Who are root hazards? A research on optimization of safety training management in coal mine enterprises from data-driven perspective

Ziwei Fa, Ke Yan, Zunxiang Qiu, Yueqian Zhang, Quanlong Liu and Xinchun Li

Resources Policy, 2024, vol. 91, issue C

Abstract: As a significant measure of behavioral risk control, the quality and efficiency of safety training and education can directly affect employees' unsafe behavioral patterns. Therefore, in order to scientifically improve the effectiveness of safety training management, this paper adopts the idea of data-driven research paradigm. Firstly, the accident causation model in the coal mine industry is redefined centered on unsafe behaviors and training loopholes as a starting point. Then the decision tree and key phrase extraction algorithm are applied respectively to mine 715 pieces of employees' violation data and safety training data in coal mine enterprises and the crucial characteristics of employees’ attributes and safety training mode along with the specific content of unsafe behaviors, are revealed in turn. Finally, the data information is transformed into decision information from three aspects which are specifying the training audience, improving the training mode and refining the training content, so as to realize the overall optimization of safety training management in coal mine enterprises.

Keywords: Coal mine; Safety training; Data-driven based; Decision tree; Key phrase extraction (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:91:y:2024:i:c:s0301420724002587

DOI: 10.1016/j.resourpol.2024.104891

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