Emotion network density in burnout

Abstract

Background: Health care workers are often affected by burnout, resulting in reduced personal well-being and professional functioning. Although emotional exhaustion is considered a core component of burnout, little is known about the dynamics of emotions and their relation to burnout. We used network analysis to investigate the correlation between the density of a negative emotion network, a marker for emotional rigidity in person-specific networks, and burnout severity.

Methods: Using an ecological momentary assessment design, the intensity of negative emotions of forty-three health care workers and medical students was assessed five times per day (between 6 am and 8 pm) for 17 days. Burnout symptoms were assessed at the end of the study period with the Maslach Burnout Inventory. Multilevel vector autoregressive models were computed to calculate network density of subject-specific temporal networks. The one-sided correlation between network density and burnout severity was assessed. The study protocol and analytic plan were registered prior to the data collection.

Results: We found a medium-sized correlation between the negative emotion network density and burnout severity at the end of the study period r(45) = .32, 95% CI = .09-1.0, p = .014).

Conclusions: The strength of the temporal interplay of negative emotions is associated with burnout, highlighting the importance of emotions and emotional exhaustion in reaction to occupational-related distress in health care workers. Moreover, our findings align with previous investigations of emotion network density and impaired psychological functioning, demonstrating the utility of conceptualizing the dynamics of emotions as a network.

Keywords: Burnout; Health care worker; Medical students; Network analysis; Stress.

Fig. 1

Visualization of the group-level emotion network. Edges indicate relationships between two emotions with one emotion at t₀ being predicted by the value of the other at t₋₁. The thickness of the edges corresponds to their strength, blue edges represent positive, red edges negative relationships. S = stressed, E = exhausted, F = frustrated, W = worried

Fig. 2

Visualization of two person-specific emotion network of participants. Visualization of two person-specific emotion network of participants number 8 and 22. Edges indicate relationships between two emotions with one emotion at t₀ being predicted by the value of the other at t₋₁. The thickness of the edges corresponds to their strength, blue edges represent positive, red edges negative relationships. S = stressed, E = exhausted, F = frustrated, W = worried

Fig. 3

Correlation between network density and burnout severity. Scatterplot with the x-axis denoting the network density (ranging from 0 to 1) and the y-axis denoting burnout severity (ranging from 0 to 6). The black line indicates the correlation, the grey area the confidence intervals