Objective:To explore the impact of the hospital health literacy environment on patients′ postoperative pain self-management behaviors, aiming to provide insights for hospitals to implement the Comprehensive Pain Management Pilot Work Program in hospitals and to promote self-health management among patients with other diseases or symptoms. Methods:From November to December 2023, a convenience sampling method was used to select postoperative patients from three grade A tertiary general hospitals in Zhejiang Province for an on-site questionnaire survey. The Chinese version of brief health literacy screen (BHLS), short-form health literacy environment scale (SF-HLES) and postoperative pain self-management behavior questionnaire (PPSMB) were used as survey tools to investigate the health literacy level of patients, the health literacy environment of the hospital, and the postoperative pain management behaviors of patients. Two-way ANOVA was used to compare the impact of different dimensions of the hospital health literacy environment on postoperative pain management behaviors among patients with different levels of health literacy. Multiple linear regression analysis was used to explore the relationship between the hospital health literacy environment, individual health literacy, and patients′ postoperative pain self-management behaviors, and to discuss the impact of individual health literacy on patients′ postoperative pain self-management behaviors under different hospital health literacy environments.Results:341 valid questionnaires were collected. The average score of the hospitals′ SF-HLES was (73.62±19.54) points. The average score of the patients′ BHLS was (9.65±2.88) points. The average score of the patients′ PPSMB was (25.99±6.35) points. Two-way ANOVA results showed that the interaction between individual health literacy and the clinical dimension ( F=5.463, P=0.020) and structural dimension ( F=6.470, P=0.011) of the hospital health literacy environment had a statistically significant impact on patients′ postoperative pain self-management behaviors, while the interaction with the interpersonal dimension ( F=0, P=0.984) had no statistically significant impact on pain self-management behaviors. Simple effect analysis indicated that only in the high health literacy environment of the clinical and structural dimensions did the difference in pain self-management behaviors between patients with good health literacy and those with limited health literacy had statistical significance ( P<0.001). Multiple linear regression analysis results showed that for each 1-point increase in the patients′ BHLS score, their PPSMB score increased by 3.74 points ( β1=0.832, P<0.001); for each 1-point increase in the hospital′s SF-HLES score, the patients′ PPSMB score could increase by 0.198 points ( β2=0.610, P<0.001). In a low health literacy environment, individual health literacy did not affect pain self-management behaviors ( P>0.05); however, in a high health literacy environment, for each 1-point increase in the patients′ BHLS score, their PPSMB score correspondingly increased by 4.037 points ( β4=0.317, P<0.001). Conclusions:The positive impact of individual health literacy on pain self-management is contingent upon a high-quality hospital health literacy environment. This suggests that optimizing the hospital health literacy environment is a necessary precondition for implementing the relevant content of the Comprehensive Pain Management Pilot Work Program and can provide a reference for promote self-health management among patients with pain and other diseases or symptoms.

Objective To construct a diagnosis models for differential diagnosis of bloodstream infection(BSI)using nomogram model,random forest model,and decision tree model,respectively.Methods A retrospective analysis was performed on 225 BSI patients diagnosed and treated in Pujiang County People's Hospital from January 2022 to January 2024,and the patients were divided into a training set and a validation set according to a ratio of 7:3.The differential diagnostic models for Gram negative BSI(GN-BSI)and Gram positive BSI(GP-BSI)were established by nomogram model,random forest model,and decision tree model,and the differential diagnostic efficacy of different models were analyzed.Results Binary Logistic regression analysis showed that neutrophil to lymphocyte ratio(NLR),C-reactive protein(CRP),interleukin-6(IL-6),red cell volume distribution width to platelet ratio(RPR),procalcitonin(PCT)were diagnostic variable between GN-BSI and GP-BSI(P＜0.05).In training set,area under the curve(AUC)of nomogram model,random forest model and decision tree model to identify GN-BSI and GP-BSI were 0.900,0.911 and 0.884,respectively,and AUC of random forest model was significantly higher than that of decision tree model(Z=3.521,P=0.038).In verification set,AUC of nomogram model,random forest model and decision tree model for identifying GN-BSI and GP-BSI were 0.908,0.916 and 0.893,respectively,and AUC of random forest model was significantly higher than that of decision tree model(Z=3.412,P=0.042).Conclusion The three models have good identification value for GN-BSI and GP-BSI,among which the random forest model and nomogram model have better identification performance.