Patient satisfaction is associated with both individual (patients and health workers) and organizational (health facilities) characteristics. This study aimed to establish a link between patient satisfaction and gender composition of physicians in community health service (CHS) organizations. Participants were selected through multistage stratified random sampling: 36 CHS centers were selected from six municipalities in Hubei, China. All physicians on duty and patients visiting the CHS during the study period (from April to October in 2015) were invited to participate in this study: 324 physicians and 865 patients completed a questionnaire survey. Multilevel linear regression analyses were performed to determine the associations of patient satisfaction (scored from 1 to 5) with patient characteristics (gender, age, education, income, medical expense, frequency of visits to the CHS) and organizational features of the CHS (sex ratio of physicians, and gender differences of physicians in education and job satisfaction). Older patients and those with a higher medical bill had a lower degree of satisfaction (p < 0.05). At the organizational level: a higher proportion of male physicians weakened the negative association between patient age and patient satisfaction (p < 0.05); a larger gap in university qualifications between male and female physicians exacerbated the negative association between patient age and patient satisfaction (p < 0.05). The gender composition of physicians in CHSs is associated with patient satisfaction in the Chinese context: a larger gap (in number and qualifications) between male and female physicians is associated with higher patient satisfaction. Further studies are needed to explore the cultural roots of such an association.

At present, unequal allocation of medical resources represents a major problem for medical service management in China and many other countries. Equity of intensive care unit (ICU) bed allocation is essential for timely and equitable access to medical care for critically ill patients. This study analysed the equity of ICU bed allocation in 31 provincial regions in China, and the associated factors, to provide a theoretical basis for improvement in the allocation of ICU beds. The equity of ICU bed allocation was investigated in 31 provincial regions in China in 2021. The Gini coefficient combined with Lorenz curves were used to analyse the current status of ICU bed allocation by both population and service area. The spatial heterogeneity and aggregation of ICU bed density were analysed using the Global Moran’s index. The spatial distribution pattern was visualized via LISA maps using the Local Moran’s index. Three grey correlation models were constructed to assess the key factors influencing ICU bed density. Finally, robustness analysis was performed to test the reliability of the results. The allocation of ICU beds in China was highly inequitable by service area (Gini = 0.68) and showed better balance by population distribution (Gini = 0.14). The distribution of ICU beds by service area was highly spatially clustered (Global Moran’s I = 0.22). The bed utilization rate exhibited the strongest association with ICU bed density by population. Registered nurses per 10,000 square kilometres was the strongest factor affecting ICU bed density by service area. The allocation of ICU beds by population is better than by service area; the allocation by service area is less equitable in China. These findings emphasise the need to implement better measures to reduce ICU bed equity differences between regions and balance and coordinate medical resources. Service area size, bed utilization, the number of registered nurses and other key factors should be considered when performing regional health planning for ICU bed supply. This will increase the equitable access to critical medical services for all populations.

Objective: The regional inequality of emergency medicine beds distribution has a great impact on population health as well as the accessibility of emergency services. This study aimed to explore the regional inequality of emergency medicine bed distribution and its influencing factors.Methods: The Gini coefficient and health resource agglomeration were used to analyze the regional inequality of emergency medicine beds distribution by area from 2012 to 2021 in China. Grey correlation models were used to explore the factors influencing the regional inequality of emergency medicine beds distribution.Results: From 2012 to 2021, Gini coefficients of emergency medicine beds distribution by geographic in China showed a worsening trend, rising from 0.6229 to 0.6636. The average HRAD index was 3.43 in the east and 0.44 in the west. Population structure factors have the greatest influence on the regional inequality of emergency medicine beds distribution.Conclusion: Health resources allocation strategy only according to population size should be changed. In formulating policies for emergency medicine beds allocation should take into account population structure, financial structure of expenditure, the inequality of geographical distribution and so on.

This study investigates and interprets the role of growth organizational culture (GOC), person–organization fit (POF) and job satisfaction (JS) in predicting hospital performance (HP). This research adopted a quantitative methodology using data collected from 513 respondents in three cities across China. Smart-PLS 3 was used to evaluate the measurement model and structural model. Growth organizational culture has a significant positive impact on hospital performance (β = 0.191, p < 0.001). Person–organization fit (54.74%) and job satisfaction (29.26%) have partial mediating effects between growth culture and hospital performance. The research revealed that the GOC, POF, and JS play a substantial role in promoting HP. All the direct relationships were positive and significant. The findings suggest that establishing a growth culture environment for physicians is an effective strategy to improve physicians’ job satisfaction and person–organization fit. This strategy provides a new path to improve the hospitals performance through promoting organizational culture. Future studies should test the findings in an interventional design.

Aim: The aim of this study was to explore the distribution and driving factors influencing the disparity of health professionals (HPs) at the Centers for Disease Control and Prevention (CDC) in China and to provide a reference for regional health planning and rational allocation of public health resources. Methods: The Gini coefficient was used to measure the equity of HP distribution at CDC sites at the provincial level during 2012–2023 in China. Moran’s I was used to analyze the spatial agglomeration effect, and the geographic detector model was used to explore the factors driving the allocation of HPs at CDC sites in different provinces. Results: The number of HPs at the CDC showed an increasing trend from 2012 to 2023 in China. The average Gini coefficients at the population and geographical areas were 0.16 and 0.58, respectively. The global Moran’s I statistic indicated a notable decline in spatial clustering for the population dimension, decreasing from 0.503 to 0.238; in contrast, spatial clustering for the geographical dimension remained relatively stable, ranging between 0.13 and 0.16. The local Moran’s I statistic revealed that provinces such as Qinghai in the western China consistently exhibited a “low–low” spatial clustering pattern. Six factors were found to explain the variability in the CDC HP distribution based on the 2020 data. In the context of factor interactions, the synergistic effects between education level and health expenditure share (q = 0.9781), and between population aging and per capita GDP (q = 0.9699), substantially exceed the explanatory power attributable to any single factor alone. Conclusions: A significant regional disparity was observed in the distribution of HPs among 31 provinces, with the distribution based on service area being less equitable than that based on population. The shortage of healthcare professionals in the western region is characterized by notably inadequate geographical distribution. Future policy initiatives should prioritize targeted spatial interventions and integrated, multi-factor collaborative strategies.

Background: Greater medical staff allocation and higher Case-Mix Index (CMI) values are both associated with increased patient medical expenditures. However, it remains unclear whether CMI moderates the relationship between medical staff allocation and medical expenditures. The present study focus on whether medical staff allocation predicts the cost per inpatient visit, whether this association is enhanced under conditions of high CMI. Aim: To explore the mechanism underlying the role of the CMI in the relationship between medical staff allocation and the average cost per inpatient visit. Methods: Data were collected from 207 general hospitals in Hubei Province in 2019 using the cluster sampling method. Pearson’s correlations were used to examine the associations between medical staff allocation, the CMI and the cost per inpatient visit. The moderating role of the CMI between medical staff allocation and the average cost per inpatient visit was analyzed using Model 1 of the PROCESS macro. Results: The number of patients per doctor ( r = −0.180, p < 0.01), beds per nurse ( r = −0.181, p < 0.01), beds per doctor ( r = −0.225, p < 0.01) and the average cost per inpatient visit showed a significant negative correlation. The CMI positively moderated the relationship between the average cost per inpatient visit and both the number of beds per doctor ( p < 0.01) and the number of beds per nurse ( p < 0.01). The moderating effect was stronger when hospitals had high CMI value influencing average cost per inpatient visit. Conclusion: CMI value can enhance the negative effects of the number of beds per doctor or nurse on average cost per inpatient visit, revealing that rational CMI benchmark for the hospital or prioritizing disease categories with clinically appropriate treatment complexity can mitigate the extent to which healthcare workforce allocation influences rising medical expenditures. Reasonable medical staff allocation and the CMI serve as important factors in controlling the average cost per inpatient visit.