Applying Social Network Analysis to the Spread of Moods and Emotions Among Graduate Nursing Students
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Turnover costs average 21 percent of an employee’s annual salary in healthcare. Organizations invest more for the turnover of specialized employees. Hospitals expend thousands of dollars annually hiring and training new staff. Healthcare workers spend a significant percentage of time at work . Heavy workloads and long hours, burn out and turnover are a continued concern. Relationships in a nursing environment are complex and highly integrated. Affect is the experience of moods and emotions, and is influenced by behavior and interactions. Affect influences performance, decision making, safety, turnover and absence, prosocial behavior, and negotiation and conflict resolution which may impact on patient outcomes. Affect contagion, the transfer of moods and emotions in groups, occurs with interaction. It’s existence is well established in corporate and industry settings to influence workplace outcomes. Understanding how relationships impact affect, behaviors, and cognition can lead to a healthier work environment therefore improving patient outcomes. Workplace affect can be of critical importance. An individual’s work affect is influenced by those around them. Affect contagion, the transfer of affect between individuals, is well documented. Affect transmission has been studied in healthcare. The purpose of the study was to observe and describe the spread of affect in a group of military graduate nursing students using social network analysis.
Healthcare affect contagion research has occurred on the dyadic level or as a single time point, identifying the presence of collective affect but does not account for the dynamic capabilities of affect to evolve and change. Using social network methodologies to examine affect contagion provides a conduit to understand affect contagion as an environmental phenomenon. Social network analysis (SNA) identifies important individuals, channels of affect flow, the dynamics behind positive and negative self-reinforcement, and the impact of network topologies on the effectiveness of affect-oriented interventions.
The Kelly and Barsade middle range theory of moods and emotions in small groups and work teams was used to develop the design of the study, find valid measurement tools, and conduct analysis.
There were 35 participants from a closed network of 60 advanced practice nursing students. Social network surveys established relationships among the participants. Student’s affect was surveyed twice daily 14 times over the course of a semester to measure changes in affect. Affect expressivity and susceptibility to the affect of others was measured. This study utilized social network analysis and linear statistics to identify and visualize relationships among graduate nursing students during an academic semester. The relationships were correlated with the affect of participants to determine the impact of individual moods and emotions on those with whom they interact.
Using assortative mixing calculations, affect among participants was correlated with their identified “friends” at four time points and affect similarity was correlated with academic specialty program and the amount of time spent with classmates. Stimulus exposure on three data collection days changed the affect of exposed participants. The students exposed to the stimuli demonstrated a change in affect, and assortativity correlation demonstrated similar affect. Environmental events occurring during the semester were identified and associated with changes in affect among the participants. Affect contagion occurs in advanced practice nursing student networks and affect influencing stimuli have the potential to influence the exposed individual.
The moods and emotions of nurses are impacted by the events of the day and those whom they spend much of their time. Developing environments that support and facilitate high positive affect and low negative affect of nursing staff should improve the quality of patient care. Utilizing advanced statistical methods, like social network analysis, promotes comprehensive quantification of the relationship that exists among group members. An understanding of the behaviors exhibited by group members related their affective state may facilitate development of methods to decrease negative affect and increase positive affect as a means to promote a healthy productive environment.