Abstract Introduction A vast amount of research has been conducted to identify human and organizational factors that contribute to the occurrence of occupational incidents. Considering the identified factors, the question is how much the occupational incident probability will decrease in the absence of one or more recognized contributory factors. Methods Twenty-one fatal accident reports were selected for Root Cause Analysis (RCA). The contributory factors were identified by content analysis of the accident scenarios. A 5-point Likert questionnaire was developed to measure the probability of identified factors. Using the identified contributory factors and their corresponding probabilities, a Bayesian network model was constructed for estimating the probability of the occupational incident in the absence of each contributory factor. Results Procedure violation, poor risk perception, and poor management commitment were three top-ranking contributory factors. The Bayesian network estimated that preventing procedures violation could cause a reduction of 44 % in the occupational incident probability. Conclusion Using Bayesian network’s advantages is an effective technique for quantifying occupational safety risks. Ranking the contributory factors enables us to choose the most effective prevention strategies. Procedure violation (a type of unsafe act) was the most influencing factor in occupational incident probability.

中文翻译：

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职业事故影响因素排序：石油行业的贝叶斯网络模型

摘要 介绍 已经进行了大量研究来确定导致职业事故发生的人和组织因素。考虑到已识别的因素，问题是在没有一种或多种公认的促成因素的情况下，职业事故概率会降低多少。方法 选择 21 份致命事故报告进行根本原因分析 (RCA)。通过事故情景的内容分析确定了促成因素。开发了 5 点李克特问卷来衡量已识别因素的概率。使用确定的促成因素及其相应的概率，构建了贝叶斯网络模型，用于在没有每个促成因素的情况下估计职业事件的概率。结果 违反程序、差的风险感知和差的管理承诺是三个最重要的促成因素。贝叶斯网络估计，防止程序违规可导致职业事故概率降低 44%。结论利用贝叶斯网络的优势是量化职业安全风险的有效技术。对促成因素进行排名使我们能够选择最有效的预防策略。程序违规（一种不安全行为）是职业事故发生概率的最大影响因素。结论利用贝叶斯网络的优势是量化职业安全风险的有效技术。对促成因素进行排名使我们能够选择最有效的预防策略。程序违规（一种不安全行为）是职业事故发生概率的最大影响因素。结论利用贝叶斯网络的优势是量化职业安全风险的有效技术。对促成因素进行排名使我们能够选择最有效的预防策略。程序违规（一种不安全行为）是职业事故发生概率的最大影响因素。