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Aims. Building on conservation of resources theory, our study investigates how inclusive human resource management (IHRM) promotes nurses' innovative behavior through job crafting and further examines the moderating role of shared leadership. Background. Nurses' involvement in innovation is essential to improve nursing care delivery and accommodate changing medical environments, especially in the face of crisis events like the COVID-19 outbreak. However, knowledge about the relationship between human resource management and nurses' innovative behavior remains scarce. Methods. We collected three-wave data from 338 on-duty registered nurses at four public hospitals in China from November 2022 to January 2023. We used SPSS 22 to conduct hierarchical regressions to test our hypotheses. Results. IHRM positively predicted innovative behavior of nurses with the mediating role of job crafting. In addition, we found that IHRM was more effective in promoting job crafting and subsequent innovative behavior when nurses perceived high levels of shared leadership. Conclusion. IHRM initiated by the organization and shared leadership style are two collaborative approaches to facilitating nurses' job crafting, thereby responding to the imperative need to foster nurses' innovative behavior. Implications for Nursing Management. The present study emphasizes the important roles of IHRM and shared leadership in promoting nurses' job crafting and subsequent innovative behavior, providing theoretical and practical implications for nursing management in the current dynamic and challenging environment. [ABSTRACT FROM AUTHOR]

Objectives: To explore the feasibility of pretreatment nonenhanced magnetic resonance imaging (MRI) in predicting insufficient biochemical response to ursodeoxycholic acid (UDCA) in patients with primary biliary cholangitis (PBC). Methods: From January 2009 to April 2022, consecutive PBC patients who were treated with UDCA and underwent nonenhanced MRI within 30 days before treatment were retrospectively enrolled. All MR images were independently evaluated by two blinded radiologists. Uni- and multivariable logistic regression analyses were performed to develop a predictive model for 12-month insufficient biochemical response. Model performances were evaluated by computing the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results: A total of 74 patients (50.6 ± 11.9 years; 62 females) were included. Three pretreatment MRI features, including hepatomegaly (odds ratio [OR]: 4.580; p = 0.011), periportal hyperintensity on T2-weighted imaging (T2WI) (OR: 4.795, p = 0.008), and narrowing of the bile ducts (OR: 3.491; p = 0.027) were associated with 12-month insufficient biochemical response in the multivariable analysis. A predictive model based on the above indicators had an AUC of 0.781, sensitivity of 85.4%, and specificity of 61.5% for predicting insufficient biochemical response. Conclusions: A noninvasive model based on three pretreatment MRI features could accurately predict 12-month insufficient biochemical response to UDCA in patients with PBC. Early identification of PBC patients at increased risk for insufficient response can facilitate the timely initiation of additional treatment. Clinical relevance statement: A noninvasive predictive model constructed by incorporating three pretreatment MRI features may help identify patients with primary biliary cholangitis at high risk of insufficient biochemical response to ursodeoxycholic acid and facilitate the timely initiation of additional treatment. Key Points: • Noninvasive imaging features based on nonenhanced pretreatment MRI may predict an insufficient biochemical response to UDCA in PBC patients. • A combined model based on three MRI features (hepatomegaly, periportal hyperintensity on T2-weighted imaging, and narrowing of the bile ducts) further improved the predictive efficacy for an insufficient biochemical response to UDCA in PBC patients, with high sensitivity and specificity. • The nomogram of the combined model showed good calibration and predictive efficacy for an insufficient biochemical response to UDCA in PBC patients. In particular, the calibration curve visualised the clinical applicability of the prediction model. [ABSTRACT FROM AUTHOR]

Purpose: ​Develop and validate a nomogram prediction model for hypertension-diabetes comorbidities based on chronic disease management in the community. Patients and methods: The nomogram prediction model was developed in a cohort of 7200 hypertensive patients at a community health service center in Hongshan District, Wuhan City. The data were collected from January 2022 to December 2022 and randomly divided into modeling and validation groups at a 7:3 ratio. The Lasso regression model was used for data dimensionality reduction, feature selection, and clinical test feature construction. Multivariate logistic regression analysis was used to build the prediction model. Results: The application of the nomogram in the verification group showed good discrimination, with an AUC of 0.9205 (95% CI: 0.8471–0.9527) and a good calibration effect. Decision curve analysis demonstrated that the predictive model was clinically useful. Conclusion: This study presents a nomogram prediction model that incorporates age, waist-height ratio and elevated density lipoprotein cholesterol (HDL-CHOLESTEROL), which can be used to predict the risk of codeveloping diabetes in hypertensive patients. [ABSTRACT FROM AUTHOR]

Background: Internet use is beneficial to mental health has been acknowledged, but its specific patterns await further investigation in China's context of urban-rural disparity. Therefore, we aimed to understand the patterns of Internet use and measure their associations with risk of depression in China from three dimensions: variety, frequency and type.Methods: We selected 18,492 urban and rural Chinses respondents aged 45 years and older from China Health and Retirement Longitudinal Study (CHARLS) 2015 dataset. Risk of depression was measured by 10-item Centre for Epidemiologic Studies (CES-D 10). Internet use was measured from three dimensions: variety, frequency and type, whereas its relationship with risk of depression was examined by multivariate logistic regression model.Results: In the whole sample, 67.2% of respondents had no risk of depression, and approximately 7% used Internet, which was inversely associated with risk of depression (AOR=0.617, 95% CI=0.444, 0.858). Rural respondents had higher risk of depression (37.1% VS 26.2%) and lower prevalence of Internet use (2.6% VS 13%). Among rural respondents, using Internet occasionally (AOR=0.338; 95% CI=0.130, 0.881), using one type of device (AOR=0.293; 95% CI=0.113, 0.760), especially mobile phone (AOR=0.427; 95% CI=0.187, 0.978) were associated with lower odds of depression. No significant associations were observed in urban respondents.Limitations: The causal relationship between variables awaits further investigation with a longitudinal design.Conclusion: Internet use was negatively associated with risks of depression, especially in rural but not in urban participants. Rural-urban disparities should be considered when designing strategies to encourage moderate Internet use. [ABSTRACT FROM AUTHOR]