ArticleInternational journal of environmental research and public health2021
Correlation in Causality: A Progressive Study of Hierarchical Relations within Human and Organizational Factors in Coal Mine Accidents.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Factors influencing unsafe acts in the automotive industry using grounded theory and fuzzy DEMATEL.Scientific reports · 2025Article
- A chemical accident cause text mining method based on improved accident triangle.BMC public health · 2024Article
- Risk-Reduction Research in Occupational Safety and Ergonomics: An Editorial.International journal of environmental research and public health · 2023Article
- Research on Coal Mine Building Compliance Inspection System Based on Accident Causation and BIM in China.International journal of environmental research and public health · 2022Article
- Comprehensive Evaluation of Deep Coal Miners' Unsafe Behavior Based on HFACS-CM-SEM-SD.International journal of environmental research and public health · 2022Article
- Risk Factors Identification of Unsafe Acts in Deep Coal Mine Workers Based on Grounded Theory and HFACS.Frontiers in public health · 2022Article
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Authors and funding
5 authors.
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Abstract
It has been revealed in numerous investigation reports that human and organizational factors (HOFs) are the fundamental causes of coal mine accidents. However, with various kinds of accident-causing factors in coal mines, the lack of systematic analysis of causality within specific HOFs could lead to defective accident precautions. Therefore, this study centered on the data-driven concept and selected 883 coal mine accident reports from 2011 to 2020 as the original data to discover the influencing paths of specific HOFs. First, 55 manifestations with the characteristics of the coal mine accidents were extracted by text segmentation. Second, according to their own attributes, all manifestations were mapped into the Human Factors Analysis and Classification System (HFACS), forming a modified HFACS-CM framework in China's coal-mining industry with 5 categories, 19 subcategories and 42 unsafe factors. Finally, the Apriori association algorithm was applied to discover the causal association rules among external influences, organizational influences, unsafe supervision, preconditions for unsafe acts and direct unsafe acts layer by layer, exposing four clear accident-causing "trajectories" in HAFCS-CM. This study contributes to the establishment of a systematic causation model for analyzing the causes of coal mine accidents and helps form corresponding risk prevention measures directly and objectively.
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