Background: A valid and efficient stress measure is important for clinical and community settings. The objectives of this study were to translate the English version of the Perceived Stress Questionnaire (PSQ) into Chinese and to assess the psychometric properties of the Chinese version of the PSQ (C-PSQ). The C-PSQ evaluates subjective experiences of stress instead of a specific and objective status. Methods: Forward translations and back translations were used to translate the PSQ into Chinese. We used the C-PSQ to survey 2798 medical students and workers at three study sites in China from 2015 to 2017. Applying Rasch analysis (RA) and factor analysis (FA), we examined the measurement properties of the C-PSQ. Data were analyzed using the Rasch model for item fit, local dependence (LD), differential item functioning (DIF), unidimensionality, separation and reliability, response forms and person-item map. We first optimized the item selection in the Chinese version to maximize its psychometric quality. Second, we used cross-validation, by exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), to determine the best fitting model in comparison to the different variants. Measurement invariance (MI) was tested using multi-group CFA across subgroups (medical students vs. medical workers). We evaluated validity of the C-PSQ using the criterion instruments, such as the Chinese version of the Perceived Stress Scale (PSS-10), the Short Form-8 Health Survey (SF-8) and the Goldberg Anxiety and Depression Scale (GADS). Reliability was assessed using internal consistency (Cronbach’s alpha, Guttman’s lambda-2, and McDonald’s omegas) and reproducibility (test–retest correlation and intraclass correlation coefficient, [ICC]). Results: Infit and/or outfit values indicated that all items fitted the Rasch model. Three item pairs presented local dependency (residual correlations > 0.30). Ten items showed DIF. Dimensionality instruction suggested that eight items should be deleted. One item showed low discrimination. Thirteen items from the original PSQ were retained in the C-PSQ adaptation (i.e. C-PSQ-13). We tested and verified four feasible models to perform EFA. Built on the EFA models, the optimal CFA model included two first-order factors (i.e. constraint and imbalance) and a second-order factor (i.e., perceived stress). The first-order model had acceptable goodness of fit (Normed Chi-square = 8.489, TLI = 0.957, CFI = 0.965, WRMR = 1.637, RMSEA [90% CI] = 0.078 [0.072, 0.084]). The second-order model showed identical model fit. Person separation index (PSI) and person reliability (PR) were 2.42 and 0.85, respectively. Response forms were adequate, item difficulty matched respondents’ ability levels, and unidimensionality was found in the two factors. Multi-group CFA showed validity of the optimal model. Concurrent validity of the C-PSQ-13 was 0.777, − 0.595 and 0.584 (Spearman correlation, P < 0.001, the same hereinafter) for the Chinese version of the PSS-10, SF-8, and GADS. For reliability analyses, internal consistency of the C-PSQ-13 was 0.878 (Cronbach’s alpha), 0.880 (Guttman’s lambda-2), and 0.880 (McDonald’s omegas); test–retest correlation and ICC were 0.782 and 0.805 in a 2-day interval, respectively. Conclusion: The C-PSQ-13 shows good metric characteristics for most indicators, which could contribute to stress research given its validity and economy. This study also contributes to the evidence based regarding between-group factorial structure analysis.

Purpose: This study aimed to evaluate outpatient satisfaction in tertiary hospitals in Shiyan, China, to predict which items had highest priorities for outpatient satisfaction, and to identify population groups on which the medical institutions should focus. Patients and Methods: A cross-sectional survey was conducted at three tertiary hospitals in Shiyan city of China, from March to June 2018. An 18-item outpatient satisfaction questionnaire was applied. We conducted matrix analysis to describe the distribution of satisfaction score and the degree of influence of the items. Outpatient satisfaction was classified into the lowest and highest groups according to the 80/20 rule. Logistic regression model was used to identify demographic factors which might influence outpatient satisfaction. Results: A total of 2109 valid questionnaires were completed. The “waiting time”, “diagnosis and treatment time” and “medical charges” items showed relatively higher degrees of influence but earned lower levels of satisfaction. Outpatients with a college level or above educational background (AOR=1.36, 95% CI=1.03– 1.79) and with a family per-capita monthly income (FPMI)> 7000 CNY (AOR=3.30, 95% CI=1.60– 6.81) were more prevalent in the lowest satisfaction group. Outpatients with college level or above education background (COR=0.77, 95% CI=0.60– 0.99), FPMI of 3001– 5000 CNY (AOR=0.76, 95% CI=0.60– 0.96), non-local residents (AOR=1.48, 95% CI=1.07– 2.04), and urban workers with medical insurance (AOR=1.74, 95% CI=1.27– 2.39) were more prevalent in the highest satisfaction group. Conclusion: The survey indicated that “long time to wait for treatment”, “short treatment time”, and “medical charges too expensive” were the top three aspects that need to be improved with priority by medical institutions. Education level, income level, residence and type of health insurance were the sociodemographic characteristics that significantly affect the outpatient satisfaction in tertiary hospitals. These factors need to be paid more attention by healthcare professionals to improve the patients’ satisfaction.

Background: Conflicts between doctors and patients occur in People’s Republic of China each year. Serious negative relationships between doctors and patients are a significant social problem. This study aims to evaluate the satisfaction of inpatients in a tertiary hospital through a third party and analyze the relationship between sociodemographic characteristics and overall satisfaction. Patients and methods: A cross-sectional investigation was conducted to obtain the demographic information and results for 20 items in an inpatient satisfaction questionnaire. A total of 872 inpatients were studied. Descriptive analysis was conducted using SPSS version 22.0 software. Independent sample t-tests and one-way ANOVA were used to compare the mean differences between groups. Multiple linear regression analysis was conducted to study the relationship between different demographic characteristics and overall satisfaction. P-values of <0.05 were considered significant. Matrix analysis was used to predict which of the 20 items had a higher priority for patient satisfaction. Results: Among the respondents, valid questionnaires were completed by 872 patients, with a response rate of 94.47% (872/923). The hospital inpatient satisfaction score was 84.92±10.34. Among the sociodemographic characteristics, education status, occupation, annual income, residence and frequency of visits were related to inpatient satisfaction and were statistically significant (P0.05). Multiple linear regression analysis results showed that residence, age, occupation and education status were the factors affecting inpatient satisfaction. In further analyses, diet management, medical expenses, complaint management and hospital environment were in urgent need of improvement. Conclusion: The overall level of inpatient satisfaction with this hospital seems to be acceptable, although areas for improvement remain.