风险"疫苗"——智能化安全管理体系

采用预防医学中疫苗的概念,用于预防安全科学的风险管理.选用液氯仓库为研究对象,开发风险"疫苗"--智能化安全管理体系,预防氯气泄漏、中毒、火灾、爆炸事故的发生.风险"疫苗"的制作过程采用计算机技术、通讯技术和网络技术,使预防系统风险的可靠性进一步提高.     

基于成本收益理论的井场作业人员不安全行为决策分析

为有效制定油气田井场作业人员不安全行为的防范措施,降低事故发生可能性,基于成本收益理论建立油气田井场作业人员的行为决策模型,通过仿真计算定量分析了不同影响因素对不安全行为产生的影响程度。结果表明:正向激励和负向激励对不安全行为的降低幅度为21.8%,17.2%,负向激励和监督力度共同作用下不安全行为可降低44.8%。油田企业在保证利润的前提下应优先提升HSE奖励水平,合理控制罚金水平的同时加大管理监督力度,从而有效减少作业人员不安全行为的发生,保障企业的安全生产。     

社会资本对山区居民山洪灾害应急避险能力的影响研究*

为研究社会资本对山区居民应急避险能力的影响,在广泛调研的基础上采用结构方程模型分析社会资本与山区居民应急避险能力各要素之间的作用关系。结果表明:个人网络对避险知识和避险意识有显著的正向影响,对避险行为影响不显著;信任与避险知识和避险意识呈显著的正向影响,而与避险行为呈反向影响,但信任可以通过避险知识和避险意识间接正向影响避险行为;互惠对避险知识影响不显著,但互惠与避险意识和避险行为存在显著的正向关系;避险知识和避险意识对避险行为呈显著正向影响,其中避险意识对避险行为影响更为显著。因此可以通过增加山区居民社会资本的方式提高山区居民山洪灾害应急避险能力。     

基于社会技术系统的复工企业疫情防控风险路径研究*

为对复工企业疫情防控中的风险致因因素进行分析,探寻复工企业疫情防控的风险路径,基于社会技术系统理论,建立宏观工效学模型,通过查阅并分析国家、地方政府、企业文件以及相关文献,从人员、技术、组织管理、内部环境及外部环境5个子系统出发,识别出16个复工企业疫情失控致因因素;运用社会网络分析（SNA）构建复工企业疫情防控关系网络,对各因素节点中心度进行计算,分析各节点在网络中的位置及影响程度;采用贝叶斯网络（BN）进行参数学习和推理学习,找出复工企业疫情失控的最大致因链。结果表明:复工企业疫情失控关系网络中,存在政府监督管理不力→防疫物资筹备不足→日常监管不到位,政府监督管理不力→ 防疫宣传、培训缺失→员工防疫知识欠缺,政府监督管理不力→防疫宣传、培训缺失→日常监管不到位3条最长风险路径。研究结果可帮助复工企业针对最长风险路径中的因素进行管理,从而有效地为复工企业防疫提供理论支持。     

基于AHP-熵权法的基层地震科普能力评价*

为提高基层地震科普评价工作的客观性和精准性,根据“环境-投入-产出-效果”的评价框架,建立涵盖保障能力、队伍能力、设施能力、产出能力、活动能力5类26项指标的基层地震科普能力评价指标体系,选用AHP-熵权法计算指标的组合权重;以天津为例进行验证,并提出相关工作建议。结果表明:建立的评价模型对于开展定量化的区域基层地震科普能力评价具有较强的参考价值,评价结果与实际情况基本相符;天津市基层地震科普在硬件建设方面取得较好成效,但整体能力依然偏弱,16个区平均得分仅为0.344 8;发展不平衡、软实力不足是制约当前发展最重要的2个因素;提出相关的工作建议可为优化基层科普工作布局、提高工作实效提供有益参考。     

An application of a modified theory of planned behavior model to investigate adolescents’ job safety knowledge,norms, attitude and intention to enact workplace safety and health skills

Introduction: For many reasons, including a lack of adequate safety training and education, U.S. adolescents experience a higher rate of job-related injury compared to adult workers. Widely used social-psychological theories in public health research and practice, such as the theory of planned behavior, may provide guidance for developing and evaluating school-based interventions to prepare adolescents for workplace hazards and risks. Method: Using a structural equation modeling approach, the current study explores whether a modified theory of planned behavior model provides insight on 1,748 eighth graders’ occupational safety and health (OSH) attitude, subjective norm, self-efficacy and behavioral intention, before and after receiving instruction on a free, national young worker safety and health curriculum. Reliability estimates for the measures were produced and direct and indirect associations between knowledge and other model constructs assessed. Results: Overall, the findings align with the theory of planned behavior. The structural equation model adequately fit the data; most path coefficients are statistically significant and knowledge has indirect effects on behavioral intention. Confirmatory factor analyses suggest that the knowledge, attitude, self-efficacy, and behavioral intention measures each reflect a unique dimension (reliability estimates ≥0.86), while the subjective norm measure did not perform adequately. Conclusion: The findings presented provide support for using behavioral theory (specifically a modified theory of planned behavior) to investigate adolescents’ knowledge, perceptions, and behavioral intention to engage in safe and healthful activities at work, an understanding of which may contribute to reducing the downstream burden of injury on this vulnerable population—the future workforce. Practical application: Health behavior theories, commonly used in the social and behavioral sciences, have utility and provide guidance for developing and evaluating OSH interventions, including those aimed at preventing injuries and promoting the health and safety of adolescent workers in the U.S., who are injured at higher rates than are adults.     

Advancing injury and violence prevention through data science

Introduction: The volume of new data that is created each year relevant to injury and violence prevention continues to grow. Furthermore, the variety and complexity of the types of useful data has also progressed beyond traditional, structured data. In order to more effectively advance injury research and prevention efforts, the adoption of data science tools, methods, and techniques, such as natural language processing and machine learning, by the field of injury and violence prevention is imperative. Method: The Centers for Disease Control and Prevention’s (CDC) National Center for Injury Prevention and Control has conducted numerous data science pilot projects and recently developed a Data Science Strategy. This strategy includes goals on expanding the availability of more timely data systems, improving rapid identification of health threats and responses, increasing access to accurate health information and preventing misinformation, improving data linkages, expanding data visualization efforts, and increasing efficiency of analytic and scientific processes for injury and violence, among others. Results: To achieve these goals, CDC is expanding its data science capacity in the areas of internal workforce, partnerships, and information technology infrastructure. Practical Application: These efforts will expand the use of data science approaches to improve how CDC and the field address ongoing injury and violence priorities and challenges.     

Failure mode and effect analysis using regret theory and PROMETHEE under linguistic neutrosophic context

Failure mode and effect analysis (FMEA), which aims to identify and assess potential failure modes in a system, has been widely utilized in diverse areas for improving and enhancing the performance of systems due to it is a powerful and useful risk and reliability assessment instrument. However, the conventional FMEA approach has been suffered several criticisms for it has some shortcomings, such as unable to handle ambiguous and uncertain information, neglect the relative weights of risk criteria, and without considering the psychological behaviors of decision-makers. To ameliorate these limitations, this paper aims at establishing a hybrid risk ranking model of FMEA via combing linguistic neutrosophic numbers, regret theory, and PROMETHEE (Preference ranking organization method for enrichment evaluation) approach. In the presented model, linguistic neutrosophic numbers are adopted to capture decision-makers’ evaluation regarding the failure modes on each risk criterion. A modified PROMETHEE approach based on regret theory is presented to obtain the risk priority of failure modes considering the psychological behaviors of decision-makers. Moreover, a maximizing deviation model and TOPSIS (Technique for order preference similar to ideal solution) are separately applied to derive the weights of risk criteria and decision-makers. Finally, a numerical example relating to the supercritical water gasification system is employed to implement the presented method, and the effectiveness and feasibility of the proposed model are validated by the results derived from a sensitivity and comparison analysis.     

The passion bug: How and when do leaders inspire work passion?

Drawing from signaling theory, we propose a work passion transfer model where leaders' passion is transmitted to employees through the former's leadership style and is contingent on employees' perceived importance of performance to self-esteem (IPSE). Data from 201 supervisor–employee dyads from the health-care industry show that leaders' harmonious passion led to employees' harmonious passion through charismatic leadership, whereas contingent reward leadership accounted for the transfer of obsessive passion; IPSE did not play a moderating role for either form of passion. Results from a supplementary study further reveal that the link between leadership and employee passion operated through employees' perception of leader passion and that employees' IPSE accentuated for the relationship between perceived leader obsessive passion and employees' obsessive passion. This study advances research in work passion, leadership, and signaling theory and provides important implications for managerial practice.     

Abusive according to whom? Manager and subordinate perceptions of abusive supervision and supervisors' performance

Recognizing that supervisor–subordinate dyads exist within a broader organizational hierarchy, we examine how the individual's role within the organizational hierarchy influences perceptions of abusive supervision. Specifically, we examine how supervisors' abusive behaviors are perceived by abusive supervisors' managers as well as abusive supervisors' subordinates. Drawing on role theory, we propose that these perceptions will differ. Further, we suggest that these differences will be reflected in different relationships between manager-rated abusive supervision and subordinate-rated abusive supervision and managers' evaluations of supervisor performance. Results from manager–supervisor–subordinate triads indicate differences between managers' and subordinates' view of abusive supervision. Further, managers' perceptions of abuse were related to supervisors' in-role performance, whereas subordinates' perceptions of abuse were related to workgroup performance. In Study 2, we replicate these findings and expand our investigation to an examination of supervisors' contextual performance. Additionally, we examine another contextual characteristic—aggressive climate—and demonstrate it influences how abusive supervision relates to managerial evaluations of supervisor performance. Future research and managerial implications are discussed.