Anxiety, job satisfaction, supervisor support and turnover intentions of mid-career nurses: A structural equation model analysis.

AIMS: To explore the effects of four predictors of anxiety (work constraints, work/family conflict, verbal abuse and negative team orientation) among nurses and their subsequent effects on job satisfaction and turnover intentions; and to examine the moderating effect of supervisor support on the relationship between job satisfaction and turnover intentions. BACKGROUND: Work-related anxiety is a well-known predictor of employee burnout. Research suggests the prevalence of stress in the workplace varies by occupation, with stress among nurses one of the highest. METHODS: We employed data from the 2015 national survey of licensed registered nurses (n=1,080). We assessed the conceptual model using partial least squares structural equation modeling (PLS-SEM). RESULTS: Work constraints, work/family conflict, and negative team orientation lead to anxiety, which diminished job satisfaction and ultimately increased turnover intentions. Supervisor support weakened the job dissatisfaction-turnover relationship. CONCLUSIONS: These findings suggest that the common experiences reported by health care professionals lead to anxiety and ultimately turnover intentions and emphasize the role of supervisor support. IMPLICATIONS FOR NURSING MANAGEMENT: The supervisor's role is crucial to the implications of workplace-generated anxiety for nurse job satisfaction and turnover intentions. As such, nurse managers need to develop tangible strategies to help nurses navigate these contextual constraints.

A rule-based semantic approach for data integration, standardization and dimensionality reduction utilizing the UMLS: Application to predicting bariatric surgery outcomes.

Utilization of existing clinical data for improving patient outcomes poses a number of challenging and complex problems involving lack of data integration, the absence of standardization across inhomogeneous data sources and computationally-demanding and time-consuming exploration of very large datasets. In this paper, we will present a robust semantic data integration, standardization and dimensionality reduction method to tackle and solve these problems. Our approach enables the integration of clinical data from diverse sources by resolving canonical inconsistencies and semantic heterogeneity as required by the National Library of Medicine's Unified Medical Language System (UMLS) to produce standardized medical data. Through a combined application of rule-based semantic networks and machine learning, our approach enables a large reduction in dimensionality of the data and thus allows for fast and efficient application of data mining techniques to large clinical datasets. An example application of the techniques developed in our study is presented for the prediction of bariatric surgery outcomes.