Work Pressure, Coping Styles and Occupational Burnout Among Chinese Police Officers: A Meta-analytic Review

Work Pressure, Coping Styles and Occupational Burnout Among Chinese Police Officers: A Meta-analytic Review | 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 Work Pressure, Coping Styles and Occupational Burnout Among Chinese Police Officers: A Meta-analytic Review Senlin Zhou, Miaomiao Li, Siru Chen, Daokui Jiang, Ying Qu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3436081/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The present study conducted a comprehensive meta-analysis to systematically review the relationship between occupational burnout and work pressure among Chinese police officers. Additionally, the study explored the mediating role of coping styles using a meta-analytic structural equation model. The investigation involved a thorough search of CNKI, PubMed, PsychInfo, Web of Science, and Google Scholar databases, resulting in the identification of a total of 39 studies with 124 effect sizes and 14089 police officers. The findings revealed a positive correlation between work pressure and occupational burnout among Chinese police officers ( r = 0.410, 95% CI = [0.347, 0.469]). Furthermore, negative coping styles mediate the relationship between work pressure and occupational burnout. Importantly, these conclusions held true across various work regions for police officers. These results provide insights into the relationship magnitude between work pressure and occupational burnout in Chinese police work and shed light on the underlying mechanisms. Based on these findings, it is recommended that interventions focusing on reducing work pressure and fostering positive coping styles be implemented to mitigate occupational burnout among police officers. work pressure occupational burnout coping styles meta-analytic meta-analytic structural equation modeling (MASEM) Figures Figure 1 Figure 2 INTRODUCTION Occupational burnout has emerged as a significant concern in the field of occupational health, drawing extensive attention from scholars [ 1 , 2 ]. In particular, burnout among police officers has become an important research focus in recent years [ 3 , 4 ]. Studies have revealed a high prevalence of burnout among police personnel in China [ 4 , 5 ]. As Maslach and Leiter suggested, burnout is closely related to occupational factors such as work pressure [ 6 ]. Therefore, investigating the impact of police job characteristics like work pressure on burnout is crucial for understanding the mechanisms underlying burnout in this profession. Previous studies extensively explored the relationship between work pressure and burnout theoretically or empirically. However, the conclusions have been mixed and inconclusive [ 7 – 9 ]. For instance, while some studies have found a significant positive association between work pressure and burnout [ 5 ], others have reported weak or non-significant relationships [ 4 ]. Most previous studies have been conducted in Western cultural contexts, with limited research examining this issue in non-Western settings like China which has distinct cultural values. There is also a lack of research specifically focused on high-stress occupations such as policing. Additionally, the mechanisms through which work pressure influences burnout remain unclear and need further investigation. For the reasons aforementioned, there remain several critical research gaps to be addressed, including the lack of systematic reviews synthesizing the evidence on work pressure and burnout among Chinese police officer. Therefore, the present study aims to address these gaps by utilizing meta-analysis methods to systematically examine the relationship between work pressure and burnout outcomes in Chinese police officers. Drawing upon existing theories of occupational stress and burnout, this study will synthesize findings from previous empirical studies on this population to determine the overall strength and direction of the work pressure-burnout association. Moderator analyses will also be conducted to uncover potential variables that influence this relationship. Additionally, the mediating effects of coping strategies will be tested using meta-analytic structural equation modeling to explore the mechanisms linking work pressure to burnout dimensions. By addressing these questions through quantitative research synthesis, this study seeks to clarify the mixed findings on work pressure and burnout, elucidate underlying mechanisms, and provide evidence-based recommendations for mitigating burnout in high-stress policing occupations. This research makes several meaningful contributions to the literature. First, it enriches research on occupational burnout by focusing on the understudied Chinese cultural context and the highly stressful policing profession. Second, it helps clarify the mixed findings on work pressure and burnout by testing their relationship with a rigorous study design. Third, it elucidates the mechanisms underlying this relationship and identifies protective factors (e.g., coping, social support), thereby providing guidance for interventions. Finally, by investigating burnout antecedents and protective factors in the police occupation, the study offers practical implications for maintaining work enthusiasm and mitigating burnout effects under high pressure. Overall, this research advances theoretical understanding of work pressure, burnout, and their interrelationships, laying the groundwork for future research and informing strategies to promote police well-being. Theoretical Model of Work Pressure and Occupation Burnout The primary theories that explain the association between work pressure and occupational burnout are the Job Demands-Resources (JD-R) and Conservation of Resources (COR) theory. The JD-R theory proposes that work pressure acts as a job demand which, when chronically high, can drain police officers' psychological and physiological resources over time, resulting in burnout [ 10 ]. Police work involves heavy workloads, long hours, and danger which impose excessive demands on officers. The COR theory further suggests that responding to these work pressures requires continual exertion of effort and energy by officers to cope. This constant resource expenditure to meet heavy work demands can slowly depleted their reserves needed to handle stress, leading to eventual burnout [ 11 ]. Together, the JD-R and COR theories indicate that the sustained demands of police work pressure can wear down officers' mental, emotional, and physical resources, reducing their capacity to cope and thus resulting in burnout. Lazarus and Folkman's transactional theory provides an explanatory model for why work pressure can influence burnout. The theory proposes that stress arises from an imbalance between demands (e.g. work pressure) and an individual's resources to cope with those demands [ 12 ]. Specifically, this theory posits that work stress results from an appraisal process, whereby an individual perceives certain work demands as exceeding their coping abilities. When faced with excessive organizational demands and pressure, employees may feel a lack of control and predictability, heightening their stress. Their negative cognitive appraisals and inadequate coping responses then lead to burnout. From three perspectives of theories, we tried to explore the relationship between work pressure and burnout, answering how and why between the relationship. Occupational Burnout and Work Pressure Occupational burnout encompasses a range of adverse reactions arising from prolonged emotional and interpersonal stress, primarily characterized by emotional exhaustion, dehumanization, and a diminished sense of accomplishment [ 13 , 14 ]. Emotional exhaustion represents the core element of burnout, reflecting individuals' feelings of weariness and disinterest in their work. Dehumanization involves experiencing indifference and alienation towards others and one's work environment. A reduced sense of accomplishment refers to a diminished appraisal of one's own work efficiency and status, leading to feelings of inadequacy and doubts about one's work capabilities. Work pressure refers to the internal tension experienced by an individual when facing physical or psychological threats, leading to the emergence of unpleasant or even distressing emotional states [ 15 ]. For police officers, work pressure arises from various sources, including the potential risks associated with dealing with criminals and the irregularity of working hours. Drawing on the Job Demands-Resources (JD-R) model and Conservation of Resources (COR) theory, work pressure can lead to burnout through a process of resource depletion. The JD-R model posits that excessive job demands exhaust employees' mental and emotional resources, resulting in energy depletion and burnout [ 16 ]. Similarly, the COR theory suggests that loss of resources due to high job demands can lead to psychological strain and burnout [ 17 ]. For police officers confronting overwhelming work pressure, their cognitive resources for managing complex tasks, as well as emotional resources for regulating negative feelings, become progressively depleted. Consequently, they are left with inadequate resources to cope with continual demands. This sustained resource loss and inability to meet demands manifests in physical and emotional exhaustion - the core symptom of burnout [ 18 ]. Officers may also distance themselves emotionally and cognitively from their work as a way of coping, leading to cynicism and reduced personal accomplishment. Empirical evidence supports the linkage between work pressure and burnout in policing occupations [ 19 , 20 ]. A meta-analysis by De Terte identified work pressure as a consistent predictor of police burnout across st DeTerteudies [ 21 ]. Survey research by Boles et al. also found a significant correlation between work stress and emotional exhaustion among law enforcement officers [ 22 ]. These findings align with the resource-based theoretical models. From the above theoretical statement and empirical support, hypothesis 1 is generated. Hypothesis 1 The work pressure experienced by policeman significantly and positively predicts occupational burnout. The Mediating Role of Coping Style in The Meta-analytic Structural Equation Model Coping style refers to the cognitive, emotional, and behavioral strategies that individuals adopt in response to external environmental stimuli, encompassing both positive and negative coping styles [ 23 ]. Coping serves as a key mediator in the role of work pressure on occupational burnout [ 24 – 26 ]. Specifically, the transactional theory states that burnout is the end result of a complex coping process initiated by chronic work stressors like work overload, role problems, and lack of autonomy [ 27 ]. When police officers face sustained work pressure from heavy workloads, mandatory overtime, danger, and organizational demands, they have to continually expend cognitive, emotional, and social support coping resources to manage this stress. Over time, this can deplete and exhaust their coping reserves, especially since police work often provides insufficient recovery opportunities. Eventually, officers' diminished coping resources combine with the unrelenting demands of work pressure to cause burnout [ 28 ]. Coping responses play a central role in this process – maladaptive strategies like avoidance, denial, venting, and self-blame can accelerate resource depletion and amplify the effects of work pressure on burnout. On the other hand, adaptive coping methods like problem-solving, seeking support, and positive reappraisal can buffer the impacts of demands before burnout develops. Therefore, the type of coping responses officers use in reaction to chronic work stressors can either mitigate or amplify the pathway between sustained work pressure and eventual burnout [ 29 ]. Sustained ineffective coping mechanism in managing workplace stressors lead to emotional exhaustion and eventual burnout. Empirical research corroborates that work pressure can predict occupational burnout either positively through negative coping styles or negatively through positive coping styles. For instance, Wang et al. found in their study on domestic civil servants that job work pressure exhibits a significant positive correlation with occupational burnout [ 4 ], while social support and coping styles demonstrate a significant negative correlation with occupational burnout. Similarly, Zheng employed multi-group confirmatory factor analysis and established that work pressure impacts occupational burnout through coping styles [ 30 ], revealing the existence of a relatively common and stable mechanism across regions. While the aforementioned theoretical and empirical studies offer insights into the influence of police job work pressure on occupational burnout and its underlying mechanisms, the relatively limited sample sizes of these studies warrant further verification of their universality. To address this gap, the present study employs meta-analysis techniques and meta-analytic structural equation modeling to comprehensively explore the aforementioned issues. Based on transactional theory and empirical studies, this study formulates the following hypothesis. Hypothesis a: The influence of police work pressure on occupational burnout is mediated by positive coping styles. Hypothesis b: The influence of police work pressure on occupational burnout is mediated by negative coping styles. The Boundary Conditions of Work Pressure and Occupational Burnout Association Although occupational burnout is prevalent among the police group, some individuals in the working environment experience significant work pressure but do not exhibit behaviors or feelings of occupational burnout. This observation suggests the existence of potential protective factors that may buffer the impact of work pressure on burnout. The occupational burnout theory emphasizes that incongruity between job remuneration and job content constitutes a major factor affecting burnout, as it can generate perceptions of injustice among police officers. Consequently, regional disparities in economic development during our country's economic growth may lead to differences in salary and remuneration for police officers engaged in similar work content, which may potentially influence the relationship between work pressure and occupational burnout. In general, in settings with high pay, police officers may not experience burnout even in the presence of perceived work pressure. Conversely, lower pay could exacerbate the positive prediction of job work pressure on burnout. As the included studies did not directly report the subjects' salaries, the differences in pay primarily manifest in the variation of work regions, which could impact the extent to which work pressure affects occupational burnout. Consequently, this study introduces the work region as a moderating variable for an exploratory aim. Based on national policies, guidelines, and the socio-economic development status of the sample's respective regions [ 31 ], regions were divided into three categories: the eastern coastal region (including 11 provinces and municipalities: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Hainan), the central emerging region (including 10 provinces and autonomous regions: Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hunan, Hubei, Guangxi), and the western development region (including 10 provinces and autonomous regions: Chongqing, Sichuan, Guizhou, Yunnan, Shanxi, Gansu, Ningxia, Tibet, Xinjiang, Qinghai). Past research has noted that men and women tend to experience occupational burnout differently [ 32 , 33 ]. This suggests gender likely intersects with work pressure and burnout in complex ways. Men and women often face different cultural expectations, discrimination, and obstacles in the workplace, which can influence their experiences of demand and burnout. For instance, women may face extra pressures balancing work and family responsibilities, while men may feel pressure to be breadwinners. Different coping mechanisms and support systems across genders could also be at play. Examining gender differences exploratorily here may shed light on nuanced ways that masculine and feminine roles shape the link between demands and burnout. While definitive conclusions cannot be drawn, results could inform future hypotheses and theories on how gender moderates burnout processes and point to gender-specific interventions. Exploring complex variables like gender on an initial basis can thus lay the groundwork for more systematic investigation in future research. While factors such as region, gender, and other variables like the type of police force, age, measure of pressure and burnout, publication year have the potential to moderate the work pressure-burnout association, the current theoretical and empirical evidence for their moderating effects is limited. Therefore, examining the moderating roles of these factors in the present meta-analysis is exploratory in nature, without specific a priori hypotheses. This preliminary investigation aims to provide directions for future research to further validate the boundary conditions of the occupational stress-burnout relationship in the context of Chinese police. Research Questions Previous research on work stress and occupational burnout has often focused on Western participants, specifically employees in corporate settings. Furthermore, Previous empirical studies on the relationship between police work pressure and occupational burnout in China have often focused on specific regional police samples [ 34 , 35 ]. It becomes challenging to generalize these research findings to the entire national police group. To address this limitation, more comprehensive research methods are needed to explore the broader relationship between work pressure and occupational burnout. Therefore, two key research questions emerge: RQ1: What is the overall relationship between work pressure and burnout among Chinese police officers across studies? RQ2: Do potential moderators, such as working region, influence the magnitude of the relationship between work pressure and burnout? To address these questions, the current study employs three-level meta-analysis to synthesize findings across studies on Chinese police samples. This integrative approach determines the "true" linkage between work pressure and burnout, while accounting for study-level confounding factors. Moderator analyses are also conducted to uncover variables that may impact this relationship. Additionally, meta-analytic structural equation modeling explores the mediating role of coping styles in the work pressure-burnout association, elucidating the mechanisms. By synthesizing the empirical evidence through advanced quantitative techniques, this study aims to clarify the effects of police work pressure on occupation burnout. METHOD Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement [ 36 ], the search for relevant articles was conducted using a multi-step approach. The initial searches were performed in January 2023 and covered publications from database inception to December 2022. Firstly, two authors independently performed searches in various databases, including CNKI, Web of Science, PubMed, and PsychInfo. The search terms used were combinations of "polic*" OR "law enforcement officer" AND "China" OR "Chinese" AND "pressure" OR "stress" OR "work overload" AND "burnout" OR "fatigue" OR " exhaustion" OR " disengagement" OR " work-induced apathy" AND " coping " OR " cognitive restructuring " OR " work-life balance". These terms were applied to search titles, abstracts, and keywords within the databases. Secondly, to ensure a comprehensive search, additional relevant studies were identified by searching through Google Scholar. Thirdly, the reference lists of the obtained articles were thoroughly screened to identify any other potentially relevant studies that may have been missed during the initial searches. This rigorous search process aims to include all relevant studies related to the relationship between work pressure and occupational burnout in the Chinese police context. Inclusion Criteria To ensure that the selected studies are directly relevant to the relationship between work pressure and occupational burnout in the context of Chinese mainland police and that the statistical measures used are appropriate for synthesizing the findings across different studies. The inclusion criteria for the literature in this meta-analysis are made as follows: (a) Studies that measure at least two variables related to work pressure, occupational burnout, or coping styles and report relevant statistical measures, such as Pearson correlation coefficients or other effect sizes (e.g., F , t , Cohen's d, Hegen's g , and f values) that can be converted into Pearson correlation coefficients. (b) These measures must be appropriate for examining the associations between the mentioned variables. (c) Only studies that specifically focus on police officers working within mainland China will be considered for inclusion. Figure 1 displays detailed information regarding the selection of studies. Data Coding In this meta-analysis, each included study's relevant data were coded following specific criteria. The following information was collected for each study: (a) Author: The names of the authors who conducted the study were recorded. (b) Publication age: The publication year of each study was noted to identify the timeframe of the research. (c) Sample size: The total number of participants in each study's sample was recorded. (d) Sex ratio: The proportion of female participants in each study's sample was calculated by dividing the number of females by the total population and multiplying by 100. (e) Region of work: The specific province or region where the study was conducted and where the Chinese mainland police officers worked was noted. (f) Correlation coefficient: The reported correlation coefficient between work pressure, occupational burnout, or coping styles in each study was recorded. (g) Variable measurement tool: The tools or scales used to measure work pressure, occupational burnout, and coping styles in each study were noted. (h) Tool's Cronbach’s Coefficient: The Cronbach’s coefficient of reliability for the measurement tools used in each study was recorded to assess the reliability of the scales. To handle studies that reported correlations between multiple dimensions of variables or between dimensions and the total score of the scale, a specific approach was employed. For studies reporting correlations between multiple dimensions, the Fisher's Z scores were calculated for each dimension's correlation and then averaged to obtain the final bivariate correlation. For studies reporting both dimensional correlations and total score correlations, only the total score correlation was used, and it was converted to Fisher's Z score. To ensure accuracy and consistency in data coding, two researchers received training in meta-analysis coding and specific research topics. Coding was conducted according to a coding standard manual developed by the research team, resulting in a coding consistency rate of 94.7%. In cases of coding discrepancies, the researchers discussed and reached a consensus to resolve any disagreements. Study Quality Evaluation In this meta-analysis, the potential bias and study quality of the included studies were assessed using the National Institutes of Health's Quality Assessment (NIHQA) tool for observational cohort and cross-sectional studies [ 37 ]. The NIHQA tool is a widely recognized and established tool for evaluating the quality of observational studies. Each study was evaluated based on specific criteria, and a total score was calculated to determine its overall quality. According to the criteria proposed by George et al. [ 38 ], studies that scored more than 80% of the total possible score were considered to be of good quality. Studies that scored between 60% and 79% were categorized as fair quality, while those scoring below 60% were deemed to be of lower quality. Meta-analysis Procedure In this study, a two-stage approach was adopted to analyze the data. First, a three-level meta-analysis was undertaken to account for the interdependence of effect sizes (ESs) through the stratification of ES variance into three hierarchical levels. These levels were defined as follows: the first level represented the variance among individual participants, the second level captured the variance among ESs originating from the same study, and the third level encompassed the variance among studies. The implementation of the three-level meta-analyses in our study was facilitated through the utilization of the metafor package [ 39 , 40 ]. and the heterogeneity was assessed using the I2 and Q statistics [ 41 ]. Sensitivity analysis was conducted to identify potential outliers, and publication bias was assessed using funnel plots, Egger's test. In case of the presence of publication bias, trim-fill analysis was used for correction. Second, the two-step method proposed by Cheung was employed to conduct a meta-analytic Structural Equation Modeling (MASEM) in order to explore the mediating effect of coping strategies on the relationship between work pressure and police occupational burnout [ 42 ], as well as the moderating effect of the geographical location of police work. The two-step method involves the following steps: Firstly, the joint correlation matrix of all study variables was computed through the amalgamation of correlation matrices from each individual study. Subsequently, the progressive covariance matrix derived from the correlation matrices was utilized as a weighted matrix and incorporated into the progressive free distribution structural equation model. The meta-analysis and meta-analytic structural equation model was executed using the metafor and metaSEM function packages [ 39 , 43 ]. RESULTS The initial search cutoff date is March 2023. We have also conducted an updated systematic search for any new studies on Chinese police personnel up to September 2023.The search yielded 2308 results. After excluding 812 duplicate records and 1211 works that did not meet the inclusion criteria (e.g., non-Chinese samples, univariate studies), 285 articles were further checked based on the full-text reading. Among these, 246 studies did not provide the necessary effect size data. Finally, a total of 39 relevant studies, comprising 124 effect sizes, were included in the meta-analysis, involving a total of 14089 police officers.The information of inclued studies are shown in Table 1 . The detailed process of document inclusion is illustrated in Fig. 1. The agreement on literature selection between the two coders was 89%. The reliability of coding was assessed using the Kappa statistic and the Intraclass Correlations Coefficient (ICC) magnitude, which ranged from 0.83 (Kappa) to 0.98 (ICC) [ 44 , 45 ]. Any coding discrepancies were resolved through consensus discussion. The detailed information on the included studies is shown in Table 1 . Table 1 Information of studies included in meta-analyses Author(s) Publication year k n Gender Region Police type Pressure measure Burnout measure Coping measure Age Zhao 2019 1 187 34 NA CO Other MBI-GS NA 35.5 Li 2012 1 476 19 SC PSP Other NA Other 35 Yao 2019 1 306 0 AH PSP NA MBI-GS Other NA Chen et al. 2016 1 450 0 GX CO NA MBI-GS CSQ NA Zhang 2012 1 398 15 HLJ CO NA MBI-GS CSQ 40.5 Chen 2012 1 216 12 SC PSP Other NA Other 38.5 Wang 2017 1 882 20 SX PSP Other NA CSQ 32.12 Deng 2016 1 195 15 HN PSP Other NA Other 30 Li 2009 1 210 11 YN PSP Other NA Other 35.5 Yi 2008 1 245 16 SD PSP NA MBI-GS CSQ 30.5 Li et al. 2010 1 388 NA XUAR PSP Other MBI-GS NA NA Yang 2008 1 905 12 Mixed PSP Other NA Other 34 Chen 2006 1 180 13 ZJ PSP NA MBI-GS CSQ NA Sun 2016 1 270 13 Tibet PSP & SWAT Other MBI-GS NA 30.5 Hou 2012 1 211 NA HA CO Other NA CSQ 36 Chen & Ding 2014 1 247 NA ZJ PSP Other NA Other NA Zhang 2011 1 571 NA HB PSP Other MBI-GS NA NA Xie et al. 2010 1 180 19 ZJ PSP EIR-Q NA CSQ 35.5 Wang et al. 2014 1 331 34 Mixed CO EIR-Q MBI-GS NA 34.4 Wang et al. 2014 1 521 NA ZJ PSP EIR-Q NA Other NA Huang 2020 1 340 0 JS PSP NA MBI-GS Other NA Gao et al. 2022 1 1024 33 LN CO Other Other NA NA Hang et al. 2012 3 221 20 AH PSP NA MBI-GS CSQ 36 Ma 2017 3 231 20 NA PSP EIR-Q MBI-GS Others 25 Yang et al. 2010 3 970 0 FJ CO Other MBI-GS CSQ 33.8 Liu 2009 3 98 NA GZ PSP NA MBI-GS CSQ 40 Zhang 2009 3 249 27 JX CO EIR-Q Other NA NA Zheng 2013 3 239 13 Mixed PSP Other NA CSQ 32.6 Wang et al. 2007 3 378 18 SD PSP Other MBI-GS NA 34.2 Xiong 2019 3 241 NA JX PSP NA Others NA 35.5 He 2012 4 334 66 Mixed CO EIR-Q Others NA 32.77 Zhao 2010 4 393 7 BJ PSP EIR-Q Other NA 40.5 Gong 2011 4 226 NA NA PSP NA MBI-GS CSQ NA Yang et al. 2021 6 251 NA SC PSP EIR-Q MBI-GS NA NA Zhang 2008 9 251 15 GD PSP Others NA CSQ 35.5 Zhang & Guo 2011 12 274 23 SX PSP NA MBI-GS CSQ 35.5 Liang 2004 12 340 20 SH PSP EIR-Q Others NA 37.83 Fang 2010 12 272 12 GD PSP NA Others CSQ 30 Pan 2014 15 388 36 SD PSP Other NA Other NA Note : 1.Region:XUAR = Xinjiang Uyghur Autonomous Region; SC = Sichuan; AH = Anhui; GX = Guangxi; HL = Heilongjinag; SN = Shaanxi; HN = Hunan; YN = Yunnan; SD = Shandong; HA = Henan; BJ = Beijing; ZJ = Zhejiang; XZ = Tibet; JS = Jiangsu; HB = Hebei; LN = Liaoning; FJ = Fujian; GZ = Guizhou; JX = Jiangxi; NX = Ningxia; SH = Shanghai; GD = Guangdong; Mixed = more than two provinces. 2. Gender = femal number/total samplenumber*100; 3. Police type: CO = correctional officer; PSP = public security police; PSP& SWAT = public security police & SWAT team; NA = not reported. 4.Scales: ERI-Q = Effort-Reward Imbalance Questionnaire, MBI-GS = Maslach burnout inventory-general survey; CSQ = Client satisfaction questionnaire; Others = Scales used less frequent (< 3 times). Main Effect The main effects analysis included 19 relevant studies and 50 effect sizes after eliminating one study due to an abnormal effect size detected through sensitivity analysis (studentized residuals > 2.5 and Cook's d value > 0.4). We employed a random effects model for the meta-analysis, as we anticipated the presence of moderators that might contribute to the heterogeneity of effect sizes. The combined effect size after eliminating the outlier is r = 0.410, with a 95% confidence interval of [0.347, 0.469]. The percentage of variance distributed at the within-study level ( I 2 level 2 ) is 79.04%; the percentage of variance distributed at the between-study level ( I 2 level 3 ) is 14.33%. According to Lipsey and Wilson's criterion, a correlation coefficient greater than 0.4 is considered a high correlation. The results support Hypothesis 1 , confirming a high positive correlation between job work pressure and police occupational burnout. To assess publication bias, a funnel diagram (Fig. 2) was examined, and Egger's test was conducted, resulting in a Z -value of -0.305 ( p = 0.761), indicating no significant publication bias. Additionally, the trim-fill method estimated number of missing studies on the right side is 0, further supporting the absence of serious publication bias. Result of Publication Bias Testing To assess publication bias, a funnel plot diagram (Fig. 2) was examined, and Egger's test was conducted, resulting in a Z -value of -0.305 ( p = 0.761), indicating no significant publication bias. Additionally, the trim-fill method estimated number of missing studies on the right side is 0, further supporting the absence of serious publication bias. The random effects model revealed heterogeneity among the included studies, with Q = 808.33 ( p < 0.001) and I 2 = 93.21%. This indicates that factors other than sampling error may be contributing to the observed heterogeneity in the study. Further exploration and analysis of potential sources of heterogeneity will be essential to better understand the variability across the included studies. Result of Meta-regression The included studies were published between 2004 and 2022. To examine the potential moderating effects of publication year, stress measures, burnout measures, the type of police force and age, meta-regression analyses were conducted. The results showed that publication year ( b = 0.03, p > 0.05), stress measures ( b = 0.04, p > 0.05), burnout measures ( b = -0.01, p > 0.05), the type of police force ( b = 0.02, p > 0.05), and age ( b = -0.01, p > 0.05) did not significantly moderate the overall relationship between occupational stress and burnout. The application of meta-regression to analyze the moderating effects of geographical region and gender across the studies revealed that these moderating effects were not significant ( b _ gender = -0.01, b _ region = -0.01, p > 0.05). Result of Mediation Effect Model Given that the samples in this study were drawn from different provinces in China, encompassing diverse police ranks and sex ratios, these factors are likely contributors to the observed heterogeneity. To account for the heterogeneity, a random effects model is employed to estimate the combined correlation matrix. The Q statistic ( p < 0.01) and I 2 (73–88%) values indicate significant heterogeneity in the correlation matrix. Subsequently, a structural equation model is constructed using the combined correlation matrix (see Table 2 ), with work pressure as the independent variable, positive coping style and negative coping style as the mediating variables, and occupational burnout as the dependent variable. The model is a saturated model, and thus the model fitting index is not presented. The regression coefficients are shown in Table 3 . From the model results, it is observed that with the inclusion of coping strategies, the product of the path coefficients indicating the influence of work pressure on occupational burnout through negative coping strategies is significant (indirect effect size = 0.03, 95% CI = [0.01, 0.05], p < 0.05). Additionally, the direct effect on occupational burnout remains significant (β = 0.41, 95% CI = [0.30, 0.51], p < 0.01). Thus, it can be inferred that the partial mediation effect of negative coping strategies positively mediates the impact of work pressure on occupational burnout among Chinese police officers. However, the path coefficient indicating the influence of work pressure on occupational burnout through positive coping strategies is not significant, indicating the absence of a mediating effect. Hypothesis 2 is partially validated. Table 2 The pooled correlation matrix Work pressure Positive coping style Negative coping style Positive coping style -0.17* [-0.32,-0.02] Negative coping style 0.24*** [0.13,0.34] -0.04 [-0.21,0.12] occupational burnout 0.45*** [0.35, 0.55] -0.14* [-0.27,-0.02] 0.23*** [0.17, 0.29] Note : * indicates p < 0.05; ** indicates p < 0.001; [ ] denotes the 95% confidence interval of the correlation coefficient. Table 3 The regression coefficients and indirect effect sizes of the mediation model Path Coefficient 95% CI Work pressure → Positive coping style -0.17 [-0.32,-0.02] Work pressure → Negative coping style 0.24 [0.13, 0.34] Positive coping style → Occupational burnout -0.07 [-0.21, 0.08] Negative coping style → Occupational burnout 0.13 [0.05, 0.21] Work pressure → occupational burnout(direct effect) 0.41 [0.30, 0.51] Work pressure → Positive coping style → Occupational burnout(indirect effect) 0.01 [-0.02, 0.04] Work pressure → negative coping style → Occupational burnout(indirect effect) 0.03 [0.01, 0.05] Note : Path coefficients are standardized regression coefficients or products; 95% CI represents the 95% confidence interval of the regression coefficient. DISCUSSION The study's findings provide valuable insights into the relationship between police job work pressure and occupational burnout. It supported the JD-R Theory in the group of police officers and expands the Transactional Model of Stress and Coping related to occupational burnout [ 46 , 47 ]. The finding of the mediating role of negative coping style revealed the mechanism of the relationship between work pressure and occupational burnout. From a practical standpoint, the research results can guide future interventions and offer useful references for relevant authorities to better understand the objective patterns of police work and psychology. This can lead to the implementation of appropriate measures and policies to enhance police mental health and job satisfaction. To reduce work pressure at the task assignment level, it is essential to consider the individual characteristics of police officers and match them with suitable work tasks. Implementing a reasonable leave system and ensuring fair work pay can also alleviate the perception of work pressure among police officers. Conducting specialized police training programs can be an effective strategy to educate officers about positive coping style skills. Encouraging the adoption of positive coping habits can significantly contribute to reducing occupational burnout. Moreover, targeted interventions should be implemented for police officers who show signs of occupational burnout or are experiencing severe burnout. This can include various objective measures to reduce work pressure and subjective training on coping styles to help alleviate burnout levels. By applying these interventions and adopting a comprehensive approach, police departments can effectively address occupational burnout issues among their officers, promote mental well-being, and foster a more positive work environment. Ultimately, this can lead to improved job satisfaction and performance among police personnel. Police Work Pressure and Occupational Burnout Based on an extensive meta-analysis of 39 literature sources, 68 independent sample studies, and a total of 19,980 subjects from diverse police samples across China, this study revealed a medium to a high positive correlation between work pressure and occupational burnout among Chinese police officers. These findings are consistent with previous individual studies by Wang et al. and Zheng [ 30 , 48 ]. Notably, the relationship between work pressure and occupational burnout was found to be relatively stable, unaffected by gender and region, indicating a consistent pattern across different contexts. We identified a robust positive correlation between work pressure and burnout, aligning seamlessly with the core tenets of the Job Demands-Resources (JD-R) theory and the Conservation of Resources (COR) theory [ 10 , 11 ]. The JD-R model posits that excessive job demands, such as heavy workloads, time constraints, and exposure to dangerous conditions, gradually deplete the psychological and emotional coping resources of police officers over time, leading to burnout. Empirical research has consistently demonstrated that the frequent demands inherent in police work, including high call volumes, mandatory overtime, and exposure to danger, contribute to emotional exhaustion and cynicism [ 49 , 50 ]. COR theory further elucidates that police officers must invest substantial effort and energy to cope with these sustained demands, gradually depleting their coping reserves and resulting in stress. Studies indicate that the daily expenditure of resources required to manage work-related pressures such as danger and trauma can exhaust officers' reserves and eventually lead to burnout [ 51 ]. In summary, the inherent high demands of modern policing deplete officers' psychological resources and diminish their coping abilities, providing robust theoretical and empirical support for the observed association between work pressure and occupational burnout in this study. Interventions aimed at mitigating occupational burnout should prioritize the modification of excessive job demands and enhancing the coping abilities of police officers to prevent resource depletion. The current analysis indicates the association between work demands and burnout remains consistent regardless of pay level variations between regions. A potential explanation is that police salaries have improved in recent years and are aligned with local costs of living [ 31 ]. This alignment may normalize the effects of absolute pay differences on burnout. Additionally, factors like perceived fairness of pay and job satisfaction may better capture the impacts of compensation on stress appraisals [ 50 ]. As this study did not assess officers' subjective evaluations of their remuneration, these unmeasured perspectives may account for the lack of moderating effects. The stability of the work pressure-burnout link across regions implies occupational demands play a greater role in burnout than geographic pay discrepancies. However, future research should directly assess officers' perceptions of pay equity and organizational justice regarding compensation to better understand if remuneration conditions influence the experience of work stress. Investigating multiple aspects of compensation beyond absolute pay rates can provide further insight into this issue. This study explored whether the relationship between work pressure and burnout differs across genders, as the demands of police work may vary for male and female officers. However, the meta-analysis did not find a significant moderating effect for gender. A potential reason is the underrepresentation of women in the source studies, which comprised predominantly male samples. Prior research on mixed-gender police samples indicates female officers face unique stressors like discrimination, harassment, and work-family conflict that contribute to burnout [ 32 ]. As women only constituted a small proportion of participants across the samples synthesized, gender differences in the experience of work stress may have been obscured. Cautions should be taken in interpreting the lack of moderating effects given this limitation. Further research utilizing more gender-balanced samples could provide greater insight into whether the work pressure-burnout association substantively differs between male and female police. Investigating the distinct occupational demands faced by each gender and their implications for burnout remains an important avenue for future exploration. The Mediating Role of Coping Style The negative correlation found between positive coping and work pressure/burnout aligns with Lazarus and Folkman's transactional theory, which indicates adaptive coping can alleviate strain [ 12 ]. However, contrary to predictions, this study did not find a significant moderating effect of job remuneration on the relationship between work pressure and burnout. A potential explanation from COR theory is that when work pressures become severely resource-draining, positive coping methods may no longer be effective in replenishing reserves [ 11 ]. However, negative coping did mediate this relationship, suggesting maladaptive responses like avoidance amplify burnout by allowing demands to intensify and resources to progressively deplete [ 52 ]. This highlights the need to curb maladaptive coping through training in problem-focused, support-seeking techniques. Overall, the findings provide a more robust test of the multidimensional stress-coping process by combining work conditions and coping responses. Yet, the partial mediating effects indicate additional variables and pathways likely influence the pressure-burnout relationship. Expanding beyond coping styles, future research should explore alternative mediators like self-efficacy, perceived control, and recovery experiences, guided by theories like the JD-R model [ 10 ]. Investigating multiple mediating mechanisms can provide a more comprehensive understanding of occupational burnout development. Theoretical and Practical Implications This study has important theoretical implications. First, it provides empirical support for the Job Demands-Resources (JD-R) theory and Conservation of Resources (COR) theory in explaining the positive association between work stressors and burnout [ 11 , 44 ]. Second, it expands the application of the Transactional Model of Stress and Coping [ 12 ] to occupational settings by demonstrating the mediating effect of negative coping. Third, this research lays the foundation for investigating additional mediators like self-efficacy and moderators like social support as proposed in JD-R theory [ 10 ]. Finally, the focus on an understudied cultural context advances theoretical understanding beyond Western settings. The findings of this study have important practical implications. First, they provide guidance for police departments to design interventions to alleviate occupational burnout among police personnel. For example, measures can be taken to reduce work pressure by adjusting task assignments, improving shift schedules, increasing leave time, and enhancing salary and benefits. Second, targeted training can be conducted for police officers to help them develop positive coping strategies. Finally, for police who already exhibit severe symptoms of occupational burnout, approaches like counseling and psychotherapy can be adopted as interventions. By comprehensively implementing these measures, police systems can effectively improve the mental health of police personnel, strengthen work enthusiasm, and create a positive working atmosphere. Research Limitations and Prospects The current study utilizes a meta-analysis combined with a structural equation model to comprehensively explore the relationship between job work pressure and occupational burnout Chinese police officer context. It identifies the mediating role of negative coping styles in this relationship. However, some limitations in the included literature and data analysis methods should be acknowledged, which can guide future research directions. Firstly, while heterogeneity existed across studies, meta-regression did not reveal moderating effects of gender or region. This implies additional unpublished variables may account for the heterogeneity. However, the literature lacked adequate information to uncover potential moderators. Future research should incorporate more primary studies with expanded data to identify sources of heterogeneity. Including a wider range of original data and testing more moderators through meta-regression can elucidate boundary conditions affecting the work stress-burnout relationship in policing. Secondly, there is an imbalance in the number of studies that focus on work pressure, coping style, and occupational burnout. Most studies examine the impact of work pressure and occupational burnout or coping style and occupational burnout individually, with fewer studies exploring the combined effects of the three factors. This data deficiency may affect the stability of conclusions drawn from the meta-analytic structural equation model [ 53 ]. To address this issue, future research should aim to explore the interactions between work pressure and coping styles from a more comprehensive perspective. Thirdly, all studies included in the meta-analysis employ cross-sectional research methods, which do not allow for the establishment of a causal relationship between job work pressure and occupational burnout. Future studies should expand their exploration of other potential mediating paths and boundary conditions of police job work pressure affecting occupational burnout. Additionally, adopting more ecological research methods, such as longitudinal research, quasi-experimental research, and experimental research, can provide valuable insights into the causal relationship between the two variables. By addressing these limitations and conducting further research, a more comprehensive understanding of the relationship between job work pressure and occupational burnout among Chinese police officers can be achieved. This knowledge will contribute to the development of effective interventions and policies to promote police mental health and well-being. CONCLUSION The current review provides a quantitative synthesis of the relationship between work pressure and occupational burnout among Chinese police officers and delves into the underlying mechanisms of this association. The findings suggest that work pressure plays a crucial role as an antecedent variable to occupational burnout in Chinese police settings. The mechanism underlying this influence can be explained as follows: work pressure exerts an impact on negative coping styles, and subsequently, negative coping styles contribute to the development of occupational burnout. Declarations Acknowledgements Thanks to all the authors and the funder who contributed to this study. Author Contributions S.Z. and M.L. developed the research concept studies. Testing and data collection were performed by S.C and Y.Q.; the data analysis and interpretation in collaboration with D.J. S.Z. drafted the manuscript. M.L. edited the manuscript. The manuscript was supervised by M.L. All authors have read and agreed to the published version of the manuscript. Funding Supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (21XNH062) Availability of data and materials The data presented in this study are available on request from the first author. Ethics approval and consent to participate All methods were carried out in accordance with relevant guidelines and regulations. This study was approved by the Ethics Committee of Renmin University of China. All included subjects voluntary participated in our study and signed informed consents. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details Senlin Zhou 1 , Miaomiao Li 2 *, Siru Chen 3 , Daokui Jiang 4 , Ying Qu 5 1 Investigation Department, Hunan Police Academy, Changsha 410138, China 2 School of Economics and Management, Shanghai University of Political Science and Law, Shanghai, China 3 School of Business, Hunan Institute of Technology, Hengyang, China 4 Business School, Shandong Normal University, Jinan, China 5 Department of Psychology at Liaoning Normal University, dialian, China References Bianchi R, Schonfeld IS, Laurent E. Burnout-depression overlap: A review. Clin Psychol Rev . 2015;36:28–41. doi.org/10.1016/j.cpr.2015.01.004 Freudenberger HJ. Staff burn-out. J Soc Issues. 1974; 30(1):159–165. doi.org/10.1111/j.1540-4560.1974.tb00706.x Griffin JD, Sun IY. Do work–family conflict and resiliency mediate police stress and burnout: A study of state police officers. Am J Crim Justice . 2017;43(2):354–370. doi.org/10.1007/s12103-017-9401-y Wang Y, Zheng L, Hu T, Zheng Q. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3436081","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":239818561,"identity":"ae087d0d-5499-4e43-8e0b-167a5468952a","order_by":0,"name":"Senlin Zhou","email":"","orcid":"","institution":"Hunan Police Academy","correspondingAuthor":false,"prefix":"","firstName":"Senlin","middleName":"","lastName":"Zhou","suffix":""},{"id":239818562,"identity":"78927f2c-6c04-43e5-a72c-9537f4fa3460","order_by":1,"name":"Miaomiao Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBACPhCRAMRsQHzgg0GNHBt78wG8WtiQtDA+nFFwzJiP51gCYS1QwGzM84E5cZ5EjgJ+LezNRzc8qLGJ5mM/fEyax4AtvY0hh4HhR8U23Fp4jqXdSDiWltvGk5YmOcdAJreN4ewBxp4zt3Frkcgxu5HAdhioMsdM4o0BW24bY18CM2MbHi3y77/dSPgH1ML/xkyCx4A5nY0ZSOLVIsHDdiOxDahFIsfYEKg4gY2NkBaeNLMbiX1Av0g8S3w4w+CYYRsPW8JBfH7hZz/87OaPbza58/uTDxz48KdGXn7+44MPflTg1oIdHCBR/SgYBaNgFIwCNAAAUgtWTJxuHigAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai University of Political Science and Law","correspondingAuthor":true,"prefix":"","firstName":"Miaomiao","middleName":"","lastName":"Li","suffix":""},{"id":239818563,"identity":"f1b93845-8653-4ced-9802-a7f1bfed3b65","order_by":2,"name":"Siru Chen","email":"","orcid":"","institution":"Hunan Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Siru","middleName":"","lastName":"Chen","suffix":""},{"id":239818564,"identity":"ade4ac5e-e640-4ee4-803e-1a4f52b1ebd6","order_by":3,"name":"Daokui Jiang","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"prefix":"","firstName":"Daokui","middleName":"","lastName":"Jiang","suffix":""},{"id":239818565,"identity":"ff84c51d-4ead-48eb-84f0-8af8b9f463d6","order_by":4,"name":"Ying Qu","email":"","orcid":"","institution":"Liaoning Normal University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Qu","suffix":""}],"badges":[],"createdAt":"2023-10-12 08:14:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3436081/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3436081/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44738507,"identity":"af073139-71a1-4e7e-85b8-71cab36c6017","added_by":"auto","created_at":"2023-10-16 23:04:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79331,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the search\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3436081/v1/2a411aa200e3799306f3891a.jpg"},{"id":44738508,"identity":"da7fd91c-1cde-4a24-b84b-7d2069286482","added_by":"auto","created_at":"2023-10-16 23:04:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":173427,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure2funnelplot.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3436081/v1/d7ff774352bc838e51b116ec.jpg"},{"id":44739963,"identity":"10453071-dda0-4315-81d4-b7e1875f3cb5","added_by":"auto","created_at":"2023-10-16 23:12:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":588334,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3436081/v1/a1423d5b-a255-43a3-8646-a525c3bcc198.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Work Pressure, Coping Styles and Occupational Burnout Among Chinese Police Officers: A Meta-analytic Review","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eOccupational burnout has emerged as a significant concern in the field of occupational health, drawing extensive attention from scholars [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In particular, burnout among police officers has become an important research focus in recent years [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Studies have revealed a high prevalence of burnout among police personnel in China [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As Maslach and Leiter suggested, burnout is closely related to occupational factors such as work pressure [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, investigating the impact of police job characteristics like work pressure on burnout is crucial for understanding the mechanisms underlying burnout in this profession.\u003c/p\u003e \u003cp\u003ePrevious studies extensively explored the relationship between work pressure and burnout theoretically or empirically. However, the conclusions have been mixed and inconclusive [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For instance, while some studies have found a significant positive association between work pressure and burnout [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], others have reported weak or non-significant relationships [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Most previous studies have been conducted in Western cultural contexts, with limited research examining this issue in non-Western settings like China which has distinct cultural values. There is also a lack of research specifically focused on high-stress occupations such as policing. Additionally, the mechanisms through which work pressure influences burnout remain unclear and need further investigation.\u003c/p\u003e \u003cp\u003eFor the reasons aforementioned, there remain several critical research gaps to be addressed, including the lack of systematic reviews synthesizing the evidence on work pressure and burnout among Chinese police officer. Therefore, the present study aims to address these gaps by utilizing meta-analysis methods to systematically examine the relationship between work pressure and burnout outcomes in Chinese police officers. Drawing upon existing theories of occupational stress and burnout, this study will synthesize findings from previous empirical studies on this population to determine the overall strength and direction of the work pressure-burnout association. Moderator analyses will also be conducted to uncover potential variables that influence this relationship. Additionally, the mediating effects of coping strategies will be tested using meta-analytic structural equation modeling to explore the mechanisms linking work pressure to burnout dimensions. By addressing these questions through quantitative research synthesis, this study seeks to clarify the mixed findings on work pressure and burnout, elucidate underlying mechanisms, and provide evidence-based recommendations for mitigating burnout in high-stress policing occupations.\u003c/p\u003e \u003cp\u003eThis research makes several meaningful contributions to the literature. First, it enriches research on occupational burnout by focusing on the understudied Chinese cultural context and the highly stressful policing profession. Second, it helps clarify the mixed findings on work pressure and burnout by testing their relationship with a rigorous study design. Third, it elucidates the mechanisms underlying this relationship and identifies protective factors (e.g., coping, social support), thereby providing guidance for interventions. Finally, by investigating burnout antecedents and protective factors in the police occupation, the study offers practical implications for maintaining work enthusiasm and mitigating burnout effects under high pressure. Overall, this research advances theoretical understanding of work pressure, burnout, and their interrelationships, laying the groundwork for future research and informing strategies to promote police well-being.\u003c/p\u003e\n\u003ch3\u003eTheoretical Model of Work Pressure and Occupation Burnout\u003c/h3\u003e\n\u003cp\u003eThe primary theories that explain the association between work pressure and occupational burnout are the Job Demands-Resources (JD-R) and Conservation of Resources (COR) theory. The JD-R theory proposes that work pressure acts as a job demand which, when chronically high, can drain police officers' psychological and physiological resources over time, resulting in burnout [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Police work involves heavy workloads, long hours, and danger which impose excessive demands on officers.\u003c/p\u003e \u003cp\u003eThe COR theory further suggests that responding to these work pressures requires continual exertion of effort and energy by officers to cope. This constant resource expenditure to meet heavy work demands can slowly depleted their reserves needed to handle stress, leading to eventual burnout [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Together, the JD-R and COR theories indicate that the sustained demands of police work pressure can wear down officers' mental, emotional, and physical resources, reducing their capacity to cope and thus resulting in burnout.\u003c/p\u003e \u003cp\u003eLazarus and Folkman's transactional theory provides an explanatory model for why work pressure can influence burnout. The theory proposes that stress arises from an imbalance between demands (e.g. work pressure) and an individual's resources to cope with those demands [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Specifically, this theory posits that work stress results from an appraisal process, whereby an individual perceives certain work demands as exceeding their coping abilities. When faced with excessive organizational demands and pressure, employees may feel a lack of control and predictability, heightening their stress. Their negative cognitive appraisals and inadequate coping responses then lead to burnout. From three perspectives of theories, we tried to explore the relationship between work pressure and burnout, answering how and why between the relationship.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOccupational Burnout and Work Pressure\u003c/h2\u003e \u003cp\u003eOccupational burnout encompasses a range of adverse reactions arising from prolonged emotional and interpersonal stress, primarily characterized by emotional exhaustion, dehumanization, and a diminished sense of accomplishment [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Emotional exhaustion represents the core element of burnout, reflecting individuals' feelings of weariness and disinterest in their work. Dehumanization involves experiencing indifference and alienation towards others and one's work environment. A reduced sense of accomplishment refers to a diminished appraisal of one's own work efficiency and status, leading to feelings of inadequacy and doubts about one's work capabilities.\u003c/p\u003e \u003cp\u003eWork pressure refers to the internal tension experienced by an individual when facing physical or psychological threats, leading to the emergence of unpleasant or even distressing emotional states [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For police officers, work pressure arises from various sources, including the potential risks associated with dealing with criminals and the irregularity of working hours.\u003c/p\u003e \u003cp\u003eDrawing on the Job Demands-Resources (JD-R) model and Conservation of Resources (COR) theory, work pressure can lead to burnout through a process of resource depletion. The JD-R model posits that excessive job demands exhaust employees' mental and emotional resources, resulting in energy depletion and burnout [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, the COR theory suggests that loss of resources due to high job demands can lead to psychological strain and burnout [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For police officers confronting overwhelming work pressure, their cognitive resources for managing complex tasks, as well as emotional resources for regulating negative feelings, become progressively depleted. Consequently, they are left with inadequate resources to cope with continual demands. This sustained resource loss and inability to meet demands manifests in physical and emotional exhaustion - the core symptom of burnout [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Officers may also distance themselves emotionally and cognitively from their work as a way of coping, leading to cynicism and reduced personal accomplishment.\u003c/p\u003e \u003cp\u003eEmpirical evidence supports the linkage between work pressure and burnout in policing occupations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A meta-analysis by De Terte identified work pressure as a consistent predictor of police burnout across st DeTerteudies [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Survey research by Boles et al. also found a significant correlation between work stress and emotional exhaustion among law enforcement officers [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These findings align with the resource-based theoretical models. From the above theoretical statement and empirical support, hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is generated.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003eThe work pressure experienced by policeman significantly and positively predicts occupational burnout.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe Mediating Role of Coping Style in The Meta-analytic Structural Equation Model\u003c/h3\u003e\n\u003cp\u003eCoping style refers to the cognitive, emotional, and behavioral strategies that individuals adopt in response to external environmental stimuli, encompassing both positive and negative coping styles [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Coping serves as a key mediator in the role of work pressure on occupational burnout [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Specifically, the transactional theory states that burnout is the end result of a complex coping process initiated by chronic work stressors like work overload, role problems, and lack of autonomy [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. When police officers face sustained work pressure from heavy workloads, mandatory overtime, danger, and organizational demands, they have to continually expend cognitive, emotional, and social support coping resources to manage this stress. Over time, this can deplete and exhaust their coping reserves, especially since police work often provides insufficient recovery opportunities. Eventually, officers' diminished coping resources combine with the unrelenting demands of work pressure to cause burnout [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Coping responses play a central role in this process \u0026ndash; maladaptive strategies like avoidance, denial, venting, and self-blame can accelerate resource depletion and amplify the effects of work pressure on burnout. On the other hand, adaptive coping methods like problem-solving, seeking support, and positive reappraisal can buffer the impacts of demands before burnout develops. Therefore, the type of coping responses officers use in reaction to chronic work stressors can either mitigate or amplify the pathway between sustained work pressure and eventual burnout [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Sustained ineffective coping mechanism in managing workplace stressors lead to emotional exhaustion and eventual burnout.\u003c/p\u003e \u003cp\u003eEmpirical research corroborates that work pressure can predict occupational burnout either positively through negative coping styles or negatively through positive coping styles. For instance, Wang et al. found in their study on domestic civil servants that job work pressure exhibits a significant positive correlation with occupational burnout [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], while social support and coping styles demonstrate a significant negative correlation with occupational burnout. Similarly, Zheng employed multi-group confirmatory factor analysis and established that work pressure impacts occupational burnout through coping styles [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], revealing the existence of a relatively common and stable mechanism across regions. While the aforementioned theoretical and empirical studies offer insights into the influence of police job work pressure on occupational burnout and its underlying mechanisms, the relatively limited sample sizes of these studies warrant further verification of their universality. To address this gap, the present study employs meta-analysis techniques and meta-analytic structural equation modeling to comprehensively explore the aforementioned issues. Based on transactional theory and empirical studies, this study formulates the following hypothesis.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis\u003c/strong\u003e \u003cp\u003ea: The influence of police work pressure on occupational burnout is mediated by positive coping styles.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis\u003c/strong\u003e \u003cp\u003eb: The influence of police work pressure on occupational burnout is mediated by negative coping styles.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eThe Boundary Conditions of Work Pressure and Occupational Burnout Association\u003c/h3\u003e\n\u003cp\u003eAlthough occupational burnout is prevalent among the police group, some individuals in the working environment experience significant work pressure but do not exhibit behaviors or feelings of occupational burnout. This observation suggests the existence of potential protective factors that may buffer the impact of work pressure on burnout. The occupational burnout theory emphasizes that incongruity between job remuneration and job content constitutes a major factor affecting burnout, as it can generate perceptions of injustice among police officers. Consequently, regional disparities in economic development during our country's economic growth may lead to differences in salary and remuneration for police officers engaged in similar work content, which may potentially influence the relationship between work pressure and occupational burnout. In general, in settings with high pay, police officers may not experience burnout even in the presence of perceived work pressure. Conversely, lower pay could exacerbate the positive prediction of job work pressure on burnout. As the included studies did not directly report the subjects' salaries, the differences in pay primarily manifest in the variation of work regions, which could impact the extent to which work pressure affects occupational burnout. Consequently, this study introduces the work region as a moderating variable for an exploratory aim. Based on national policies, guidelines, and the socio-economic development status of the sample's respective regions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], regions were divided into three categories: the eastern coastal region (including 11 provinces and municipalities: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Hainan), the central emerging region (including 10 provinces and autonomous regions: Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hunan, Hubei, Guangxi), and the western development region (including 10 provinces and autonomous regions: Chongqing, Sichuan, Guizhou, Yunnan, Shanxi, Gansu, Ningxia, Tibet, Xinjiang, Qinghai).\u003c/p\u003e \u003cp\u003ePast research has noted that men and women tend to experience occupational burnout differently [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This suggests gender likely intersects with work pressure and burnout in complex ways. Men and women often face different cultural expectations, discrimination, and obstacles in the workplace, which can influence their experiences of demand and burnout. For instance, women may face extra pressures balancing work and family responsibilities, while men may feel pressure to be breadwinners. Different coping mechanisms and support systems across genders could also be at play. Examining gender differences exploratorily here may shed light on nuanced ways that masculine and feminine roles shape the link between demands and burnout. While definitive conclusions cannot be drawn, results could inform future hypotheses and theories on how gender moderates burnout processes and point to gender-specific interventions. Exploring complex variables like gender on an initial basis can thus lay the groundwork for more systematic investigation in future research.\u003c/p\u003e \u003cp\u003eWhile factors such as region, gender, and other variables like the type of police force, age, measure of pressure and burnout, publication year have the potential to moderate the work pressure-burnout association, the current theoretical and empirical evidence for their moderating effects is limited. Therefore, examining the moderating roles of these factors in the present meta-analysis is exploratory in nature, without specific a priori hypotheses. This preliminary investigation aims to provide directions for future research to further validate the boundary conditions of the occupational stress-burnout relationship in the context of Chinese police.\u003c/p\u003e\n\u003ch3\u003eResearch Questions\u003c/h3\u003e\n\u003cp\u003ePrevious research on work stress and occupational burnout has often focused on Western participants, specifically employees in corporate settings. Furthermore, Previous empirical studies on the relationship between police work pressure and occupational burnout in China have often focused on specific regional police samples [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It becomes challenging to generalize these research findings to the entire national police group. To address this limitation, more comprehensive research methods are needed to explore the broader relationship between work pressure and occupational burnout. Therefore, two key research questions emerge:\u003c/p\u003e \u003cp\u003eRQ1: What is the overall relationship between work pressure and burnout among Chinese police officers across studies?\u003c/p\u003e \u003cp\u003eRQ2: Do potential moderators, such as working region, influence the magnitude of the relationship between work pressure and burnout?\u003c/p\u003e \u003cp\u003eTo address these questions, the current study employs three-level meta-analysis to synthesize findings across studies on Chinese police samples. This integrative approach determines the \"true\" linkage between work pressure and burnout, while accounting for study-level confounding factors. Moderator analyses are also conducted to uncover variables that may impact this relationship. Additionally, meta-analytic structural equation modeling explores the mediating role of coping styles in the work pressure-burnout association, elucidating the mechanisms. By synthesizing the empirical evidence through advanced quantitative techniques, this study aims to clarify the effects of police work pressure on occupation burnout.\u003c/p\u003e"},{"header":"METHOD","content":"\u003cp\u003eFollowing the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], the search for relevant articles was conducted using a multi-step approach. The initial searches were performed in January 2023 and covered publications from database inception to December 2022. Firstly, two authors independently performed searches in various databases, including CNKI, Web of Science, PubMed, and PsychInfo. The search terms used were combinations of \"polic*\" OR \"law enforcement officer\" AND \"China\" OR \"Chinese\" AND \"pressure\" OR \"stress\" OR \"work overload\" AND \"burnout\" OR \"fatigue\" OR \" exhaustion\" OR \" disengagement\" OR \" work-induced apathy\" AND \" coping \" OR \" cognitive restructuring \" OR \" work-life balance\". These terms were applied to search titles, abstracts, and keywords within the databases. Secondly, to ensure a comprehensive search, additional relevant studies were identified by searching through Google Scholar. Thirdly, the reference lists of the obtained articles were thoroughly screened to identify any other potentially relevant studies that may have been missed during the initial searches. This rigorous search process aims to include all relevant studies related to the relationship between work pressure and occupational burnout in the Chinese police context.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInclusion Criteria\u003c/h2\u003e \u003cp\u003eTo ensure that the selected studies are directly relevant to the relationship between work pressure and occupational burnout in the context of Chinese mainland police and that the statistical measures used are appropriate for synthesizing the findings across different studies. The inclusion criteria for the literature in this meta-analysis are made as follows: (a) Studies that measure at least two variables related to work pressure, occupational burnout, or coping styles and report relevant statistical measures, such as Pearson correlation coefficients or other effect sizes (e.g., \u003cem\u003eF\u003c/em\u003e, \u003cem\u003et\u003c/em\u003e, Cohen's d, Hegen's \u003cem\u003eg\u003c/em\u003e, and f values) that can be converted into Pearson correlation coefficients. (b) These measures must be appropriate for examining the associations between the mentioned variables. (c) Only studies that specifically focus on police officers working within mainland China will be considered for inclusion. Figure\u0026nbsp;1 displays detailed information regarding the selection of studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData Coding\u003c/h2\u003e \u003cp\u003eIn this meta-analysis, each included study's relevant data were coded following specific criteria. The following information was collected for each study: (a) Author: The names of the authors who conducted the study were recorded. (b) Publication age: The publication year of each study was noted to identify the timeframe of the research. (c) Sample size: The total number of participants in each study's sample was recorded. (d) Sex ratio: The proportion of female participants in each study's sample was calculated by dividing the number of females by the total population and multiplying by 100. (e) Region of work: The specific province or region where the study was conducted and where the Chinese mainland police officers worked was noted. (f) Correlation coefficient: The reported correlation coefficient between work pressure, occupational burnout, or coping styles in each study was recorded. (g) Variable measurement tool: The tools or scales used to measure work pressure, occupational burnout, and coping styles in each study were noted. (h) Tool's Cronbach\u0026rsquo;s Coefficient: The Cronbach\u0026rsquo;s coefficient of reliability for the measurement tools used in each study was recorded to assess the reliability of the scales.\u003c/p\u003e \u003cp\u003eTo handle studies that reported correlations between multiple dimensions of variables or between dimensions and the total score of the scale, a specific approach was employed. For studies reporting correlations between multiple dimensions, the Fisher's \u003cem\u003eZ\u003c/em\u003e scores were calculated for each dimension's correlation and then averaged to obtain the final bivariate correlation. For studies reporting both dimensional correlations and total score correlations, only the total score correlation was used, and it was converted to Fisher's \u003cem\u003eZ\u003c/em\u003e score.\u003c/p\u003e \u003cp\u003eTo ensure accuracy and consistency in data coding, two researchers received training in meta-analysis coding and specific research topics. Coding was conducted according to a coding standard manual developed by the research team, resulting in a coding consistency rate of 94.7%. In cases of coding discrepancies, the researchers discussed and reached a consensus to resolve any disagreements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStudy Quality Evaluation\u003c/h2\u003e \u003cp\u003eIn this meta-analysis, the potential bias and study quality of the included studies were assessed using the National Institutes of Health's Quality Assessment (NIHQA) tool for observational cohort and cross-sectional studies [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The NIHQA tool is a widely recognized and established tool for evaluating the quality of observational studies. Each study was evaluated based on specific criteria, and a total score was calculated to determine its overall quality. According to the criteria proposed by George et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], studies that scored more than 80% of the total possible score were considered to be of good quality. Studies that scored between 60% and 79% were categorized as fair quality, while those scoring below 60% were deemed to be of lower quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMeta-analysis Procedure\u003c/h2\u003e \u003cp\u003eIn this study, a two-stage approach was adopted to analyze the data. First, a three-level meta-analysis was undertaken to account for the interdependence of effect sizes (ESs) through the stratification of ES variance into three hierarchical levels. These levels were defined as follows: the first level represented the variance among individual participants, the second level captured the variance among ESs originating from the same study, and the third level encompassed the variance among studies. The implementation of the three-level meta-analyses in our study was facilitated through the utilization of the metafor package [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. and the heterogeneity was assessed using the I2 and \u003cem\u003eQ\u003c/em\u003e statistics [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Sensitivity analysis was conducted to identify potential outliers, and publication bias was assessed using funnel plots, Egger's test. In case of the presence of publication bias, trim-fill analysis was used for correction.\u003c/p\u003e \u003cp\u003eSecond, the two-step method proposed by Cheung was employed to conduct a meta-analytic Structural Equation Modeling (MASEM) in order to explore the mediating effect of coping strategies on the relationship between work pressure and police occupational burnout [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], as well as the moderating effect of the geographical location of police work. The two-step method involves the following steps: Firstly, the joint correlation matrix of all study variables was computed through the amalgamation of correlation matrices from each individual study. Subsequently, the progressive covariance matrix derived from the correlation matrices was utilized as a weighted matrix and incorporated into the progressive free distribution structural equation model. The meta-analysis and meta-analytic structural equation model was executed using the metafor and metaSEM function packages [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe initial search cutoff date is March 2023. We have also conducted an updated systematic search for any new studies on Chinese police personnel up to September 2023.The search yielded 2308 results. After excluding 812 duplicate records and 1211 works that did not meet the inclusion criteria (e.g., non-Chinese samples, univariate studies), 285 articles were further checked based on the full-text reading. Among these, 246 studies did not provide the necessary effect size data. Finally, a total of 39 relevant studies, comprising 124 effect sizes, were included in the meta-analysis, involving a total of 14089 police officers.The information of inclued studies are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The detailed process of document inclusion is illustrated in Fig.\u0026nbsp;1. The agreement on literature selection between the two coders was 89%. The reliability of coding was assessed using the Kappa statistic and the Intraclass Correlations Coefficient (ICC) magnitude, which ranged from 0.83 (Kappa) to 0.98 (ICC) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Any coding discrepancies were resolved through consensus discussion. The detailed information on the included studies is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation of studies included in meta-analyses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublication year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ek\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePolice\u003c/p\u003e \u003cp\u003etype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePressure\u003c/p\u003e \u003cp\u003emeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBurnout measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCoping measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\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\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2012\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\u003e476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\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\u003e306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\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\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2012\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\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHLJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2012\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\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2017\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\u003e882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e32.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeng\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\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\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2009\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\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2008\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\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2010\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\u003e388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eXUAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2008\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\u003e905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2006\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\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\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\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTibet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP \u0026amp; SWAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2012\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\u003e211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen \u0026amp; Ding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2014\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\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2011\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\u003e571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXie et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2010\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\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEIR-Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2014\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\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEIR-Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2014\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\u003e521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEIR-Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\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\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGao et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\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\u003e1024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHang et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEIR-Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYang et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEIR-Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZheng\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXiong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2012\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\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEIR-Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e32.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2010\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\u003e393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEIR-Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2011\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\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYang et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEIR-Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2008\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\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang \u0026amp; Guo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2011\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\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2004\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\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEIR-Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e37.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2010\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\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePSP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eNote\u003c/em\u003e: 1.Region:XUAR\u0026thinsp;=\u0026thinsp;Xinjiang Uyghur Autonomous Region; SC\u0026thinsp;=\u0026thinsp;Sichuan; AH\u0026thinsp;=\u0026thinsp;Anhui; GX\u0026thinsp;=\u0026thinsp;Guangxi; HL\u0026thinsp;=\u0026thinsp;Heilongjinag; SN\u0026thinsp;=\u0026thinsp;Shaanxi; HN\u0026thinsp;=\u0026thinsp;Hunan; YN\u0026thinsp;=\u0026thinsp;Yunnan; SD\u0026thinsp;=\u0026thinsp;Shandong; HA\u0026thinsp;=\u0026thinsp;Henan; BJ\u0026thinsp;=\u0026thinsp;Beijing; ZJ\u0026thinsp;=\u0026thinsp;Zhejiang; XZ\u0026thinsp;=\u0026thinsp;Tibet; JS\u0026thinsp;=\u0026thinsp;Jiangsu; HB\u0026thinsp;=\u0026thinsp;Hebei; LN\u0026thinsp;=\u0026thinsp;Liaoning; FJ\u0026thinsp;=\u0026thinsp;Fujian; GZ\u0026thinsp;=\u0026thinsp;Guizhou; JX\u0026thinsp;=\u0026thinsp;Jiangxi; NX\u0026thinsp;=\u0026thinsp;Ningxia; SH\u0026thinsp;=\u0026thinsp;Shanghai; GD\u0026thinsp;=\u0026thinsp;Guangdong; Mixed\u0026thinsp;=\u0026thinsp;more than two provinces. 2. Gender\u0026thinsp;=\u0026thinsp;femal number/total samplenumber*100; 3. Police type: CO\u0026thinsp;=\u0026thinsp;correctional officer; PSP\u0026thinsp;=\u0026thinsp;public security police; PSP\u0026amp; SWAT\u0026thinsp;=\u0026thinsp;public security police \u0026amp; SWAT team; NA\u0026thinsp;=\u0026thinsp;not reported. 4.Scales: ERI-Q\u0026thinsp;=\u0026thinsp;Effort-Reward Imbalance Questionnaire, MBI-GS\u0026thinsp;=\u0026thinsp;Maslach burnout inventory-general survey; CSQ\u0026thinsp;=\u0026thinsp;Client satisfaction questionnaire; Others\u0026thinsp;=\u0026thinsp;Scales used less frequent (\u0026lt;\u0026thinsp;3 times).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMain Effect\u003c/h2\u003e \u003cp\u003eThe main effects analysis included 19 relevant studies and 50 effect sizes after eliminating one study due to an abnormal effect size detected through sensitivity analysis (studentized residuals\u0026thinsp;\u0026gt;\u0026thinsp;2.5 and Cook's \u003cem\u003ed\u003c/em\u003e value\u0026thinsp;\u0026gt;\u0026thinsp;0.4). We employed a random effects model for the meta-analysis, as we anticipated the presence of moderators that might contribute to the heterogeneity of effect sizes. The combined effect size after eliminating the outlier is \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.410, with a 95% confidence interval of [0.347, 0.469]. The percentage of variance distributed at the within-study level (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003elevel 2\u003c/sub\u003e) is 79.04%; the percentage of variance distributed at the between-study level (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003elevel 3\u003c/sub\u003e) is 14.33%. According to Lipsey and Wilson's criterion, a correlation coefficient greater than 0.4 is considered a high correlation. The results support Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, confirming a high positive correlation between job work pressure and police occupational burnout.\u003c/p\u003e \u003cp\u003eTo assess publication bias, a funnel diagram (Fig.\u0026nbsp;2) was examined, and Egger's test was conducted, resulting in a \u003cem\u003eZ\u003c/em\u003e-value of -0.305 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.761), indicating no significant publication bias. Additionally, the trim-fill method estimated number of missing studies on the right side is 0, further supporting the absence of serious publication bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eResult of Publication Bias Testing\u003c/h2\u003e \u003cp\u003eTo assess publication bias, a funnel plot diagram (Fig.\u0026nbsp;2) was examined, and Egger's test was conducted, resulting in a \u003cem\u003eZ\u003c/em\u003e-value of -0.305 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.761), indicating no significant publication bias. Additionally, the trim-fill method estimated number of missing studies on the right side is 0, further supporting the absence of serious publication bias.\u003c/p\u003e \u003cp\u003eThe random effects model revealed heterogeneity among the included studies, with \u003cem\u003eQ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;808.33 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;93.21%. This indicates that factors other than sampling error may be contributing to the observed heterogeneity in the study. Further exploration and analysis of potential sources of heterogeneity will be essential to better understand the variability across the included studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eResult of Meta-regression\u003c/h2\u003e \u003cp\u003eThe included studies were published between 2004 and 2022. To examine the potential moderating effects of publication year, stress measures, burnout measures, the type of police force and age, meta-regression analyses were conducted. The results showed that publication year (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), stress measures (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), burnout measures (\u003cem\u003eb\u003c/em\u003e = -0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), the type of police force (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and age (\u003cem\u003eb\u003c/em\u003e = -0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) did not significantly moderate the overall relationship between occupational stress and burnout. The application of meta-regression to analyze the moderating effects of geographical region and gender across the studies revealed that these moderating effects were not significant (\u003cem\u003eb\u003c/em\u003e_\u003csub\u003egender\u003c/sub\u003e = -0.01, \u003cem\u003eb\u003c/em\u003e_\u003csub\u003eregion\u003c/sub\u003e = -0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eResult of Mediation Effect Model\u003c/h2\u003e \u003cp\u003eGiven that the samples in this study were drawn from different provinces in China, encompassing diverse police ranks and sex ratios, these factors are likely contributors to the observed heterogeneity. To account for the heterogeneity, a random effects model is employed to estimate the combined correlation matrix. The \u003cem\u003eQ\u003c/em\u003e statistic (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (73\u0026ndash;88%) values indicate significant heterogeneity in the correlation matrix.\u003c/p\u003e \u003cp\u003eSubsequently, a structural equation model is constructed using the combined correlation matrix (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with work pressure as the independent variable, positive coping style and negative coping style as the mediating variables, and occupational burnout as the dependent variable. The model is a saturated model, and thus the model fitting index is not presented. The regression coefficients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. From the model results, it is observed that with the inclusion of coping strategies, the product of the path coefficients indicating the influence of work pressure on occupational burnout through negative coping strategies is significant (indirect effect size\u0026thinsp;=\u0026thinsp;0.03, 95% CI = [0.01, 0.05], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, the direct effect on occupational burnout remains significant (β\u0026thinsp;=\u0026thinsp;0.41, 95% CI = [0.30, 0.51], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Thus, it can be inferred that the partial mediation effect of negative coping strategies positively mediates the impact of work pressure on occupational burnout among Chinese police officers. However, the path coefficient indicating the influence of work pressure on occupational burnout through positive coping strategies is not significant, indicating the absence of a mediating effect. Hypothesis 2 is partially validated.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe pooled correlation matrix\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=\"\u0026minus;\" 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\u003eWork pressure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive coping style\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative coping style\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive coping style\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.17* [-0.32,-0.02]\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\u003eNegative coping style\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24*** [0.13,0.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-0.04 [-0.21,0.12]\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 burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45*** [0.35, 0.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e-0.14* [-0.27,-0.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23*** [0.17, 0.29]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e: * indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; [ ] denotes the 95% confidence interval of the correlation coefficient.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe regression coefficients and indirect effect sizes of the mediation model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork pressure \u0026rarr; Positive coping style\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.32,-0.02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork pressure \u0026rarr; Negative coping style\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.13, 0.34]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive coping style \u0026rarr; Occupational burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.21, 0.08]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative coping style \u0026rarr; Occupational burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.05, 0.21]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork pressure \u0026rarr; occupational burnout(direct effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.30, 0.51]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork pressure \u0026rarr; Positive coping style \u0026rarr; Occupational burnout(indirect effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-0.02, 0.04]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork pressure \u0026rarr; negative coping style \u0026rarr; Occupational burnout(indirect effect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.01, 0.05]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote\u003c/em\u003e: Path coefficients are standardized regression coefficients or products; 95% CI represents the 95% confidence interval of the regression coefficient.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe study's findings provide valuable insights into the relationship between police job work pressure and occupational burnout. It supported the JD-R Theory in the group of police officers and expands the Transactional Model of Stress and Coping related to occupational burnout [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The finding of the mediating role of negative coping style revealed the mechanism of the relationship between work pressure and occupational burnout.\u003c/p\u003e \u003cp\u003eFrom a practical standpoint, the research results can guide future interventions and offer useful references for relevant authorities to better understand the objective patterns of police work and psychology. This can lead to the implementation of appropriate measures and policies to enhance police mental health and job satisfaction. To reduce work pressure at the task assignment level, it is essential to consider the individual characteristics of police officers and match them with suitable work tasks. Implementing a reasonable leave system and ensuring fair work pay can also alleviate the perception of work pressure among police officers. Conducting specialized police training programs can be an effective strategy to educate officers about positive coping style skills. Encouraging the adoption of positive coping habits can significantly contribute to reducing occupational burnout. Moreover, targeted interventions should be implemented for police officers who show signs of occupational burnout or are experiencing severe burnout. This can include various objective measures to reduce work pressure and subjective training on coping styles to help alleviate burnout levels. By applying these interventions and adopting a comprehensive approach, police departments can effectively address occupational burnout issues among their officers, promote mental well-being, and foster a more positive work environment. Ultimately, this can lead to improved job satisfaction and performance among police personnel.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePolice Work Pressure and Occupational Burnout\u003c/h2\u003e \u003cp\u003eBased on an extensive meta-analysis of 39 literature sources, 68 independent sample studies, and a total of 19,980 subjects from diverse police samples across China, this study revealed a medium to a high positive correlation between work pressure and occupational burnout among Chinese police officers. These findings are consistent with previous individual studies by Wang et al. and Zheng [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Notably, the relationship between work pressure and occupational burnout was found to be relatively stable, unaffected by gender and region, indicating a consistent pattern across different contexts.\u003c/p\u003e \u003cp\u003eWe identified a robust positive correlation between work pressure and burnout, aligning seamlessly with the core tenets of the Job Demands-Resources (JD-R) theory and the Conservation of Resources (COR) theory [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The JD-R model posits that excessive job demands, such as heavy workloads, time constraints, and exposure to dangerous conditions, gradually deplete the psychological and emotional coping resources of police officers over time, leading to burnout. Empirical research has consistently demonstrated that the frequent demands inherent in police work, including high call volumes, mandatory overtime, and exposure to danger, contribute to emotional exhaustion and cynicism [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. COR theory further elucidates that police officers must invest substantial effort and energy to cope with these sustained demands, gradually depleting their coping reserves and resulting in stress. Studies indicate that the daily expenditure of resources required to manage work-related pressures such as danger and trauma can exhaust officers' reserves and eventually lead to burnout [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In summary, the inherent high demands of modern policing deplete officers' psychological resources and diminish their coping abilities, providing robust theoretical and empirical support for the observed association between work pressure and occupational burnout in this study. Interventions aimed at mitigating occupational burnout should prioritize the modification of excessive job demands and enhancing the coping abilities of police officers to prevent resource depletion.\u003c/p\u003e \u003cp\u003eThe current analysis indicates the association between work demands and burnout remains consistent regardless of pay level variations between regions. A potential explanation is that police salaries have improved in recent years and are aligned with local costs of living [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This alignment may normalize the effects of absolute pay differences on burnout. Additionally, factors like perceived fairness of pay and job satisfaction may better capture the impacts of compensation on stress appraisals [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. As this study did not assess officers' subjective evaluations of their remuneration, these unmeasured perspectives may account for the lack of moderating effects. The stability of the work pressure-burnout link across regions implies occupational demands play a greater role in burnout than geographic pay discrepancies. However, future research should directly assess officers' perceptions of pay equity and organizational justice regarding compensation to better understand if remuneration conditions influence the experience of work stress. Investigating multiple aspects of compensation beyond absolute pay rates can provide further insight into this issue.\u003c/p\u003e \u003cp\u003eThis study explored whether the relationship between work pressure and burnout differs across genders, as the demands of police work may vary for male and female officers. However, the meta-analysis did not find a significant moderating effect for gender. A potential reason is the underrepresentation of women in the source studies, which comprised predominantly male samples. Prior research on mixed-gender police samples indicates female officers face unique stressors like discrimination, harassment, and work-family conflict that contribute to burnout [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As women only constituted a small proportion of participants across the samples synthesized, gender differences in the experience of work stress may have been obscured. Cautions should be taken in interpreting the lack of moderating effects given this limitation. Further research utilizing more gender-balanced samples could provide greater insight into whether the work pressure-burnout association substantively differs between male and female police. Investigating the distinct occupational demands faced by each gender and their implications for burnout remains an important avenue for future exploration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe Mediating Role of Coping Style\u003c/h2\u003e \u003cp\u003eThe negative correlation found between positive coping and work pressure/burnout aligns with Lazarus and Folkman's transactional theory, which indicates adaptive coping can alleviate strain [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, contrary to predictions, this study did not find a significant moderating effect of job remuneration on the relationship between work pressure and burnout. A potential explanation from COR theory is that when work pressures become severely resource-draining, positive coping methods may no longer be effective in replenishing reserves [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, negative coping did mediate this relationship, suggesting maladaptive responses like avoidance amplify burnout by allowing demands to intensify and resources to progressively deplete [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This highlights the need to curb maladaptive coping through training in problem-focused, support-seeking techniques.\u003c/p\u003e \u003cp\u003eOverall, the findings provide a more robust test of the multidimensional stress-coping process by combining work conditions and coping responses. Yet, the partial mediating effects indicate additional variables and pathways likely influence the pressure-burnout relationship. Expanding beyond coping styles, future research should explore alternative mediators like self-efficacy, perceived control, and recovery experiences, guided by theories like the JD-R model [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Investigating multiple mediating mechanisms can provide a more comprehensive understanding of occupational burnout development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical and Practical Implications\u003c/h2\u003e \u003cp\u003eThis study has important theoretical implications. First, it provides empirical support for the Job Demands-Resources (JD-R) theory and Conservation of Resources (COR) theory in explaining the positive association between work stressors and burnout [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Second, it expands the application of the Transactional Model of Stress and Coping [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] to occupational settings by demonstrating the mediating effect of negative coping. Third, this research lays the foundation for investigating additional mediators like self-efficacy and moderators like social support as proposed in JD-R theory [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Finally, the focus on an understudied cultural context advances theoretical understanding beyond Western settings.\u003c/p\u003e \u003cp\u003eThe findings of this study have important practical implications. First, they provide guidance for police departments to design interventions to alleviate occupational burnout among police personnel. For example, measures can be taken to reduce work pressure by adjusting task assignments, improving shift schedules, increasing leave time, and enhancing salary and benefits. Second, targeted training can be conducted for police officers to help them develop positive coping strategies. Finally, for police who already exhibit severe symptoms of occupational burnout, approaches like counseling and psychotherapy can be adopted as interventions. By comprehensively implementing these measures, police systems can effectively improve the mental health of police personnel, strengthen work enthusiasm, and create a positive working atmosphere.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eResearch Limitations and Prospects\u003c/h2\u003e \u003cp\u003eThe current study utilizes a meta-analysis combined with a structural equation model to comprehensively explore the relationship between job work pressure and occupational burnout Chinese police officer context. It identifies the mediating role of negative coping styles in this relationship. However, some limitations in the included literature and data analysis methods should be acknowledged, which can guide future research directions. Firstly, while heterogeneity existed across studies, meta-regression did not reveal moderating effects of gender or region. This implies additional unpublished variables may account for the heterogeneity. However, the literature lacked adequate information to uncover potential moderators. Future research should incorporate more primary studies with expanded data to identify sources of heterogeneity. Including a wider range of original data and testing more moderators through meta-regression can elucidate boundary conditions affecting the work stress-burnout relationship in policing. Secondly, there is an imbalance in the number of studies that focus on work pressure, coping style, and occupational burnout. Most studies examine the impact of work pressure and occupational burnout or coping style and occupational burnout individually, with fewer studies exploring the combined effects of the three factors. This data deficiency may affect the stability of conclusions drawn from the meta-analytic structural equation model [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. To address this issue, future research should aim to explore the interactions between work pressure and coping styles from a more comprehensive perspective. Thirdly, all studies included in the meta-analysis employ cross-sectional research methods, which do not allow for the establishment of a causal relationship between job work pressure and occupational burnout. Future studies should expand their exploration of other potential mediating paths and boundary conditions of police job work pressure affecting occupational burnout. Additionally, adopting more ecological research methods, such as longitudinal research, quasi-experimental research, and experimental research, can provide valuable insights into the causal relationship between the two variables.\u003c/p\u003e \u003cp\u003eBy addressing these limitations and conducting further research, a more comprehensive understanding of the relationship between job work pressure and occupational burnout among Chinese police officers can be achieved. This knowledge will contribute to the development of effective interventions and policies to promote police mental health and well-being.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe current review provides a quantitative synthesis of the relationship between work pressure and occupational burnout among Chinese police officers and delves into the underlying mechanisms of this association. The findings suggest that work pressure plays a crucial role as an antecedent variable to occupational burnout in Chinese police settings. The mechanism underlying this influence can be explained as follows: work pressure exerts an impact on negative coping styles, and subsequently, negative coping styles contribute to the development of occupational burnout.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to all the authors and the funder who contributed to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.Z. and M.L. developed the research concept studies. Testing and data collection were performed by S.C and Y.Q.; the data analysis and interpretation in collaboration with D.J. S.Z. drafted the manuscript. M.L. edited the manuscript. The manuscript was supervised by M.L. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (21XNH062)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are available on request from the first author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods were carried out in accordance with relevant guidelines and regulations. This study was approved by the Ethics Committee of Renmin University of China. All included subjects voluntary participated in our study and signed informed consents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSenlin Zhou\u003csup\u003e1\u003c/sup\u003e, Miaomiao Li\u003csup\u003e2\u003c/sup\u003e*, Siru Chen\u003csup\u003e3\u003c/sup\u003e, Daokui Jiang\u003csup\u003e4\u003c/sup\u003e, \u0026nbsp;Ying Qu\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eInvestigation Department, Hunan Police Academy, Changsha 410138, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eSchool of Economics and Management, Shanghai University of Political Science and Law, Shanghai,\u0026nbsp;China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eSchool of Business, Hunan Institute of Technology, Hengyang,\u0026nbsp;China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eBusiness School, Shandong Normal University, Jinan, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eDepartment of Psychology at Liaoning Normal University, dialian, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBianchi R, Schonfeld IS, Laurent E. 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Methods \u003c/em\u003e2021;12(4): 590\u0026ndash;606. doi.org/10.1002/jrsm.1498\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"work pressure, occupational burnout, coping styles, meta-analytic, meta-analytic structural equation modeling (MASEM)","lastPublishedDoi":"10.21203/rs.3.rs-3436081/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3436081/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study conducted a comprehensive meta-analysis to systematically review the relationship between occupational burnout and work pressure among Chinese police officers. Additionally, the study explored the mediating role of coping styles using a meta-analytic structural equation model. The investigation involved a thorough search of CNKI, PubMed, PsychInfo, Web of Science, and Google Scholar databases, resulting in the identification of a total of 39 studies with 124 effect sizes and 14089 police officers. The findings revealed a positive correlation between work pressure and occupational burnout among Chinese police officers (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.410, 95% CI = [0.347, 0.469]). Furthermore, negative coping styles mediate the relationship between work pressure and occupational burnout. Importantly, these conclusions held true across various work regions for police officers. These results provide insights into the relationship magnitude between work pressure and occupational burnout in Chinese police work and shed light on the underlying mechanisms. Based on these findings, it is recommended that interventions focusing on reducing work pressure and fostering positive coping styles be implemented to mitigate occupational burnout among police officers.\u003c/p\u003e","manuscriptTitle":"Work Pressure, Coping Styles and Occupational Burnout Among Chinese Police Officers: A Meta-analytic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-16 23:04:19","doi":"10.21203/rs.3.rs-3436081/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-05T16:47:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-28T13:51:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"914a426e-3e43-4ecd-883e-a46872518338","date":"2024-02-27T16:29:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23803091-4a1b-4196-a298-b0373f0eb6ee","date":"2024-02-17T04:31:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6fb71f3b-da43-4783-a8d7-c05d2692f899","date":"2024-02-14T15:34:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-12-19T10:26:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-10-26T07:59:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-10-13T05:37:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-10-13T05:37:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2023-10-12T08:09:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c7c49e4d-d3f7-4601-afd8-cffb606c7ee5","owner":[],"postedDate":"October 16th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-09T12:41:27+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-16 23:04:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3436081","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3436081","identity":"rs-3436081","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}