Pinghua Zhu

Background: This study aims to examine occupational burnout among mental health workers at the municipal, county, and township levels in Guangxi in 2024, as well as its influencing factors, from the perspective of ensuring workforce stability, thereby providing a theoretical basis for the development of targeted intervention measures. Methods: This study employed cluster sampling to conduct a cross-sectional survey of 1,520 mental health workers from different regions of Guangxi, China. The Chinese version of the Maslach Burnout Inventory-General Scale (MBI-GS) was used to assess burnout levels. To ensure the stability of the mental health workforce, we conducted univariate and binary logistic regression analyses using SPSS 27.0 (IBM Corp., Armonk, New York, USA) software across three dimensions: demographic information, institutional level, and work recommendations. Additionally, we utilized ArcMap 10.2 (Esri (Environmental Systems Research Institute, Inc.), Redlands, California, USA.) software to visualize occupational burnout levels across cities in Guangxi using hierarchical maps, thereby identifying the key factors influencing occupational burnout among mental health workers in the region. Results: The prevalence of occupational burnout among mental health workers in Guangxi was 65.46%. Workers in county-level (OR = 2.977, 95% CI: 1.480–5.988) and township-level (OR = 2.312, 95% CI: 1.210–4.416) mental health institutions were more likely to experience occupational burnout. In contrast, mental health workers in municipal institutions exhibited a lower risk of occupational burnout. Based on the recommendations made by mental health professionals, those who suggested “reducing the workload” (OR = 3.449, 95% CI: 2.142–5.554), increasing staff (OR = 2.714, 95% CI: 1.527–4.822), and lowering the difficulty of tasks (OR = 2.745, 95% CI: 1.406–5.359) showed a significantly increased risk of occupational burnout. Among the 14 cities in Guangxi, mental health workers in Laibin, Yulin, Hezhou, and Nanning exhibited higher burnout level than those in other cities. Conclusion: The prevalence of occupational burnout among mental health workers in Guangxi was relatively high. To ensure the stability of this workforce and reduce staff turnover, the competent authorities overseeing mental health services should prioritize monitoring burnout levels among workers at the county and township levels, and in key urban areas. Additionally, measures such as reducing workloads, increasing staffing, and simplifying tasks can help alleviate and symptoms of occupational burnout.

Background: Global population aging is accelerating, with China among the countries experiencing the fastest and largest aging populations. This study aims to analyze the current health management(HMN) needs of rural elderly in underdeveloped regions of China. Machine learning algorithms were employed to construct predictive models and identify key influencing factors, providing empirical evidence for the development of targeted health management strategies. Methods: From August 2023 to January 2024, a convenience sample of 641 rural community elderly aged ≥ 60 years across four prefecture-level cities in Guangxi was surveyed using questionnaires. Predictor variables were chosen using LASSO regression and Logistic regression. The predictive efficacy of three machine learning models - Logistic regression, Random Forest, and XGBoost - was methodically evaluated. Variable contributions were assessed using SHAP values, and model validity and practical applicability were validated through the nomogram, ROC curve,calibration curves, and decision curve analysis. Results: Health management needs among rural elderly in China’s underdeveloped regions were relatively low (43.84%). SHAP interpretability analysis identified five key factors influencing health management needs. By conducting SHAP explainability analysis, five crucial factors affecting the healthcare requirements of the elderly were pinpointed. XGB exhibited superior predictive accuracy in both the training and validation datasets, achieving AUC values of 0.783 and 0.723, respectively.Independent samples t-tests revealed six critical individual factors affecting health management needs. The study identified three primary health management needs: regular health monitoring and screening, enhanced health management education, and targeted chronic disease management services. Conclusion: The XGB prediction model constructed in this study effectively identifies health management needs among rural elderly in underdeveloped regions of China. It is recommended that relevant authorities optimize the allocation of rural community health service resources and implement targeted health interventions based on the high-demand population characteristics identified by the model to promote active aging.

Occupational burnout is very common among hospital workers; however, previous research has primarily focused on doctors and nurses, and research on medical

Objective: Performance evaluation is critical for improving healthcare quality and service delivery. This study analyzes the healthcare quality of tertiary hospitals across various cities in Guangxi under China’s public hospital performance assessment policy to identify influencing factors and propose targeted improvement strategies for the national evaluation system. Methods: The healthcare quality of general hospitals in Guangxi from 2019 to 2022 was evaluated using a fuzzy set entropy-weighted TOPSIS and RSR method, followed by a comprehensive city-level ranking. Results: Entropy-weighted TOPSIS revealed the top three weighted indicators: (1) number of referred-out patients, (2) low-risk group mortality rate, and (3) proportion of reviewed prescriptions. The quality of H7 and H11 improved significantly, while H9 consistently ranked in the top 4. The RSR stratification classified H1, H2, H8, and H9 as high-performing, H6 and H12 as low-performing, and H4, H5, H6, H12, and H13 as persistently below average for four consecutive years. Using the fuzzy set method, H1, H9, H11, and H1 were ranked as the highest-performing cities from 2019 to 2022, respectively. Conclusion: There are minor discrepancies among the three methodologies, but the trends remain largely consistent. The fuzzy-combined approach provides more robust evaluations, which offers actionable insights for healthcare quality enhancement and management standardization. Consequently, hospitals should improve the quality of services and sustain the core competitiveness of public hospitals by implementing tiered healthcare systems and standardized prescription review protocols.

Background: Nasopharyngeal carcinoma (NPC) is a malignant epithelial tumor most commonly in China. In 2013, NPC incidence and mortality in China were also at high levels worldwide, which poses a great health burden in China. This study analyzes the medical expenditure and influencing factors of inpatients with NPC and aims to provide reference suggestions for reducing medical expenditure for NPC.Methods: Based on the data from one of the western China hospitals, we use the random forest model to identify the important factors in total medical expenditure, out-of-pocket costs, and the rates of out-of-pocket costs.Results: Total medical expenditure, out-of-pocket costs, and the rates of out-of-pocket costs were decreased. According to the indicators of InNodePurti and %IncMSE, the top three influencing factors of total medical expenditure and out-of-pocket costs are Length of stay, Medical payment method, and Readmission status; the top three influencing factors of the rates of out-of-pocket costs are Medical payment method, Occupation, and Age.Conclusion: Length of stay was an important factor in total medical expenditure and out-of-pocket costs, and the medical payment method was an important factor in the rates of out-of-pocket costs. To reduce the burden of NPC patients’ medical expenditure, (1) we can reduce the length of stay by improving the level of medical technology; (2) increasing government medical expenditures and improving the level of medical security; (3)promoting smoking bans, and strengthening screening in high-risk areas.

Compensation plays a critical role in motivating staff and enhancing operational performance and human resource costs in hospitals. This study was aimed at investigating pay levels and the key factors influencing pay satisfaction in secondary and tertiary public hospitals in Guangxi. Questionnaires were distributed to 48 hospitals across 14 prefecture-level cities in Guangxi. Information on personal characteristics, salary levels, work situations and perceptions of current salary conditions was provided by 10,343 staff in secondary and tertiary hospitals. Five machine learning models were employed to identify the most significant influencing factors of salary satisfaction in Guangxi public hospitals. Overall, the actual total after-tax income in secondary public hospitals in Guangxi ranged from $466.55-$744, while the income of staff in municipal-level tertiary public hospitals ranged from $5,001 to $1,041.75 per month. Among the five models, the support vector machine (SVM) demonstrated the best performance in analyzing the influencing factors of compensation satisfaction. The most influential factors for total compensation satisfaction included the extent to which compensation reflected labor value, salary increases since 2017 compared to peer hospitals, total after-tax income and the difference in compensation between staff within and outside the establishment of hospitals. Satisfaction with salary growth aligned closely with the factors influencing overall compensation satisfaction. Satisfaction with pay equity was also influenced by the ability of salary gaps between different positions to reflect differential effort. A relatively low pay level in secondary hospitals in Guangxi was revealed. The factors influencing satisfaction with total pay, pay fairness and pay growth since 2017 varied. SVM outperformed other models in the analysis of the factors influencing pay satisfaction. To enhance pay satisfaction in secondary and tertiary hospitals in Guangxi, it is crucial to establish a salary distribution system aligned with the value of labor across different positions and tailored to the unique characteristics of each hospital.

Objective: The continuous increase in health care costs and the growing demand for health services among residents make it an urgent priority to improve the operational efficiency of the health system in Chinese society. In this study, data on the operational efficiency of tertiary public hospitals in Guangxi were analyzed to identify issues in hospital management within the context of performance assessment, which thereby enhanced the social service capacity of hospitals.Methods: A comprehensive evaluation index system was constructed based on the “national monitoring indicators” of operational efficiency. The indicators were analyzed using data envelopment analysis with Banker, Charnes and Cooper (DEA-BCC) and Malmquist index models. The Tobit regression model was used to analyze the major factors affecting the efficiency of public hospitals.Results: Between 2019 and 2022, the pure technical efficiency (PTE) of 61 tertiary public hospitals in Guangxi remained at a relatively high level. The results of the Malmquist index showed a downward trend. The technical progress (TC) indicator became the main factor affecting the decline in the operational efficiency of hospitals. Tobit regression analysis revealed that plenty of factors exerted a significant impact on the operating efficiency of hospitals. These factors included the number of beds, the ratio of outpatient and inpatient patients relative to total patient numbers, the proportion of discharged patients undergoing surgery among total patients, business expenditures and total annual revenue.Conclusion: The scale of tertiary public hospitals in Guangxi is prominently unreasonable. It is necessary to raise the efficiency of resource utilization. The operation and management situation is not optimistic. Hospitals should accelerate the transformation of their development model, rationally allocate medical resources and shift from scale expansion to the improvement of quality and efficiency. Meanwhile, they should actively participate in establishing the hierarchical medical treatment system, controlling operating costs and reasonably increasing the proportion of personnel expenses to improve operational and management efficiency.