Background The psychological health (PH) of doctors affects the quality of medical service and is related to the safety of patients. The serious problems with the doctor-patient relationship in China can lead to long-term imbalances in doctor PH, and the poor PH status of doctors has raised scholars' concern. Current research mainly focuses on how factors such as social support and the impact of the residential environment correlate with individual PH. We continue this direction of research to see how the mechanism of social support impacts physician PH, also investigating the moderating effect of demographic indicators on physician PH. Methods Based on a survey of 399 physicians, a descriptive analysis of measured data was done using SPSS 19.0. Pearson correlation coefficient analysis was used to examine the correlations between PH and the social support rating scale (SSRS) and the demographic variables. KMO and Bartlett methods were used to examine the correlations between PH and SDS (a scale to measure depression) and between PH and SAS (a scale to measure anxiety). The method of factor analysis was used for multicollinearity tests, and multiple stepwise regression analysis was used to explore the demographic factors correlated with PH and SSRS. Two-way interactions in moderated multiple regression were used to test the moderating effect of education level and title on SSRS, SDS, and SAS. Results Our results indicate that the level of PH is influenced by the age, education, and title of a doctor. A physician's title is significantly and positively correlated with PH, but age and

BackgroundThe tension in doctor-patient relationships is becoming progressively greater due to the high expectations of patients and the physicians’ work pressure. Recent studies have addressed factors which affect the tension of doctor-patient relationships, and our study continues this trend by looking at the influence of resiliency and physician trust in the patient (PTP), that is, how much the doctor trusts the patient.MethodsBased on a survey of 329 physicians, a descriptive analysis of measured data was done using SPSS 19.0. Pearson correlation coefficient analysis was used to examine the correlation between PTP and resilience and the demographic variables. KMO and Bartlett methods were used to examine the correlation between PTPS and resilience. The method of factor analysis was used for multicollinearity tests, and multiple stepwise regression analysis was used to explore the demographic factors correlated with PTP and resilience.ResultOur results indicate that the level of PTP is influenced by the age, education, and income of the doctors. Physician age and income are significantly and positively correlated with PTP, but education is significantly and negatively related. Age, education, and income also affect the level of psychological resilience of physicians. Resilience is positively correlated with age and education but is negatively related to income. Resilience positively influences PTP.ConclusionThe direct factors of PTP include resilience, age, education, and income, while gender, title, and hospital department were found to be indirect influencing factors. To meet goals expressed in Chinese government policy related to these issues, we suggest improving the level of education of the doctors, providing reasonable annual salary increases for doctors, easing the tensions involved in medical treatment, reducing the physicians’ work pressure, improving the physicians’ work environment, and enhancing the physicians’ professional sympathy. Through such measures, the level of PTP will be enhanced.

Following the integration of the urban residents’ medical insurance into the new rural cooperative medical insurance in 2016, China has now formed a basic medical insurance system with the urban workers’ basic medical insurance system and the rural residents’ basic medical insurance system as the main entities. With the development of basic medical insurance, the protection for residents is becoming more and more comprehensive, and its fund expenditure also increases, so it is necessary to research the efficiency of the medical insurance fund expenditure. This paper conducts a three-stage DEA analysis of the efficiency of basic health insurance for urban and rural residents in 31 provinces, based on a Chinese panel data from 2017 to 2020. It is found that China’s health insurance operation is still in the development stage, with four regions in the efficiency frontier and Guizhou province having the lowest efficiency value nationwide. The GDP and fiscal investment on social security effectively reduce the input redundancy in the basic health insurance operation, which contributes to the efficiency of the health insurance operation. This study further proposes suggestions and countermeasures to improve the operational efficiency of China’s basic health insurance, based on the empirical results: (1) develop the economy and broaden the financing sources; (2) improve the level of health care services and improve the efficiency driven by quality; and (3) improve the level of health insurance supervision through multiple measures.

This study takes single task pricing as an example, analyzes the key factors of task pricing and constructs an index model of single task pricing through the data of 835 task cases. Through the empirical analysis of the new pricing model, we draw the conclusion: the new pricing model reduces the cost of the task and increases the completion rate. It has a certain guiding significance for the development and management of the public crowdsourcing platforms.

Background. To develop an individual’s physical subhealth risk perception scale and evaluate its reliability and validity, so as to provide a measurement tool for individual physical health risk. Methods. A questionnaire on the perception risk of physical subhealth was developed. Using a random sampling method, 785 people in the Anhui provincial physical examination centre were selected as the research participants. Of the questionnaires returned, 770 were valid, giving an effective rate of 98%. Firstly, the Pearson correlation coefficient method was used to study the correlation of 35 items in the initial scale, and then, polychoric factor structure analysis was carried out by using the Pratt D matrix to optimize the item structure. The Cronbach’α coefficient method was used to test the internal consistency reliability, and a structural equation model was used to explore the construct validity of the scale. The discriminant validity of the scale was obtained by factor analysis. A general linear model was used to analyse the relationship between the clinical manifestations of physical subhealth and the level of risk perception, and the convergent validity of the scale was evaluated. Results. All the data of 35 items were significantly correlated at the 0.01 level. The correlation coefficients between a1 and a2, a3 and a4, b1 and b2, b2 and b3, c4 and c5, c5 and c6, c6 and c7, c8 and c9, d1 and d2, d2 and d3, e5 and e6, g1 and g2, g2 and g3, and g2 and g4 were greater than 0.6. The items with correlation coefficients greater than 0.6 were reduced by a Pratt D matrix. The resulting physical subhealth risk perception scale covers five factors with a total of 18 items. The Cronbach’α coefficient of the scale was 0.889, and the Cronbach’α coefficients of the five factors F1-F5 were 0.780, 0.825, 0.801, 0.736, and 0.704, respectively. Structural equation model analysis showed that χ 2 / df = 3.43 , p < 0.001 , RMSEA = 0.08 , GFI = 0.88 , NFI = 0.84 , AGFI = 0.84 , and CFI = 0.88 . Factor analysis showed that factors F1–F5 had significant correlations ( p < 0.01 ), and the correlation coefficients were less than the corresponding square root value of AVE. Based on the subhealth clinical manifestations of the participants, the general linear model was used to explore the convergent validity of the scale, and the results indicated that the scale passed the convergent validity test. Conclusions. We propose a physical subhealth risk perception scale amounting to 18 items, which includes five dimensions: health knowledge (2 items), risk perception (5 items), trust selection (4 items), information channel (4 items), and social groups (3 items). The reliability and validity of the physical subhealth risk perception scale are acceptable. Applying the scale into practice has potential to improve the overall public health level.