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Learning Objective 1: The learner will be able to discuss the implications of emotional intelligence on bedside quality of patient care.
Learning Objective 2: The learner will be able to analyze the differences between components of emotional intelligence.
The Impact of Nurses’ Emotional Intelligence on
Quality of Patient Care
Emotional Intelligence (EI) is a concept that is becoming increasingly significant for its ability to describe human interactions and performance in the workplace setting. Although studied primarily in business, EI has been studied in health care industry management. However, EI of direct patient care nurses has not been assessed nor has its impact on patient outcomes. The aim of our research study was to explore if units employing Registered Nurses (RNs) with higher emotional intelligence provide higher quality of care to patients.
Methods:
Our study used a predictive, cross-sectional design, based on Salovey and Mayer’s definition of EI as “the ability to monitor one’s own and other’s feelings and emotions, to discriminate among them and use this information to guide one’s own thinking and actions.” We used the Mayer-Salovey–Caruso Emotional Intelligence Test (MSCEIT) to measure emotional intelligence. Each participant in our study took the MSCEIT and received an individual report of EI. The scores of participants who worked on the same unit were combined to determine a mean emotional intelligence score for the unit. Demographic variables were collected as potential covariates. For each patient care unit, we averaged three months of quality metrics including five nursing-sensitive patient care outcomes and five nursing care compliance indicators. Simple linear regression will be used to see if a unit’s nurses’ mean emotional intelligence scores can be effective in predicting quality of patient care. Demographic variables will be added to the regression model as covariates to determine their impact on quality of patient care. Statistical significance is set at p < 0.05
Results:
Currently in data analysis
Conclusion:
Currently in data analysis