A pilot study investigating the quantitative electroencephalogram profiles of academic staff with burnout

Abstract

The construct and diagnosis of burnout are hotly debated in the literature. There is a lack of consensus regarding the conceptualisation, measurement, and the associated neural correlates of burnout, as well as its relationship with depression. University Faculty (referred to as academic staff in the South African context) represents an understudied population that is vulnerable to burnout. The main aim of this study was to conduct a pilot-study investigation to explore the psychometric and electrophysiological profile of academic burnout, as part of a wider longitudinal study investigating the efficacy of neurofeedback training as an intervention for academic burnout. The pilot study consisted of a two-stage sample design. Stage-one drew on survey-based methods with a sample of 55 academic staff members from the University of the Witwatersrand (Wits). Burnout was measured using the Burnout Assessment Tool (BAT) and the Maslach Burnout Inventory (MBI) and the Beck Depression Inventory (BDI-II) for depression. Psychometric analysis was performed using descriptive and correlational analyses of BAT, MBI, and BDI-II scores to address the research aims. The sample (n = 55) presented with high levels of burnout as determined by the BAT. Moderate and low levels of burnout were found as determined by the MBI, with a high correlation between the two measures. Mild levels of depression were found as measured by the BAT-II on average with categorical analysis revealing a maximum of 36.36% overlap between depression and burnout. Treated dimensionally, depression and burnout were significantly and strongly correlated. The consistent replication of our findings across various measures, samples, and countries, strongly questions the discriminant validity of burnout versus depression. Stage-two included a smaller sample of academic staff (N=11) drawn from the wider sample and utilised quantitative electroencephalogram (qEEG) methods at rest for the eyes open and closed conditions. Data analysis included a descriptive comparison of this sample to a normative database provided by the qEEG-Pro software. Although individual qEEG results showed substantial deviations from the norm, the group average revealed no qEEG deviations among our sample of academic staff scoring high on burnout. It appears that there is a lack of consistency among research focused on the qEEG correlates of burnout for the alpha, beta, and delta bands. Therefore, it is currently impossible to draw definite conclusions about the direction and existence of specific brain wave abnormalities in the burnout population, vi which signals the need for further exploration to establish reliable neurobiological markers of burnout.