Validating the effectiveness of a self-report tool to predict unsafe behavior of industrial workers: a QEEG analysis.

Abstract

Objectives. Unsafe behavior (UB) is defined as the likelihood of intentionally or unintentionally deviating from pre-defined plans. This study aims to investigate the validation of a self-report tool for measuring workers' cognitive-based UB using quantitative electroencephalography (QEEG). Methods. The cognitive-based unsafe behavior questionnaire (CUBQ) was completed by 632 front-line workers in a manufacturing industry to identify differences in the backgrounds of the subjects regarding UBs. Two groups were then selected as extreme groups and QEEG was conducted based on the international 10-20 electrode placement. Results. The mean values of absolute power (AP), alpha/beta ratio (ABR) and alpha/gamma ratio (AGR) from brain oscillations in different regions of the cortex were significantly different between the studied groups (p < 0.05). Additionally, these values were found to be significantly correlated with slips, lapses and mistakes, as measured by certain scales of the CUBQ (p < 0.05). Conclusions. The findings of this study indicated differences in brain oscillation activities among industrial workers with different UB backgrounds. These results confirm the effectiveness of CUBQ as a proactive tool for safety practitioners to predict industrial workers' UBs.