Key points are not available for this paper at this time.
The emergency response ability of police officers is a critical component of their career, and is also an important support for public security. However, few researchers have focused on the factors that influence emergency response ability, especially in the group of novice policemen. On the other hand, as the popular way to train emergency response ability, case-based instruction (CBI) generates various types of data, especially valuable text data; however, such text data is always ignored because of the lack of effective analysis methods. Therefore, this study employed automatic semantic analysis and hierarchical linear regression models to investigate the factors influencing the emergency response ability of novice policemen in the process of CBI. Results indicated that, among personal differences, prior knowledge, and basic professional skills, the latter showed stronger predictive validity than the others. In particular, information processing and judgment, command and decision, and order maintenance were the main indicators. This study also illustrated that automatic semantic analysis can effectively identify deep value from semantic data, which will support stakeholders to design strategies, make decisions, conduct evaluations in training and instructions, and ultimately help sustainable development in human careers.
Sun et al. (Thu,) studied this question.