact of altruistic behavior on occupational well-being among medical professionals and to analyze the mediating effect of flow experience between altruistic behavior and occupational well-being. Methods A cross-sectional survey was conducted from March to June 2025, collecting data from 430 medical professionals in Beijing, China, using convenience sampling. The analysis was carried out using the partial least squares structural equation modeling (PLS-SEM) approach in SmartPLS 4.0 software. Results (1) altruistic behavior among medical professionals was positively correlated with both their flow experience and occupational well-being; (2) flow experience among medical professionals was positively correlated with their occupational well-being; (3) flow experience served as a mediator in the relationship between altruistic behavior and occupational well-being. Conclusion This study is the first to integrate the pathways connecting altruistic behavior, flow experience, and occupational well-being in the Chinese healthcare context. The findings indicate that medical professionals with altruistic tendencies are more likely to experience flow states, which in turn enhances their occupational well-being. These results offer valuable insights for improving the occupational well-being of healthcare workers and for refining human resource management practices in medical institutions. medical professionals altruistic behavior occupational well-being flow PLS-SEM mediating effect Figures Figure 1 Figure 2 1. Introduction The healthcare industry is a core sector for maintaining public health, with medical professionals being the driving force of the healthcare system. Their physical, psychological health, and occupational well-being profoundly influence the quality of medical services and patients' health and well-being. Recently, with the increasing medical demands and escalating work stress, issues such as occupational burnout[ 1 , 2 , 3 ] and mental health problems[ 4 ] among medical professionals have become increasingly prominent, posing critical challenges to the stability and service quality of the medical workforce. In this context, improving the occupational well-being of medical professionals is not only related to their personal well-being, but also a fundamental guarantee of sustainability for the entire medical and health system. As a multi-dimensional concept to measure the quality of professional life, occupational well-being covers many aspects such as physical and mental health, value embodiment, and social support. Its improvement not only helps to enhance the work commitment and professional identity of medical personnel, but also helps to optimize the overall efficiency of medical services. Although previous studies have explored factors influencing medical professionals' occupational well-being, such as organizational culture[ 5 ] and remuneration[ 6 ], few have analyzed the impact of intrinsic behavioral traits, especially altruistic behavior, which is a factor with significant humanistic care characteristics. In addition, "flow", as a highly engaged and enjoyable psychological state, has been found to enhance personal well-being in fields such as education [ 7 ] and sports [ 8 ], but its role in medical situations has not been fully explored. In reality, there is no shortage of related phenomena that suggest that there is a connection between the three. For example, healthcare workers who participate in long-term counterpart primary care services often enter a highly focused and meaningful working state, experiencing a sense of accomplishment and happiness due to their continued selfless assistance to patients in under-resourced areas [ 9 ]. Similarly, in public health emergencies, many medical personnel enter a state of selfless and engaged flow due to their all-out treatment of patients and selfless teamwork, not only completing treatment tasks efficiently, but also feeling a strong sense of professional value and satisfaction [ 10 ]. This suggests that altruistic behaviors might contribute to the enhancement of occupational well-being by promoting flow experiences. However, the integration of altruistic behavior, flow experience and occupational well-being in medical contexts has not been thoroughly investigated, especially the mediating effect of flow between altruistic behavior and occupational well-being. Therefore, the purpose of this study is to systematically explore the impact of altruistic behavior on medical professionals' occupational well-being and the mediating effect of flow experience, providing a scientific basis for interventions to enhance medical professionals' occupational well-being and informing human resource management in healthcare institutions. 2. Theoretical Background and Study Hypotheses 2.1 Altruistic Behavior and Well-being The term "altruism" was first proposed by sociologist Comte to describe selfless acts towards others. It is generally understood as a voluntary tendency to help others without expecting future reward. Altruistic behavior refers to selfless acts undertaken for the benefit of others. Different research perspectives offer varying interpretations. For example, sociologist Trivers defined altruism as "behavior that is clearly detrimental to the organism performing it but beneficial to another organism not closely related." Biologist Wilson defined altruism as "behavior beneficial to others at a cost to oneself." Post defined altruism as acts where the agent sacrifices self-interest for the benefit of others, subjectively not for gaining personal satisfaction or happiness [ 11 ]. Altruism among healthcare providers is key to ensuring patient welfare and has long been considered a vital component of professional competence and a core medical value. Current research indicates a strong link between altruistic behavior and well-being. Curry et al. [ 12 ] demonstrated through a systematic review that performing acts of kindness has a beneficial effect on the well-being of the individual performing them, an effect unaffected by gender, age, participant type, intervention, control condition, or outcome measurement. Research by Rasooli et al. [ 13 ] investigating factors influencing the well-being of Iranian nurses found a significant correlation between nurses' well-being and their caring behaviors. Concurrently, studies by other scholars [ 14 , 15 ] found that altruism leads to personal happiness, health, and work enjoyment, suggesting a potential mechanism through which altruism enhances well-being. Based on these arguments, we propose: H1: Altruistic behavior among medical professionals is positively correlated with their occupational well-being. 2.2. Altruistic Behavior and Flow Experience The concept of "flow" was introduced in the 1970s by the American psychologist Csikszentmihalyi, referring to the holistic sensation experienced when fully immersed in an activity [ 16 ]. In this subjective state, individuals highly concentrate their attention on the current activity, experience a unity of knowledge and action, feel skilled, perceive time passing quickly, and greatly enjoy the process [ 17 , 18 ]. Altruistic behavior and flow experience share a common foundation: intrinsic motivation. Both are driven by the pleasure and satisfaction derived from the activity itself, rather than external rewards (e.g., money, reputation). Current research indicates a close relationship between altruistic behavior and flow experience. Wang Yifang et al. [ 19 ] found that high-intensity altruistic behavior among healthcare workers during pandemic control can trigger a "flow effect," manifesting as self-motivation through immersive work, promoting a sublimation process from "altruistic pleasure → altruistic happiness → altruistic personality." Research by Hamzaa et al. [ 20 ] showed a direct positive impact of altruism on passion, and according to previous research [ 21 ], greater harmonious passion predisposes individuals to experience flow in their preferred activities. Therefore, based on previous research, we propose: H2: Altruistic behavior among medical professionals is positively correlated with their flow experience. 2.3. Flow and Occupational Well-being Occupational well-being, also known as career well-being, was conceptualized by British scholar Kidd [ 22 ] in 2008. She expanded well-being research to encompass emotional experiences throughout one's career. Furthermore, she distinguished occupational well-being from job-related well-being (job satisfaction): the latter reflects an individual's subjective experience of their specific job, while the former reflects subjective feelings about one's career journey and development process, including not only current work emotions but also overall career perceptions. Research suggests a possible association between flow and occupational well-being. Fullagar and colleagues [ 23 ] investigated the connections among flow, core job characteristics, and subjective well-being, differentiating state flow from trait flow. Their results revealed a link between flow and positive emotions. Furthermore, cross-lagged regression analysis demonstrated that momentary flow experiences predict subsequent emotional states. Research by Carpentier et al. [ 21 ] found that individuals with higher levels of harmonious passion are more likely to enter a state of flow during preferred activities, which in turn predicts higher well-being. A study comprising 178 music teachers and 605 students across 16 music institutions [ 24 ] revealed that job resources such as autonomy, performance feedback, and social support enhanced teachers’ challenge-skill balance, thereby facilitating the occurrence of flow experiences. Social support is an important dimension of the Medical Workers' Occupational Well-being Scale [ 25 ]. Simultaneously, research by Salanova et al. [ 26 ] confirmed that work-related flow positively influences organizational resources like social support. Akman's [ 27 ] research found a moderate positive correlation between nurses' flow experience and subjective well-being. Furthermore, nurses participating in surgeries had significantly higher flow experiences than those not participating. Based on previous research, we propose: H3: Flow experience among medical professionals is positively correlated with their occupational well-being. 2.4. The Mediating Effect of Flow Based on the above theories, altruistic behavior may enhance occupational well-being by inducing flow experiences. That is, altruistic behavior provides a highly engaging and meaningful work context that facilitates the occurrence of flow states, thereby indirectly boosting occupational well-being. Consequently, we propose the following: H4: Flow experience mediates the relationship between altruistic behavior and occupational well-being among medical professionals. Figure 1 depicts the hypothesized theoretical framework for this study. 3. Methods 3.1. Measurement Tools A quantitative methodology was employed using questionnaires to measure each variable. To assess occupational well-being, we used the Medical Workers' Occupational Well-being Scale compiled by Hu Dongmei et al. [ 25 ]. This scale includes five dimensions: physical and mental health, value embodiment, social support, work environment, and economic income, totaling 24 items. Items are rated on a 1–5 scale, yielding a total score between 24 and 120, where higher scores correspond to higher levels of occupational well-being. For altruistic behavior, a scale developed by Li Peng et al. [ 28 ], referencing Rushton et al. [ 29 ] and Leung et al. [ 30 ], was used. It includes three dimensions: attitude (3 items), behavior (4 items), and quality (3 items). Each item uses a Likert scale from 1 ("Never") to 5 ("Very Frequently"). To assess flow among medical professionals, a Flow State Scale for medical personnel was constructed based on the "Flow State Scale" developed by Jackson et al. [ 31 ] and the revised Chinese version of the "Flow State Scale-2" by Liu Weina [ 32 ], adapted to the characteristics of healthcare workers. Reliability and validity tests confirmed the scientific rigor and applicability of this scale. It includes four dimensions: concentration and directional feedback, intrinsic empowerment and control, transformation of time, and loss of self-consciousness. Comprising 15 items measured on a 5-point Likert scale. Higher total scores indicate stronger flow experience. (See Appendix IV for details on the development and validation of a flow state scale for medical personnel ). 3.2. Study Participants and Data Collection Prior to the formal survey, a pilot test was conducted with 65 subjects using an electronic questionnaire to estimate completion time and assess subjects' comprehension of the scale content. After revisions based on feedback, an online survey was administered via Questionnaire Star from March to June 2025 using convenience sampling. Participants were medical professionals from medical institutions of different levels, various occupational categories, and diverse departments in Beijing, China. Inclusion criteria were: (1) medical professionals from different levels of medical institutions, different departments (including surgery, internal medicine, pediatrics, obstetrics/gynecology, traditional Chinese medicine, anesthesiology, etc.), and different occupational categories (including doctors, nurses, pharmacists, and medical technicians); (2) substantial clinical experience in their relevant field; (3) voluntary participation. The minimum required sample size was calculated with G*Power version 3.1.9.7. Using a statistical power (1 − β) of 0.95, a significance level (α) of 0.05, and a medium effect size (f² = 0.15), the analysis indicated a minimum sample size of 89 participants [ 33 ]. Additionally, following Barclay et al.'s recommendation [ 34 ], the sample size should be at least 10 times the maximum number of indicators (manifest variables) for any latent variable. With the maximum number of indicators being 24 in this study, the required sample size was 24 × 10 = 240. This sample size is considered adequate to ensure both the accuracy and representativeness of the subsequent data analysis [ 35 ]. In total, 504 questionnaires were collected. Following the exclusion of 74 invalid responses (response time < 180 seconds, logical contradictions, or extreme values), 430 valid questionnaires remained, yielding an effective response rate of 85%, sufficient for data analysis. 3.3. Data Analysis Descriptive statistics were analyzed with SPSS 26.0. Given the non-normal distribution of the sample data and its relatively limited size, traditional Covariance-Based Structural Equation Modeling (CB-SEM) was less suitable due to its stricter distributional assumptions and larger sample size requirements. Therefore, Partial Least Squares Structural Equation Modeling (PLS-SEM) was selected. The model was built using SmartPLS 4.0 software to explore the relationships among the main variables. PLS-SEM is a non-parametric method that does not rely on specific data distribution assumptions [ 36 ] and is more suitable for analyzing small samples and non-normal data. Furthermore, compared to CB-SEM, PLS-SEM is more appropriate for exploratory research, especially in emerging fields lacking mature theoretical support [ 37 ]. 4. Results 4.1. Descriptive Statistics Table 1 displays the basic demographic profile of the study participants. Among the 430 valid questionnaires, the majority were female (71.2%), and ages were concentrated between 26–45 years (68.9%). Educational attainment was predominantly bachelor's degree (70.7%), and over half had ≥ 10 years of work experience (74.4%). The sample primarily came from tertiary hospitals (42.6%) and primary hospitals (39.3%). Physicians constituted the largest occupational group (52.1%). Department distribution showed high proportions in general practice (48.1%), other departments (17.0%), and internal medicine (11.9%). (Note: "Other departments" include ENT, critical care medicine, psychiatry, etc.). Table 1 Demographic Characteristics of Respondents (N = 430) Characteristic Categories Frequencies Percentages Gender Male 123 28.6% Female 306 71.2% Other genders 1 0.2% Age 25 or below 21 4.9% 26–35 119 27.7% 36–45 177 41.2% 46 or above 113 26.3% Education Level Associate degree or below 45 10.5% Bachelor's degree 304 70.7% Master's degree 55 12.8% Doctoral degree 26 6.0% Work Experience ≤ 6 years 74 17.2% 7–9 years 36 8.4% ≥ 10 years 320 74.4% Hospital Level Grassroots medical institutions 39 9.1% Unrated institutions 4 0.9% Primary hospital 169 39.3% Secondary hospital 35 8.1% Tertiary hospital 183 42.6% Professional Category Physician 224 52.1% Nurse 123 28.6% Pharmacist 17 4.0% Medical technician 41 9.5% Other 25 5.8% Department Internal Medicine 51 11.9% Surgery 30 7.0% Pediatrics 8 1.9% Obstetrics and Gynecology 12 2.8% Traditional Chinese Medicine 9 2.1% Anesthesiology 8 1.9% Medical Technology Departments 32 7.4% General Practitioner 207 48.1% Other 73 17.0% Note: The grassroots medical institutions in this study refer to community health service stations and village clinics. 4.2. Measurement Model Assessment The evaluation of the measurement model was conducted in two stages. Initially, the reliability and convergent validity of the constructs were examined. This involved verifying that indicator loadings, composite reliability (CR), and average variance extracted (AVE) met the recommended thresholds of ≥ 0.5, ≥ 0.7, and ≥ 0.5, respectively [ 38 , 39 ]. As shown in Table 2 , all observed factor loadings in this study surpassed the threshold of 0.5, while both the average variance extracted (AVE) and composite reliability (CR) values met or exceeded the recommended benchmarks of 0.5 and 0.7, respectively. Thus, the sample demonstrated sufficient reliability and convergent validity. Next, Discriminant validity was subsequently evaluated by applying both the Fornell-Larcker criterion and the heterotrait-monotrait ratio (HTMT). According to the Fornell-Larcker criterion, the square root of the AVE for a latent variable should be greater than its correlations with other latent variables [ 40 ]. The results in Table 3 show that the square root of the AVE for each latent variable (diagonal elements) is greater than its off-diagonal correlations. The HTMT ratio, which compares the average correlations across constructs to the average correlations within constructs, should have values below 0.85 [ 41 ]. The results in Table 4 indicate that all HTMT values meet this requirement, confirming good discriminant validity between variables. Table 2 Outer Model Evaluation: Convergent Validity and Reliability Analysis Construct Variable Loadings Cronbach's alpha CR AVE Altrusim Attitude 0.909 0.893 0.933 0.823 Behavior 0.881 Quality 0.931 Flow Concentration and directional feedback 0.861 0.808 0.871 0.635 Intrinsic empowerment and control 0.909 Loss of self-consciousness 0.547 Transformation of time 0.820 Occupational Well-being Health 0.608 0.865 0.903 0.655 Value 0.867 Support 0.859 Environment 0.888 Income 0.793 Table 3 Outer model test: Discriminant Validity Based on the Fornell-Larcker Criterion Altrusim Flow Occupational Well-being Altrusim 0.907 Flow 0.553 0.797 Occupational Well-being 0.613 0.578 0.809 Table 4 Outer model test: Discriminant Validity via the Heterotrait-Monotrait Ratio (HTMT) Altrusim Flow Occupational Well-being Altrusim Flow 0.598 Occupational Well-being 0.677 0.650 Additionally, due to the use of single-source data, common method bias (CMB) was a potential concern. Variance Inflation Factor (VIF) values were examined to assess this possibility. The results showed that all VIF values for the structural model ranged from 1.000 to 1.441, all below the accepted cutoff of 5[ 42 ], suggesting no severe common method bias issues. 4.3. Structural Model Assessment SmartPLS 4.0 was used to calculate the significance levels of relationships between latent variables and path coefficients to test the hypotheses. Two key metrics evaluated the structural model: R² (coefficient of determination) and path coefficients. Bootstrapping with 5000 subsamples was performed. The hypothesis testing results are shown in Table 5 . The R² for occupational well-being was 0.458, and for flow experience was 0.306, indicating that 45.8% of the variance in occupational well-being can be explained by altruistic behavior and flow experience, and 30.6% of the variance in flow experience can be explained by altruistic behavior. Altruistic behavior was positively related to occupational well-being (β = 0.423, P < 0.001), supporting H1. Altruistic behavior was positively related to flow experience (β = 0.553, P < 0.001), supporting H2. Flow experience was positively related to occupational well-being (β = 0.344, P < 0.001), supporting H3. Flow experience partially mediated the relationship between altruistic behavior and occupational well-being (β = 0.190, P < 0.001), supporting H4. Figure 2 shows the output PLS-SEM model diagram with path coefficients. Table 5 Structural Model Evaluation Results (N = 430, Bootstrap = 5000) Hypothesis Path Coefficient T statistics Confidence interval P-Value Remark Altrusim - >Occupational Well-being 0.423 8.702 [0.323,0.515] P Flow 0.553 16.501 [0.485,0.617] P Occupational Well-being 0.344 7.538 [0.258,0.434] P Flow - >Occupational Well-being 0.190 6.732 [0.139,0.248] P < 0.001 support 5. Discussion 5.1. Main Findings This study is the first to integrate altruistic behavior, flow experience, and occupational well-being within the Chinese healthcare context, constructing and validating a theoretical model with flow as a mediator. The results systematically elucidate the impact of medical professionals' altruistic behavior on their occupational well-being. The findings indicate that altruistic behavior not only directly and positively predicts occupational well-being (β = 0.423, p < 0.001) but also indirectly influences it through the partial mediating effect of flow experience (β = 0.190, p < 0.001). Furthermore, flow experience itself was positively correlated with occupational well-being (β = 0.344, p < 0.001). These findings support all proposed hypotheses, suggesting that within the Chinese healthcare setting, medical professionals with altruistic tendencies are more likely to enter flow states during medical service provision, thereby enhancing their occupational well-being. 5.2. Comparison with Previous Studies The findings are consistent with existing domestic and international literature that generally affirms a positive association between altruism and well-being [ 43 , 44 ]. The innovative aspect of this study lies in its incorporation of flow as a mediator within the framework linking medical professionals' altruistic behavior to occupational well-being, confirming its partial mediating role. This extends the application of flow theory within healthcare and organizational psychology. Relative to Western studies, this research underscores unique characteristics of the Chinese healthcare system and cultural context. For instance, medical professionals in China often face heavier workloads and higher public expectations, wherein altruistic behavior is not merely a personal attribute but is frequently an internalized component of their professional identity. This study demonstrates that even within such high-pressure settings, altruistic behavior can elicit profound flow experiences, subsequently improving occupational well-being. This observation aligns with Gu et al.'s [ 45 ] research involving Chinese teachers, suggesting that flow, as a positive affective experience, serves a similar cross-functional role across various helping professions. 5.3. Strengths and Limitations The study possesses several strengths. Firstly, it employed PLS-SEM for data analysis, which, compared to traditional CB-SEM, is more suitable for exploratory studies during the initial phases of theoretical model building, as well as for contexts involving limited sample sizes. It effectively handles complex relationships among multiple variables and is less prone to convergence issues. Secondly, the scales used in the survey demonstrated good reliability and validity, and the questionnaire design was optimized through pilot testing. Thirdly, the study sample encompassed medical professionals from different hospital levels, departments, and occupational categories, enhancing its representativeness. However, several limitations of this study should be noted. Firstly, the cross-sectional design precludes causal inferences between variables. Future research should employ longitudinal tracking or experimental designs to further validate the causal pathways among altruistic behavior, flow experience, and occupational well-being. Secondly, data collection relied on participants' self-reports of their occupational well-being and flow experiences over a past period, potentially introducing recall bias. Future studies could incorporate the Experience Sampling Method (ESM) or Ecological Momentary Assessment (EMA) to collect data on occupational well-being and flow in real-time within actual work settings, reducing reliance on retrospective memory. Thirdly, the sample was drawn solely from medical institutions in Beijing, potentially limiting the generalizability of the results. Future research should expand to include medical professionals from different regions to improve sample representativeness. Finally, the study did not include other potential influencing variables on occupational well-being (e.g., perceived organizational support, work stress). Future research could construct more comprehensive theoretical models to enhance explanatory power. 5.4. Theoretical Implications and Practical Contributions Theoretically, this study constructs and validates a mediating mechanism model of "Altruistic Behavior → Flow Experience → Occupational Well-being." It represents the first integration of perspectives from positive psychology and occupational health within the context of Chinese healthcare organizations, expanding the application boundaries of flow theory in high-pressure helping professions. Practically, the findings offer targeted insights for human resource management in healthcare institutions: Firstly, hospital administrators should move beyond traditional focus solely on qualifications and skills during recruitment. Incorporating situational judgment tests or behavioral event interviews to assess candidates' empathy, service orientation, and intrinsic altruistic motivation is recommended. Selecting individuals whose values align with the essential "healing and saving" nature of medical work from the outset can lay a solid foundation for building high well-being, high-performance teams. Furthermore, fostering an organizational culture that recognizes and rewards altruistic behavior and teamwork, celebrating actions embodying altruism and professionalism, is crucial. Secondly, healthcare institutions can develop flow-promotion training and create work environments conducive to flow, such as mindfulness training, task design optimization, and feedback mechanism improvement. Providing systematic training and career development support can help balance challenges and skills for medical professionals, aiding their entry into flow states. Thirdly, enhancing occupational well-being not only benefits the physical and mental health of healthcare workers but also helps reduce burnout, improve medical service quality, and increase patient satisfaction [ 46 ]. Additionally, enhanced flow experience can contribute to improved work performance [ 47 ], ultimately achieving a win-win situation for both healthcare institutions and professionals. 5.5. Unresolved Issues and Future Research Directions Although this research preliminarily reveals the impact of altruistic behavior on occupational well-being among medical professionals and the mediating effect of flow experience, there are still some issues worth exploring in depth. Future research could expand and refine in the following aspects: First, employing longitudinal designs or the Experience Sampling Method (ESM) for repeated measurements would capture the dynamic relationships among variables over time, allowing for more robust causal inferences. Second, incorporating more potential explanatory variables and boundary conditions. For example, examining the supplementary mediating roles of variables like psychological capital or emotion regulation strategies, or exploring the moderating effects of personality traits, perceived organizational support, and work stress could reveal more complex psychological and contextual mechanisms. Third, expanding the geographical coverage and institutional diversity of samples to include healthcare institutions from different regions, economic levels, and management models would test the generalizability of the current model across different contexts. Cross-cultural comparative studies could also be conducted. Finally, future research could focus on translating these findings into practice, developing targeted intervention programs or human resource management practices based on the identified mechanisms, and testing their application effectiveness and sustainability in real healthcare settings. 6. Conclusion This study confirms the positive impact of altruistic behavior on occupational well-being among medical professionals and is the first to verify the partial mediating role of flow experience in this relationship within the Chinese healthcare context. It offers healthcare organizations a fresh perspective rooted in positive psychology for addressing the challenge of professional burnout and enhancing human resource effectiveness: the essence of management lies in empowerment—creating conditions that enable altruistically-minded medical professionals to more readily achieve flow states, thereby finding personal fulfillment and professional accomplishment through selfless dedication. Abbreviations The following abbreviations are used in this manuscript: PLS-SEM partial least squares structural equation modeling CB-SEM Covariance-Based Structural Equation Modeling CR Composite reliability AVE Average variance extracted VIF Variance Inflation Factor CMB Common method bias ESM Experience Sampling Method EMA Ecological Momentary Assessment Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Capital Medical University (protocol code Z2022SY019). The research project titled "Study on the Dynamic Adjustment Mechanism of Pricing for New Medical Technology Services in Beijing" was supported by the Beijing Social Science Foundation (21JCC116) and undertaken by the School of Public Health. Written informed consent was obtained from all participants prior to their inclusion in the study. Consent for publication Not applicable Availability of data and materials The data of this study cannot be publicly shared due to privacy protection concerns for participants, but the data that has been desensitized can be obtained upon reasonable request from the corresponding author. The application must be approved by the ethics committee and a data usage agreement must be signed. Competing interests The authors declare no conflicts of interest. Funding This research was funded by Capital High-Level Public Health Technical Talents Development Project (Subject Backbone-02-43) and Beijing Social Science Fund Policy Advisory Project（21JCC116）. Authors' contributions Conceptualization, Y.Z. and J.L.; methodology,Y.Z., X.S.,Y.Z. and J.L.; software, Y.Z.; validation,Y.Z., X.S.,Y.Z. and J.L.; formal analysis, Y.Z.; investigation,Y.Z. and X.S.; resources, J.L.; data curation, Y.Z. and X.S.; writing—original draft preparation, Y.Z.; writing—review and editing, Y.Z.; visualization, Y.Z. and X.S.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript. Acknowledgements We would like to express our sincere gratitude to the Graduate School of Capital Medical University, the Beijing Hospitals Authority, and the Health Commission of Daxing District, Beijing for their support. References Shanafelt TD, West CP, Dyrbye LN et al. (2022). Changes in Burnout and Satisfaction With Work-Life Integration in Physicians During the First 2 Years of the COVID-19 Pandemic. 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1","display":"","copyAsset":false,"role":"figure","size":10413,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesized research model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7598155/v1/c1996bea68eb0d9ea70c9c33.png"},{"id":94198636,"identity":"01722046-4793-4e7a-a943-98af636da608","added_by":"auto","created_at":"2025-10-23 13:35:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29038,"visible":true,"origin":"","legend":"\u003cp\u003ePLS-SEM results with path coefficients (*p \u0026lt; 0.001))\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7598155/v1/9ce7b0a1ea12013b5f2db37a.png"},{"id":98945446,"identity":"95fc2940-999b-4f4d-be35-f8e065670eb2","added_by":"auto","created_at":"2025-12-24 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Introduction","content":"\u003cp\u003eThe healthcare industry is a core sector for maintaining public health, with medical professionals being the driving force of the healthcare system. Their physical, psychological health, and occupational well-being profoundly influence the quality of medical services and patients' health and well-being. Recently, with the increasing medical demands and escalating work stress, issues such as occupational burnout[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and mental health problems[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] among medical professionals have become increasingly prominent, posing critical challenges to the stability and service quality of the medical workforce. In this context, improving the occupational well-being of medical professionals is not only related to their personal well-being, but also a fundamental guarantee of sustainability for the entire medical and health system.\u003c/p\u003e\u003cp\u003eAs a multi-dimensional concept to measure the quality of professional life, occupational well-being covers many aspects such as physical and mental health, value embodiment, and social support. Its improvement not only helps to enhance the work commitment and professional identity of medical personnel, but also helps to optimize the overall efficiency of medical services.\u003c/p\u003e\u003cp\u003eAlthough previous studies have explored factors influencing medical professionals' occupational well-being, such as organizational culture[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and remuneration[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], few have analyzed the impact of intrinsic behavioral traits, especially altruistic behavior, which is a factor with significant humanistic care characteristics. In addition, \"flow\", as a highly engaged and enjoyable psychological state, has been found to enhance personal well-being in fields such as education [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and sports [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], but its role in medical situations has not been fully explored. In reality, there is no shortage of related phenomena that suggest that there is a connection between the three. For example, healthcare workers who participate in long-term counterpart primary care services often enter a highly focused and meaningful working state, experiencing a sense of accomplishment and happiness due to their continued selfless assistance to patients in under-resourced areas [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, in public health emergencies, many medical personnel enter a state of selfless and engaged flow due to their all-out treatment of patients and selfless teamwork, not only completing treatment tasks efficiently, but also feeling a strong sense of professional value and satisfaction [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This suggests that altruistic behaviors might contribute to the enhancement of occupational well-being by promoting flow experiences. However, the integration of altruistic behavior, flow experience and occupational well-being in medical contexts has not been thoroughly investigated, especially the mediating effect of flow between altruistic behavior and occupational well-being.\u003c/p\u003e\u003cp\u003eTherefore, the purpose of this study is to systematically explore the impact of altruistic behavior on medical professionals' occupational well-being and the mediating effect of flow experience, providing a scientific basis for interventions to enhance medical professionals' occupational well-being and informing human resource management in healthcare institutions.\u003c/p\u003e"},{"header":"2. Theoretical Background and Study Hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Altruistic Behavior and Well-being\u003c/h2\u003e\u003cp\u003eThe term \"altruism\" was first proposed by sociologist Comte to describe selfless acts towards others. It is generally understood as a voluntary tendency to help others without expecting future reward. Altruistic behavior refers to selfless acts undertaken for the benefit of others. Different research perspectives offer varying interpretations. For example, sociologist Trivers defined altruism as \"behavior that is clearly detrimental to the organism performing it but beneficial to another organism not closely related.\" Biologist Wilson defined altruism as \"behavior beneficial to others at a cost to oneself.\" Post defined altruism as acts where the agent sacrifices self-interest for the benefit of others, subjectively not for gaining personal satisfaction or happiness [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Altruism among healthcare providers is key to ensuring patient welfare and has long been considered a vital component of professional competence and a core medical value. Current research indicates a strong link between altruistic behavior and well-being.\u003c/p\u003e\u003cp\u003eCurry et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] demonstrated through a systematic review that performing acts of kindness has a beneficial effect on the well-being of the individual performing them, an effect unaffected by gender, age, participant type, intervention, control condition, or outcome measurement. Research by Rasooli et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] investigating factors influencing the well-being of Iranian nurses found a significant correlation between nurses' well-being and their caring behaviors. Concurrently, studies by other scholars [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] found that altruism leads to personal happiness, health, and work enjoyment, suggesting a potential mechanism through which altruism enhances well-being. Based on these arguments, we propose:\u003c/p\u003e\u003cp\u003eH1: Altruistic behavior among medical professionals is positively correlated with their occupational well-being.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Altruistic Behavior and Flow Experience\u003c/h2\u003e\u003cp\u003eThe concept of \"flow\" was introduced in the 1970s by the American psychologist Csikszentmihalyi, referring to the holistic sensation experienced when fully immersed in an activity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this subjective state, individuals highly concentrate their attention on the current activity, experience a unity of knowledge and action, feel skilled, perceive time passing quickly, and greatly enjoy the process [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Altruistic behavior and flow experience share a common foundation: intrinsic motivation. Both are driven by the pleasure and satisfaction derived from the activity itself, rather than external rewards (e.g., money, reputation). Current research indicates a close relationship between altruistic behavior and flow experience. Wang Yifang et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] found that high-intensity altruistic behavior among healthcare workers during pandemic control can trigger a \"flow effect,\" manifesting as self-motivation through immersive work, promoting a sublimation process from \"altruistic pleasure \u0026rarr; altruistic happiness \u0026rarr; altruistic personality.\" Research by Hamzaa et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] showed a direct positive impact of altruism on passion, and according to previous research [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], greater harmonious passion predisposes individuals to experience flow in their preferred activities. Therefore, based on previous research, we propose:\u003c/p\u003e\u003cp\u003eH2: Altruistic behavior among medical professionals is positively correlated with their flow experience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Flow and Occupational Well-being\u003c/h2\u003e\u003cp\u003eOccupational well-being, also known as career well-being, was conceptualized by British scholar Kidd [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] in 2008. She expanded well-being research to encompass emotional experiences throughout one's career. Furthermore, she distinguished occupational well-being from job-related well-being (job satisfaction): the latter reflects an individual's subjective experience of their specific job, while the former reflects subjective feelings about one's career journey and development process, including not only current work emotions but also overall career perceptions. Research suggests a possible association between flow and occupational well-being. Fullagar and colleagues [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] investigated the connections among flow, core job characteristics, and subjective well-being, differentiating state flow from trait flow. Their results revealed a link between flow and positive emotions. Furthermore, cross-lagged regression analysis demonstrated that momentary flow experiences predict subsequent emotional states. Research by Carpentier et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] found that individuals with higher levels of harmonious passion are more likely to enter a state of flow during preferred activities, which in turn predicts higher well-being. A study comprising 178 music teachers and 605 students across 16 music institutions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] revealed that job resources such as autonomy, performance feedback, and social support enhanced teachers\u0026rsquo; challenge-skill balance, thereby facilitating the occurrence of flow experiences. Social support is an important dimension of the Medical Workers' Occupational Well-being Scale [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Simultaneously, research by Salanova et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] confirmed that work-related flow positively influences organizational resources like social support. Akman's [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] research found a moderate positive correlation between nurses' flow experience and subjective well-being. Furthermore, nurses participating in surgeries had significantly higher flow experiences than those not participating. Based on previous research, we propose:\u003c/p\u003e\u003cp\u003eH3: Flow experience among medical professionals is positively correlated with their occupational well-being.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. The Mediating Effect of Flow\u003c/h2\u003e\u003cp\u003eBased on the above theories, altruistic behavior may enhance occupational well-being by inducing flow experiences. That is, altruistic behavior provides a highly engaging and meaningful work context that facilitates the occurrence of flow states, thereby indirectly boosting occupational well-being. Consequently, we propose the following:\u003c/p\u003e\u003cp\u003eH4: Flow experience mediates the relationship between altruistic behavior and occupational well-being among medical professionals.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the hypothesized theoretical framework for this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Measurement Tools\u003c/h2\u003e\u003cp\u003eA quantitative methodology was employed using questionnaires to measure each variable. To assess occupational well-being, we used the Medical Workers' Occupational Well-being Scale compiled by Hu Dongmei et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This scale includes five dimensions: physical and mental health, value embodiment, social support, work environment, and economic income, totaling 24 items. Items are rated on a 1\u0026ndash;5 scale, yielding a total score between 24 and 120, where higher scores correspond to higher levels of occupational well-being. For altruistic behavior, a scale developed by Li Peng et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], referencing Rushton et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and Leung et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], was used. It includes three dimensions: attitude (3 items), behavior (4 items), and quality (3 items). Each item uses a Likert scale from 1 (\"Never\") to 5 (\"Very Frequently\"). To assess flow among medical professionals, a Flow State Scale for medical personnel was constructed based on the \"Flow State Scale\" developed by Jackson et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and the revised Chinese version of the \"Flow State Scale-2\" by Liu Weina [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], adapted to the characteristics of healthcare workers. Reliability and validity tests confirmed the scientific rigor and applicability of this scale. It includes four dimensions: concentration and directional feedback, intrinsic empowerment and control, transformation of time, and loss of self-consciousness. Comprising 15 items measured on a 5-point Likert scale. Higher total scores indicate stronger flow experience. (See Appendix IV for details on the development and validation of a flow state scale for medical personnel ).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Study Participants and Data Collection\u003c/h2\u003e\u003cp\u003ePrior to the formal survey, a pilot test was conducted with 65 subjects using an electronic questionnaire to estimate completion time and assess subjects' comprehension of the scale content. After revisions based on feedback, an online survey was administered via Questionnaire Star from March to June 2025 using convenience sampling. Participants were medical professionals from medical institutions of different levels, various occupational categories, and diverse departments in Beijing, China. Inclusion criteria were: (1) medical professionals from different levels of medical institutions, different departments (including surgery, internal medicine, pediatrics, obstetrics/gynecology, traditional Chinese medicine, anesthesiology, etc.), and different occupational categories (including doctors, nurses, pharmacists, and medical technicians); (2) substantial clinical experience in their relevant field; (3) voluntary participation. The minimum required sample size was calculated with G*Power version 3.1.9.7. Using a statistical power (1\u0026thinsp;\u0026minus;\u0026thinsp;β) of 0.95, a significance level (α) of 0.05, and a medium effect size (f\u0026sup2; = 0.15), the analysis indicated a minimum sample size of 89 participants [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, following Barclay et al.'s recommendation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], the sample size should be at least 10 times the maximum number of indicators (manifest variables) for any latent variable. With the maximum number of indicators being 24 in this study, the required sample size was 24 \u0026times; 10\u0026thinsp;=\u0026thinsp;240. This sample size is considered adequate to ensure both the accuracy and representativeness of the subsequent data analysis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In total, 504 questionnaires were collected. Following the exclusion of 74 invalid responses (response time\u0026thinsp;\u0026lt;\u0026thinsp;180 seconds, logical contradictions, or extreme values), 430 valid questionnaires remained, yielding an effective response rate of 85%, sufficient for data analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Data Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were analyzed with SPSS 26.0. Given the non-normal distribution of the sample data and its relatively limited size, traditional Covariance-Based Structural Equation Modeling (CB-SEM) was less suitable due to its stricter distributional assumptions and larger sample size requirements. Therefore, Partial Least Squares Structural Equation Modeling (PLS-SEM) was selected. The model was built using SmartPLS 4.0 software to explore the relationships among the main variables. PLS-SEM is a non-parametric method that does not rely on specific data distribution assumptions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and is more suitable for analyzing small samples and non-normal data. Furthermore, compared to CB-SEM, PLS-SEM is more appropriate for exploratory research, especially in emerging fields lacking mature theoretical support [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Descriptive Statistics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the basic demographic profile of the study participants. Among the 430 valid questionnaires, the majority were female (71.2%), and ages were concentrated between 26\u0026ndash;45 years (68.9%). Educational attainment was predominantly bachelor's degree (70.7%), and over half had\u0026thinsp;\u0026ge;\u0026thinsp;10 years of work experience (74.4%). The sample primarily came from tertiary hospitals (42.6%) and primary hospitals (39.3%). Physicians constituted the largest occupational group (52.1%). Department distribution showed high proportions in general practice (48.1%), other departments (17.0%), and internal medicine (11.9%). (Note: \"Other departments\" include ENT, critical care medicine, psychiatry, etc.).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic Characteristics of Respondents (N\u0026thinsp;=\u0026thinsp;430)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequencies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentages\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e71.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther genders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26\u0026ndash;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAssociate degree or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBachelor's degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaster's degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDoctoral degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWork Experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;6 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u0026ndash;9 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrassroots medical institutions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnrated institutions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertiary hospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfessional Category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhysician\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNurse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePharmacist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical technician\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepartment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternal Medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePediatrics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObstetrics and Gynecology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraditional Chinese Medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnesthesiology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical Technology Departments\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral Practitioner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: The grassroots medical institutions in this study refer to community health service stations and village clinics.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Measurement Model Assessment\u003c/h2\u003e\u003cp\u003eThe evaluation of the measurement model was conducted in two stages. Initially, the reliability and convergent validity of the constructs were examined. This involved verifying that indicator loadings, composite reliability (CR), and average variance extracted (AVE) met the recommended thresholds of \u0026ge;\u0026thinsp;0.5, \u0026ge;\u0026thinsp;0.7, and \u0026ge;\u0026thinsp;0.5, respectively [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, all observed factor loadings in this study surpassed the threshold of 0.5, while both the average variance extracted (AVE) and composite reliability (CR) values met or exceeded the recommended benchmarks of 0.5 and 0.7, respectively. Thus, the sample demonstrated sufficient reliability and convergent validity. Next, Discriminant validity was subsequently evaluated by applying both the Fornell-Larcker criterion and the heterotrait-monotrait ratio (HTMT). According to the Fornell-Larcker criterion, the square root of the AVE for a latent variable should be greater than its correlations with other latent variables [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that the square root of the AVE for each latent variable (diagonal elements) is greater than its off-diagonal correlations. The HTMT ratio, which compares the average correlations across constructs to the average correlations within constructs, should have values below 0.85 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicate that all HTMT values meet this requirement, confirming good discriminant validity between variables.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOuter Model Evaluation: Convergent Validity and Reliability Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLoadings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCronbach's alpha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAVE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAltrusim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAttitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.823\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBehavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConcentration and directional feedback\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntrinsic empowerment and control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLoss of self-consciousness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTransformation of time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupational Well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.655\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSupport\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnvironment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOuter model test: Discriminant Validity Based on the Fornell-Larcker Criterion\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAltrusim\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFlow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOccupational Well-being\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAltrusim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupational Well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.809\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOuter model test: Discriminant Validity via the Heterotrait-Monotrait Ratio (HTMT)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAltrusim\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFlow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOccupational Well-being\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAltrusim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupational Well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAdditionally, due to the use of single-source data, common method bias (CMB) was a potential concern. Variance Inflation Factor (VIF) values were examined to assess this possibility. The results showed that all VIF values for the structural model ranged from 1.000 to 1.441, all below the accepted cutoff of 5[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], suggesting no severe common method bias issues.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Structural Model Assessment\u003c/h2\u003e\u003cp\u003eSmartPLS 4.0 was used to calculate the significance levels of relationships between latent variables and path coefficients to test the hypotheses. Two key metrics evaluated the structural model: R\u0026sup2; (coefficient of determination) and path coefficients. Bootstrapping with 5000 subsamples was performed. The hypothesis testing results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The R\u0026sup2; for occupational well-being was 0.458, and for flow experience was 0.306, indicating that 45.8% of the variance in occupational well-being can be explained by altruistic behavior and flow experience, and 30.6% of the variance in flow experience can be explained by altruistic behavior. Altruistic behavior was positively related to occupational well-being (β\u0026thinsp;=\u0026thinsp;0.423, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H1. Altruistic behavior was positively related to flow experience (β\u0026thinsp;=\u0026thinsp;0.553, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H2. Flow experience was positively related to occupational well-being (β\u0026thinsp;=\u0026thinsp;0.344, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H3. Flow experience partially mediated the relationship between altruistic behavior and occupational well-being (β\u0026thinsp;=\u0026thinsp;0.190, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H4. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the output PLS-SEM model diagram with path coefficients.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStructural Model Evaluation Results (N\u0026thinsp;=\u0026thinsp;430, Bootstrap\u0026thinsp;=\u0026thinsp;5000)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePath Coefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT statistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConfidence interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRemark\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAltrusim - \u0026gt;Occupational Well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e[0.323,0.515]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003esupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAltrusim - \u0026gt;Flow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e[0.485,0.617]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003esupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlow- \u0026gt;Occupational Well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e[0.258,0.434]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003esupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAltrusim - \u0026gt;Flow - \u0026gt;Occupational Well-being\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e[0.139,0.248]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003esupport\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Main Findings\u003c/h2\u003e\u003cp\u003eThis study is the first to integrate altruistic behavior, flow experience, and occupational well-being within the Chinese healthcare context, constructing and validating a theoretical model with flow as a mediator. The results systematically elucidate the impact of medical professionals' altruistic behavior on their occupational well-being. The findings indicate that altruistic behavior not only directly and positively predicts occupational well-being (β\u0026thinsp;=\u0026thinsp;0.423, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but also indirectly influences it through the partial mediating effect of flow experience (β\u0026thinsp;=\u0026thinsp;0.190, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, flow experience itself was positively correlated with occupational well-being (β\u0026thinsp;=\u0026thinsp;0.344, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings support all proposed hypotheses, suggesting that within the Chinese healthcare setting, medical professionals with altruistic tendencies are more likely to enter flow states during medical service provision, thereby enhancing their occupational well-being.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Comparison with Previous Studies\u003c/h2\u003e\u003cp\u003eThe findings are consistent with existing domestic and international literature that generally affirms a positive association between altruism and well-being [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The innovative aspect of this study lies in its incorporation of flow as a mediator within the framework linking medical professionals' altruistic behavior to occupational well-being, confirming its partial mediating role. This extends the application of flow theory within healthcare and organizational psychology. Relative to Western studies, this research underscores unique characteristics of the Chinese healthcare system and cultural context. For instance, medical professionals in China often face heavier workloads and higher public expectations, wherein altruistic behavior is not merely a personal attribute but is frequently an internalized component of their professional identity. This study demonstrates that even within such high-pressure settings, altruistic behavior can elicit profound flow experiences, subsequently improving occupational well-being. This observation aligns with Gu et al.'s [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] research involving Chinese teachers, suggesting that flow, as a positive affective experience, serves a similar cross-functional role across various helping professions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Strengths and Limitations\u003c/h2\u003e\u003cp\u003eThe study possesses several strengths. Firstly, it employed PLS-SEM for data analysis, which, compared to traditional CB-SEM, is more suitable for exploratory studies during the initial phases of theoretical model building, as well as for contexts involving limited sample sizes. It effectively handles complex relationships among multiple variables and is less prone to convergence issues. Secondly, the scales used in the survey demonstrated good reliability and validity, and the questionnaire design was optimized through pilot testing. Thirdly, the study sample encompassed medical professionals from different hospital levels, departments, and occupational categories, enhancing its representativeness.\u003c/p\u003e\u003cp\u003eHowever, several limitations of this study should be noted. Firstly, the cross-sectional design precludes causal inferences between variables. Future research should employ longitudinal tracking or experimental designs to further validate the causal pathways among altruistic behavior, flow experience, and occupational well-being. Secondly, data collection relied on participants' self-reports of their occupational well-being and flow experiences over a past period, potentially introducing recall bias. Future studies could incorporate the Experience Sampling Method (ESM) or Ecological Momentary Assessment (EMA) to collect data on occupational well-being and flow in real-time within actual work settings, reducing reliance on retrospective memory. Thirdly, the sample was drawn solely from medical institutions in Beijing, potentially limiting the generalizability of the results. Future research should expand to include medical professionals from different regions to improve sample representativeness. Finally, the study did not include other potential influencing variables on occupational well-being (e.g., perceived organizational support, work stress). Future research could construct more comprehensive theoretical models to enhance explanatory power.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.4. Theoretical Implications and Practical Contributions\u003c/h2\u003e\u003cp\u003eTheoretically, this study constructs and validates a mediating mechanism model of \"Altruistic Behavior \u0026rarr; Flow Experience \u0026rarr; Occupational Well-being.\" It represents the first integration of perspectives from positive psychology and occupational health within the context of Chinese healthcare organizations, expanding the application boundaries of flow theory in high-pressure helping professions. Practically, the findings offer targeted insights for human resource management in healthcare institutions: Firstly, hospital administrators should move beyond traditional focus solely on qualifications and skills during recruitment. Incorporating situational judgment tests or behavioral event interviews to assess candidates' empathy, service orientation, and intrinsic altruistic motivation is recommended. Selecting individuals whose values align with the essential \"healing and saving\" nature of medical work from the outset can lay a solid foundation for building high well-being, high-performance teams. Furthermore, fostering an organizational culture that recognizes and rewards altruistic behavior and teamwork, celebrating actions embodying altruism and professionalism, is crucial. Secondly, healthcare institutions can develop flow-promotion training and create work environments conducive to flow, such as mindfulness training, task design optimization, and feedback mechanism improvement. Providing systematic training and career development support can help balance challenges and skills for medical professionals, aiding their entry into flow states. Thirdly, enhancing occupational well-being not only benefits the physical and mental health of healthcare workers but also helps reduce burnout, improve medical service quality, and increase patient satisfaction [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Additionally, enhanced flow experience can contribute to improved work performance [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], ultimately achieving a win-win situation for both healthcare institutions and professionals.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e5.5. Unresolved Issues and Future Research Directions\u003c/h2\u003e\u003cp\u003eAlthough this research preliminarily reveals the impact of altruistic behavior on occupational well-being among medical professionals and the mediating effect of flow experience, there are still some issues worth exploring in depth. Future research could expand and refine in the following aspects: First, employing longitudinal designs or the Experience Sampling Method (ESM) for repeated measurements would capture the dynamic relationships among variables over time, allowing for more robust causal inferences. Second, incorporating more potential explanatory variables and boundary conditions. For example, examining the supplementary mediating roles of variables like psychological capital or emotion regulation strategies, or exploring the moderating effects of personality traits, perceived organizational support, and work stress could reveal more complex psychological and contextual mechanisms. Third, expanding the geographical coverage and institutional diversity of samples to include healthcare institutions from different regions, economic levels, and management models would test the generalizability of the current model across different contexts. Cross-cultural comparative studies could also be conducted. Finally, future research could focus on translating these findings into practice, developing targeted intervention programs or human resource management practices based on the identified mechanisms, and testing their application effectiveness and sustainability in real healthcare settings.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study confirms the positive impact of altruistic behavior on occupational well-being among medical professionals and is the first to verify the partial mediating role of flow experience in this relationship within the Chinese healthcare context. It offers healthcare organizations a fresh perspective rooted in positive psychology for addressing the challenge of professional burnout and enhancing human resource effectiveness: the essence of management lies in empowerment\u0026mdash;creating conditions that enable altruistically-minded medical professionals to more readily achieve flow states, thereby finding personal fulfillment and professional accomplishment through selfless dedication.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"546\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePLS-SEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003epartial least squares structural equation modeling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCB-SEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCovariance-Based Structural Equation Modeling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eComposite reliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAverage variance extracted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVariance Inflation Factor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCommon method bias\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eESM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eExperience Sampling Method\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEcological Momentary Assessment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Capital Medical University (protocol code Z2022SY019). The research project titled \"Study on the Dynamic Adjustment Mechanism of Pricing for New Medical Technology Services in Beijing\" was supported by the Beijing Social Science Foundation (21JCC116) and undertaken by the School of Public Health. Written informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data of this study cannot be publicly shared due to privacy protection concerns for participants, but the data that has been desensitized can be obtained upon reasonable request from the corresponding author. The application must be approved by the ethics committee and a data usage agreement must be signed.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was funded by Capital High-Level Public Health Technical Talents Development Project (Subject Backbone-02-43) and Beijing Social Science Fund Policy Advisory Project（21JCC116）.\u003c/p\u003e\n\u003cp\u003eAuthors' contributions\u003c/p\u003e\n\u003cp\u003eConceptualization, Y.Z. and J.L.; methodology,Y.Z., X.S.,Y.Z. and J.L.; software, Y.Z.; validation,Y.Z., X.S.,Y.Z. and J.L.; formal analysis, Y.Z.; investigation,Y.Z. and X.S.; resources, J.L.; data curation, Y.Z. and X.S.; writing—original draft preparation, Y.Z.; writing—review and editing, Y.Z.; visualization, Y.Z. and X.S.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to the Graduate School of Capital Medical University, the Beijing Hospitals Authority, and the Health Commission of Daxing District, Beijing for their support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShanafelt TD, West CP, Dyrbye LN et al. (2022). Changes in Burnout and Satisfaction With Work-Life Integration in Physicians During the First 2 Years of the COVID-19 Pandemic. 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J Occup Health Psychol. 2006;11(3):266\u0026ndash;80.\u003c/span\u003e \u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/1076-8998.11.3.266\u003c/span\u003e\u003cspan address=\"10.1037/1076-8998.11.3.266\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"medical professionals, altruistic behavior, occupational well-being, flow, PLS-SEM, mediating effect","lastPublishedDoi":"10.21203/rs.3.rs-7598155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7598155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was designed to explore the impact of altruistic behavior on occupational well-being among medical professionals and to analyze the mediating effect of flow experience between altruistic behavior and occupational well-being.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional survey was conducted from March to June 2025, collecting data from 430 medical professionals in Beijing, China, using convenience sampling. The analysis was carried out using the partial least squares structural equation modeling (PLS-SEM) approach in SmartPLS 4.0 software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1) altruistic behavior among medical professionals was positively correlated with both their flow experience and occupational well-being; (2) flow experience among medical professionals was positively correlated with their occupational well-being; (3) flow experience served as a mediator in the relationship between altruistic behavior and occupational well-being.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is the first to integrate the pathways connecting altruistic behavior, flow experience, and occupational well-being in the Chinese healthcare context. The findings indicate that medical professionals with altruistic tendencies are more likely to experience flow states, which in turn enhances their occupational well-being. These results offer valuable insights for improving the occupational well-being of healthcare workers and for refining human resource management practices in medical institutions.\u003c/p\u003e","manuscriptTitle":"The Impact of Altruistic Behavior on Occupational Well-being Among Medical Professionals: The Mediating Role of Flow Experience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 13:35:19","doi":"10.21203/rs.3.rs-7598155/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"25a630d7-79a1-4233-bdb2-b2c8cbff1533","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T12:09:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 13:35:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7598155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7598155","identity":"rs-7598155","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}