Workforce Stability as a Cornerstone of Public Health Sustainability: A Latent Profile Analysis of Turnover Intention among CDC Staff in China

Workforce Stability as a Cornerstone of Public Health Sustainability: A Latent Profile Analysis of Turnover Intention among CDC Staff in China | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 28 January 2026 V1 Latest version Share on Workforce Stability as a Cornerstone of Public Health Sustainability: A Latent Profile Analysis of Turnover Intention among CDC Staff in China Authors : Xinyu Liu 0000-0002-4401-5015 , Yanmin Cao , Xiaoting Wang , and Yuheng Xin [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176957835.59509945/v1 113 views 45 downloads Contents Abstract 2.1. Participants 2.1.2. Job Satisfaction 2.1.3. Social Support 2.1.4. Statistical Analysis 3.1. Latent Profile Analysis on Turnover Intention 3.2. Inter-Profile Characteristic Differences 3.3. Multinomial Logistic Regression of Turnover Intention Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background/Objectives : Sustainable public health systems rely on a stable and motivated workforce. However, the mental status and turnover intention of CDC staff, who play a foundational role in national health security, remain underexplored. This study examines turnover intention profiles among CDC staff in China and identifies factors affecting public health workforce sustainability. Methods : We conducted a cross-sectional survey among 1,057 staff members from 14 CDCs in a major city in Shandong Province, China. Self-administered questionnaires measured turnover intention (using a validated 4-item scale), job satisfaction, social support, and demographic variables. Latent profile analysis (LPA) was used to identify distinct turnover intention profiles, followed by multinomial logistic regression to examine associated factors. Results : Four turnover intention profiles were identified: Low Turnover Intention (29.4%), High Dissatisfaction but Low Turnover Intention (22.7%), High Boredom but Low Turnover Intention (39.0%), and High Resignation Intention (8.9%). Staff with low job satisfaction and low social support were significantly more likely to fall into high-risk profiles. Other associated factors included administrative position, employment type, age, self-rated health, and overtime. These findings highlight the risks to public health workforce sustainability posed by organizational strain and insufficient institutional incentives. Conclusions : This study highlights the critical need to address job satisfaction, social support, and workload among CDC staff to reduce turnover intention. Strengthening workforce retention is essential for ensuring the continuity of public health programs, particularly those affecting non-communicable disease control, elderly health and infectious disease prevention. Workforce Stability as a Cornerstone of Public Health Sustainability: A Latent Profile Analysis of Turnover Intention among CDC Staff in China Xinyu Liu 1+ , Yanmin Cao 2+ , Xiaoting Wang 1 , Yuheng Xin 3, * 1 Jinan Municipal Center for Disease Control and Prevention, Jinan, China; [email protected] Jinan Blood Center, Jinan, China; [email protected] Jinan Maternity and Child Care Hospital, Jinan, China; [email protected] + These authors contributed equally to this paper.* Correspondence: [email protected] Abstract Background/Objectives : Sustainable public health systems rely on a stable and motivated workforce. However, the mental status and turnover intention of CDC staff, who play a foundational role in national health security, remain underexplored. This study examines turnover intention profiles among CDC staff in China and identifies factors affecting public health workforce sustainability. Methods : We conducted a cross-sectional survey among 1,057 staff members from 14 CDCs in a major city in Shandong Province, China. Self-administered questionnaires measured turnover intention (using a validated 4-item scale), job satisfaction, social support, and demographic variables. Latent profile analysis (LPA) was used to identify distinct turnover intention profiles, followed by multinomial logistic regression to examine associated factors. Results : Four turnover intention profiles were identified: Low Turnover Intention (29.4%), High Dissatisfaction but Low Turnover Intention (22.7%), High Boredom but Low Turnover Intention (39.0%), and High Resignation Intention (8.9%). Staff with low job satisfaction and low social support were significantly more likely to fall into high-risk profiles. Other associated factors included administrative position, employment type, age, self-rated health, and overtime. These findings highlight the risks to public health workforce sustainability posed by organizational strain and insufficient institutional incentives. Conclusions : This study highlights the critical need to address job satisfaction, social support, and workload among CDC staff to reduce turnover intention. Strengthening workforce retention is essential for ensuring the continuity of public health programs, particularly those affecting non-communicable disease control, elderly health and infectious disease prevention. Keywords: Turnover Intention; Health Worker; Center for Disease Control and Prevention; Latent Profile Analysis Highlights • Identified four distinct turnover intention profiles among Chinese CDC staff, with nearly 40% facing high work boredom and 8.9% having high resignation intention.• Low job satisfaction and low social support were the key factors for high-risk turnover profiles, revealing organizational risks to public health workforce sustainability.• Targeted interventions for satisfaction, support and workload are vital to retain CDC professionals and ensure the continuity of disease control programs.• Used LPA and multinomial logistic regression to explore turnover intention profiles and associated factors, providing empirical basis for targeted public health workforce management globally. 1. Introduction The Centers for Disease Control and Prevention (CDCs) are central to global governance, leading efforts in disease control and prevention, and consequently improving public health outcomes. In 2003, the Chinese government underwent a significant restructuring of its disease control system, establishing CDCs at both central and local government levels .[1] Since their establishment, CDCs have become the linchpin of disease prevention and control, emergency response to public health crises, and the promotion of various health initiatives. These initiatives span a wide array of areas including environmental and occupational health, nutritional health, elderly health, maternal and child health, radiation health, and school hygiene. [2] Given their multifaceted responsibilities, maintaining a stable, high-quality, and resilient human resource team within CDCs is paramount for promoting public health in China, particularly in the aftermath of the COVID-19 pandemic. The effectiveness of CDCs in addressing health challenges is inherently tied to the expertise, dedication, and stability of their human resources. [3 4] However, despite their critical role, CDCs in China operate under a non-profit administrative position, relying solely on government funds as for-profit services are strictly prohibited. Before the onset of COVID-19, China had not experienced a national pandemic for over 15 years, leading to relative budget reductions for CDCs. Additionally, evaluating the work outcomes of CDCs is inherently challenging, contributing to a lack of motivation plans for CDC staff. [5] These factors have resulted in relatively low-income levels and a high turnover rate among CDC staff. [6 7] According to the China Statistics Yearbook, the total number of CDC staff decreased from 197 thousand in 2007 to 188 thousand in 2019, representing a 4.6% decrease, while the staff in the medical sector increased from 5.9 million to 12.9 million, a 118.6% increase during the same period. [8] Since 2020, in response to the impact of COVID-19, the number of CDC staff has begun to increase slightly. However, due to the higher exposure risk and mental pressure associated with their work, the turnover rate remains high. [9] In the context of the Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) and SDG 8 (Decent Work and Economic Growth), workforce stability within public health institutions is fundamental to the sustainability of health systems. However, there is a dearth of evidence about the mental status and turnover intention of CDC staff in China, nor any evidence-based policy recommendations. Although some studies have investigated turnover intention among health workers in the medical sector, suggesting that factors such as job satisfaction [10] social support, [11 12] employment type, [13] sex, [14 15] age, [15] and income level are associated with turnover intention, [14] these findings may not directly translate to CDC staff due to the substantial differences in work content, responsibilities, and employment conditions. The unique role and working environment of CDC staff present distinct challenges that require tailored investigation. This lack of insight hampers the development of effective strategies to address turnover risk and protect workforce well-being. Therefore, understanding the current status and key factors associated with turnover intention among CDC staff is crucial to safeguarding the continuity and effectiveness of disease control and prevention functions, particularly in the post-COVID era. By identifying latent profiles of turnover intention and their associated determinants, this study seeks to inform institutional practices and policy interventions that enhance the sustainability of the public health workforce. 2. Materials and Methods 2.1. Participants The sample comprised all 1057 staff members of 14 CDCs in a major city of Shandong Province, including 2 CDCs at the municipal level, 10 CDCs at the district level, and 2 county CDCs. Staff members who were on long-term sick leave, unwilling to participate in the study, or exhibited a pattern of responding with consistent options (e.g., selecting same option repeatedly) were excluded from the study (Figure 1). The data were collected using on-paper questionnaires from July 1, 2019, to July 15, 2019. [1]¿p#1 Insert Figure 1 Here Ultimately, 44 staff members were excluded due to long-term sick leave, and 50 were excluded because they were unwilling to participate, resulting in a response rate of 91.11%. Additionally, 20 responses were excluded due to a pattern of consistent responses, leaving 97.92% of responses considered valid for analysis. The majority of participants fell within the age range of 30 to 50. Approximately 60% of participants were female, and 84% had permanent employment contracts. Detail demographic information is provided in Appendix Table 1. 2.2. Measures 2.1.1. Turnover Intention We employed the turnover intention scale developed by Farh et al., which consistsof 4 questions .[16] Each question uses a 5-point Likert scale ranging from ”stronglyagree” to ”strongly disagree.” Therefore, the scale has a total of 20 points, withhigher scores indicating greater turnover intention. This scale has been widely usedin studies within the Chinese context, demonstrating its validity. The Cronbach’salpha coefficient for this scale ranged from 0.87 to 0.92 in previous studies. [17 18] 2.1.2. Job Satisfaction The job satisfaction of CDC staff was measured using The Minnesota Satisfaction Questionnaire (MSQ) short form, validated in Chinese, which contains 20 questions. [19] The short form employs Likert ratings ranging from 1 to 5, from ”very dissatisfied” to ”very satisfied.” A higher score indicates better job satisfaction. The Chinese version of the MSQ short form has been widely utilized in various studies involving health workers, with Cronbach’s alpha coefficients ranging from 0.88 to 0.93. [20-22] 2.1.3. Social Support We employed the Social Support Rating Scale (SSRS) to measure the level of social support among participants. The SSRS consists of 10 items assessing both objective and subjective support .[23] This scale has been widely used in Chinese contexts, with Cronbach’s alpha coefficients ranging from 0.83 to 0.94. [24 25] 2.1.4. Statistical Analysis We conducted latent profile analysis (LPA) to identify different latent profiles of turnover intention. Following the approach outlined by Nylund et al., we began with a two-profile model and gradually increased the number of profiles, fitting models ranging from 2 to 5 profiles of turnover intention. [26] We selected the most suitable model based on objective evaluation indexes. Specifically, model fit was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Sample Size Adjusted BIC (SSA-BIC) .[27] Smaller values of these indexes indicate a better fit. [28] Classification accuracy was assessed using the Entropy index, with values more than 0.8 indicating high accuracy. [28] The LPA was conducted by Mplus Version 8.10 (Mplus Group, USA). After determining the best-fitting model, we compared the scores of the four items of turnover intention scale between different latent profiles using graphical comparisons. We also analyzed the sociodemographic characteristics associated with different turnover intention profiles. Distribution differences in characteristics were assessed using the chi-square test (or Fisher’s exact test) for categorical data and the Kruskal-Wallis H test for continuous data. Additionally, we conducted multinomial logistic regression analysis of latent profiles of turnover intention, using a significance level of α=0.05 for two-sided tests. The analyses were performed using Stata Version 18.0 (StataCorp LLC, USA). 3. Results 3.1. Latent Profile Analysis on Turnover Intention We conducted latent profile analysis on the four items of turnover intention to identify different latent profiles. As shown in Table 1, as we increased the number of profiles, the values of evaluation indices gradually decreased. Although the values of AIC, BIC, and SSA-BIC reached their lowest level with the 5-profile model, the proportion of the smallest profile was only 4.6%, and the clinical interpretability was poor. Additionally, the Entropy index of the 5-profile model was smaller than that of the 4-profile model. Therefore, we selected the 4-profile model as the optimal model for this study. Insert Table 1 Here Figure 2 reflects the mean values of the four profiles of turnover intention levels based on the scores of the turnover intention scale:  •Profile 1 participants showed low scores on all four questions, suggesting that they were neither dissatisfied with their work nor had a strong intention to resign. Therefore, we named Profile 1 class as the ”Low Resign Intention Group.” •Profile 2 participants showed high scores on Question 1 (boredom with work), Question 3 (willingness to look for another job), and Question 4 (willingness to accept another suitable job), and low scores on Question 2 (intention to resign). Therefore, we named Profile 2 class as the ”High Dissatisfaction but Low Resign Intention Group.” • Profile 3 participants showed high scores on Question 1 (boredom with work) and Question 2 (intention to resign), but low scores on Question 3 (willingness to look for another job) and Question 4 (willingness to accept another suitable job). Therefore, we named Profile 3 class as the ”High Boredom but Low Resign Intention Group.” •Profile 4 participants showed high scores on all four questions. Therefore, we named Profile 4 class as the ”High Resign Intention Group.” Insert Figure 2 Here The four profile classes of turnover intention showed significant differences on each question. There were 277, 214, 368, and 84 participants distributed into Profile 1 to 4 groups, accounting for 29.4%, 22.7%, 39.0%, and 8.9% of all participants, respectively. 3.2. Inter-Profile Characteristic Differences The chi-square test and the Kruskal-Wallis H test were used to compare differences among demographic factors in different turnover intention profile groups (Table 2). The results showed that most advanced degree, function, technical title, monthly income level, and annual training times were not statistically significant. However, the rest of the demographic differences were statistically significant, including CDC level, sex, age group, length of service, employment type, administrative position, weekly overtime hours, and self-reported health status. Insert Table 2 Here 3.3. Multinomial Logistic Regression of Turnover Intention We divided job satisfaction and social support into high and low groups based on their mean values. The profiles of turnover intention among CDC staff were used as the dependent variables, with Profile 1 (Low Resignation Intention Group) as the reference class. Multiple logistic regression analyses were conducted using collected control variables, including social support, job satisfaction, function, sex, etc.The results showed that CDC staff with low social support (OR=2.589, P<0.001), low job satisfaction (OR=11.229, P<0.001), function as service (OR=3.327, P=0.027), and average health status (OR=2.694, P=0.003) were more likely to belong to the High Dissatisfaction but Low Resignation Intention Group (Profile 2), compared to those in Profile 1. CDC staff who work overtime for more than 5 hours per week are more likely to be classified as High Dissatisfaction but Low Resignation Intention Group (Profile 2) (OR=0.321, P=0.001) compared to those who do not work overtime per week. CDC staff with low social support (OR=1.511, P=0.026), low job satisfaction (OR=2.870, P<0.001), and poor, average, and good health status (OR=2.703, P=0.030, OR=2.589, P<0.001, OR=2.065, P=0.004) are more likely to belong to the High Boredom but Low Resignation Intention Group (Profile 3). CDC staff who work overtime for more than 5 hours per week are also more likely to be classified as High Boredom but Low Resignation Intention Group (Profile 3) (OR=0.516, P=0.019) compared to those who do not work overtime per week. CDC staff with low social support (OR=4.619, P<0.001), low job satisfaction (OR=9.315, P<0.001), age range of 41-50 years (OR=5.588, P=0.010), professional function (OR=3.395, P=0.039), and middle-level or director-level administrative positions (OR=2.637, P=0.041, OR=8.668, P=0.002) are more likely to belong to the HighResignation Intention Group (Profile 4). CDC staff who work overtime for more than 5 hours per week are more likely to belong to the High Resignation Intention Group (Profile 4) (OR=0.323, P=0.006, OR=0.331, P=0.005) compared to those who do not work overtime and work overtime for 5 hours or less per week. CDC staff who have worked for more than 30 years are more likely to belong to the High Resignation Intention Group (Profile 4) (OR=0.119, P=0.001) compared to those who have worked for 21-30 years. Please refer to Appendix Table 2 for details. 3.4. Figures, Tables and Schemes Table 1 and Table2 Table 1. Indicators for each latent profile of turnover intention. [1]¿p#1 Profile AIC BIC SSA-BIC Portion of smallest profile P-LMR P-BLRT Entropy index 2 9298.01 9361.05 9319.76 37.3 <0.001 <0.001 0.92 3 8422.02 8509.30 8452.14 27.8 0.002 <0.001 0.94 4 6641.18 6752.71 6679.66 8.9 0.033 <0.001 1.00 5 6441.25 6577.03 6488.09 4.6 0.004 <0.001 0.99 Note: AIC, Akaike Information Criteria; BlC, Bayesian Information Criteria; SSA-BlC, sample-size-adjusted Bayesian Information Criteria; p-LMR, p value of the adjusted Lo-Mendell-Rubin Likelihood Ratio Test; p-BLRT: p value of the Bootstrapped likelihood ratio test. Variables Profile Group χ 2 p Profile 1 Profile 2 Profile 3 Profile 4 CDC level 8.754 0.033 Municipal level 120 66 131 29 District/county level 157 148 237 55 Function 9.038 0.434 Professional 191 154 267 63 Administrative 29 14 37 11 Service 18 18 22 5 Other 39 28 42 5 Sex 9.515 0.023 Female 172 128 232 38 Male 105 86 136 46 Age group 23.253 0.006 20-30 29 36 34 12 31-40 80 88 119 27 41-50 104 61 139 27 Above 50 64 29 76 18 Most Advanced Degree 5.952 0.429 Master or higher 34 37 41 12 Bachelor 210 158 291 63 High school diploma or lower 33 19 36 9 Length of service 42.180 <0.001 Less than 10 years 51 81 74 26 11-20 years 69 53 92 22 21-30 years 102 52 128 15 More than 30 years 55 28 74 21 Employment type 8.201 0.042 Permanent contract 241 168 317 69 Non-permanent contract 36 46 51 15 Technical tittle 8.381 0.496 Non 65 55 74 23 Junior 80 66 102 24 Middle 95 71 142 23 Senior 37 22 50 14 Administrative position 19.361 0.022 Non 81 57 101 21 Officer 130 100 147 28 Mid-level 52 44 98 23 Director-level 14 13 22 12 Monthly income 10.207 0.116 Less than 4,000 RMB 65 67 78 24 4,000 to 5,999 RMB 126 91 184 33 More than 6,000 RMB 86 56 106 27 Weekly overtime hours 50.029 <0.001 No overtime work 142 57 135 26 Less than 5 103 108 170 29 More than 5 32 49 63 29 Annual training times 5.656 0.774 0 31 29 43 9 1 86 58 119 29 2 58 40 83 15 More than 2 102 87 123 31 Self-reported health status /* <0.001 Very poor 5 2 4 3 Poor 10 16 24 12 Average 96 110 172 36 Nice 96 63 125 20 Very nice 70 23 43 13 Table 2. Inter-profile characteristic difference analysis results (n=943) *: By implementing Fisher’s exact test because of less than 5 in cells. Figure 1 and Figure 2 Figure 1. Flowchart of participants selection Figure 2. Latent profile model of turnover intention among CDC staff 4. Discussion To the best of our knowledge, this is the first study to investigate turnover intention and mental status specifically among CDC staff in China. While prior studies have examined similar topics among healthcare workers in clinical settings, the markedly different organizational contexts, responsibilities, and incentive structures between clinical staff and CDC personnel render the present findings both novel and policy-relevant. Based on our analysis, approximately 8.4% of participants were deemed to have a high resignation intention (Profile 4), which is lower than that reported among health workers in the medical sector, such as nurses (9.82%), [29] emergency physician (55.18%), [14] and primary care provider (26.38%). [15] Although not directly addressed in this study, we also collected human resource status data of CDC staff in this city. From 2014 to 2018, 59 CDC staff members resigned, 83 retired, and 75 new members were recruited into CDCs. Based on the available data, we observed that the total number of CDC staff in China decreased before 2019 mainly due to more staff retiring than being recruited. Two possible factors contributed to this situation: low-income levels and limited employment quotas. From 2003 to 2020, there were more than 15 years without any major pandemics in China. Consequently, the Chinese government gradually decreased the priority of CDCs, leading to limited budgets and employment quotas. Before the COVID-19 pandemic, there were discussions about merging CDCs into other civil departments to reduce the total number of civil servants required. Consequently, the budget for CDCs was limited, as was the employment quota. Since 2020, in response to the impact of the pandemic, Chinese CDCs have received significant investments in subsequent years, resulting in a gradual increase in the total staff of CDCs at the national level, from 188 thousand in 2018 to 210 thousand in 2021. [30] We believe this change also supports our previous deduction. The results of the multinomial logistic regression analysis shed light on the factors associated with different turnover intention profiles among CDC staff. Individuals with low social support and low job satisfaction were more likely to belong to the ”High Dissatisfaction but Low Resignation Intention Group” (Profile 2). This suggests that while these individuals may experience dissatisfaction with their work, they may not actively seek to leave their positions. This finding underscores the importance of fostering supportive work environments and addressing factors contributing to job dissatisfaction to mitigate turnover intention among CDC staff. Employees who reported working overtime for more than 5 hours per week were more likely to be classified into both the ”High Dissatisfaction but Low Resignation Intention Group” (Profile 2) and the ”High Boredom but Low Resignation Intention Group” (Profile 3). This indicates that excessive work hours may contribute to both dissatisfaction and boredom, which in turn affects turnover intention. Existing study about medical worker showed similar results. [29 31] Therefore, efforts to manage workloads and promote work-life balance may help reduce turnover intention among CDC staff. Thirdly, individuals with low social support and low job satisfaction were also more likely to belong to the ”High Boredom but Low Resignation Intention Group” (Profile 3). This suggests that while these individuals may experience boredom with their work, they may not actively seek to resign. Strategies aimed at enhancing social support within the workplace and addressing factors contributing to job dissatisfaction may help alleviate boredom and reduce turnover intention in this group. Lastly, employees with low social support, low job satisfaction, and certain demographic characteristics (such as age, position, and tenure) were more likely to belong to the ”High Resignation Intention Group” (Profile 4). This group represents individuals with a strong intention to resign, potentially posing a significant challenge for CDCs in retaining experienced staff. Efforts to improve social support, job satisfaction, and address specific demographic factors associated with resignation intention are crucial in mitigating turnover in this group. These findings highlight that workforce instability in CDCs is not merely an individual psychological issue, but a symptom of deeper organizational, institutional, and policy-related shortcomings. In the context of the SDGs—particularly SDG 3 (Good Health and Well-being) and SDG 8 (Decent Work and Economic Growth)—sustaining a strong and stable public health workforce is essential to the resilience and equity of health systems. Addressing turnover intention in CDCs thus has implications beyond human resources—it is central to the rational sustainability of public health institutions. It should be noted that there were several limitations in the current study. The questionnaire data of the participants were obtained through self-reporting, which may introduce recall bias and lead to inaccuracies in the collected results. While the study had a large sample size, all participants were from 10 CDCs in one major city. This limited geographical scope may affect the generalizability of the findings. Different CDCs in other regions may have different organizational cultures, policies, and work environments, which could influence turnover intention differently. Therefore, caution should be exercised when generalizing the results to CDC staff in other regions or countries. Besides, the study did not consider certain policy factors that could influence turnover intention among CDC staff. Policy changes at the organizational or governmental level, such as changes in funding, staffing quotas, or organizational restructuring, could have significant impacts on turnover intention but were not examined in this study. This study is cross-sectional in nature, which limits the ability to establish causality between the identified factors and turnover intention. 5. Conclusions This study provides new empirical evidence on the multidimensional nature of turnover intention among CDC staff in China. Job satisfaction, social support, overtime, and demographic characteristics were all found to significantly shape distinct turnover intention profiles. Importantly, even among those not actively seeking to resign, dissatisfaction and disengagement were prevalent, indicating hidden risks to organizational effectiveness. Targeted strategies—such as improving workplace support, optimizing workload distribution, and developing retention policies tailored to experienced staff—are essential to mitigate turnover. These findings have direct relevance for health system managers and policymakers seeking to enhance the sustainability and resilience of public health institutions in the post-pandemic era. Author Contributions: LX: Conceptualization, Data curation, Formal analysis, Validation, and Writing – review & editing. CY: Data curation and Formal analysis. WX: Writing – review & editing. XY: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, and Writing – review & editing. Funding: This study was supported by the Science and Technology Development Program of Jinan Municipal Health Commission (Project Title: Research on Influencing Factors of Influenza Vaccination Willingness among Primary Healthcare Workers in Jinan in the Post-Pandemic Era—Based on the ”3Cs” Model, Project No.: 2024-3-007001). Institutional Review Board Statement: Informed consent was obtained from all individual participants included in the study. This study received ethical approval from Institutional Review Board of School of Public Health, Shandong University (LL20190328). 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China Health Statistics Yearbook 2022. Beijing: Peking Union Medical College Press 2022. 31. Tang C, Zhou S, Liu C, et al. Turnover intention of nurses in public hospitals and its association with quality of working life: a cross-sectional survey in six provinces in China. Front Public Health 2023;11:1305620. doi: 10.3389/fpubh.2023.1305620 [published Online First: 20231218] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. Supplementary Material File (appendix.zip) Download 336.08 KB Information & Authors Information Version history V1 Version 1 28 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords center for disease control and prevention health worker latent profile analysis turnover intention Authors Affiliations Xinyu Liu 0000-0002-4401-5015 Jinan Municipal Center for Disease Control and Prevention View all articles by this author Yanmin Cao Jinan Blood Center View all articles by this author Xiaoting Wang Jinan Municipal Center for Disease Control and Prevention View all articles by this author Yuheng Xin [email protected] Jinan Maternity and Child Care Hospital Affiliated to Shandong First Medical University View all articles by this author Metrics & Citations Metrics Article Usage 113 views 45 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xinyu Liu, Yanmin Cao, Xiaoting Wang, et al. Workforce Stability as a Cornerstone of Public Health Sustainability: A Latent Profile Analysis of Turnover Intention among CDC Staff in China. Authorea . 28 January 2026. 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