Understanding the Ecological Validity of Relying Practice as a Basis for Risk Identification

Understanding the reliability of hazardous organizations and their protective systems is central to understanding the risk they produce. Work on “high reliability organization” has done much to illuminate the conditions in which social organization becomes reliable in highly demanding conditions. But risk depends just as much on how relying entities do their relying as it does on the reliability of the entities they rely on. Patterns of relying are often opaque in sociotechnical systems, and processes of relying and being relied on are mutually influencing in complex ways, so the relationship between relying and risk may not be at all obvious. This study was an attempt to study relying as a social practice, in particular analyzing how it had ecological validity in a social organization—how practice was responsive to the conditions in which it took place. This involved observational fieldwork and inductive, qualitative analysis on an offshore oil and gas production platform that was nearing the end of its design life and undergoing refurbishment. The analysis produced four main categories of ecological validity: responsiveness to formal organization, responsiveness to situational contingency, responsiveness to information asymmetry, and responsiveness to sociomateriality. This ecological validity of relying practice should be a primary focus of risk identification, assessing how relying can become mismatched to reliability in certain ways, both when relying practice is responsive to circumstances and when it is not.     

考虑自适应行为的研发网络风险传播模型构建及仿真

构建风险视域下研发网络企业自适应行为规则，基于SIS模型构建研发网络风险传播模型，运用数值仿真的方法通过改变模型参数探索在考虑自适应行为的情况下研发网络的风险传播规律，研究结果表明：（1）C1策略增强了网络的层次性和社团强度，一定程度上抑制了研发网络中风险的传播；C2策略下节点之间新连接的建立更多是基于临近性的考量，容易陷入路径依赖和能力陷阱；（2）研发网络企业的自适应行为会导致社团强度的涨落，平均路径长度的下降以及平均聚类系数的增长充分体现出C1策略的有效性。（3）C1策略下，断边概率p与I*之间呈现"U"型相关关系；在C2策略下随着断边概率p的增长I*逐渐降低。（4）在C1策略和C2策略下，随着参数ζ的增长I*也随之增长，可知组织依赖水平是研发网络风险传播控制中需要重点关注的因素。本文揭示了在考虑自适应行为的情况下研发网络的风险传播规律，为网络化运作背景下研发网络治理提供理论依据。     

The Child Abuse Risk Evaluation Dutch Version (CARE-NL): A retrospective validation study

Abstract

The Child Abuse Risk Evaluation – Dutch version (CARE-NL) is a structured professional guideline for assessing the risk of all types of child abuse. The CARE-NL comprises 18 risk factors: eight Parental characteristics, three Parent-Child interaction factors, five Family factors, one Child vulnerability factor, and an extra factor for child sexual abuse risk. We examined interrater reliability and predictive accuracy of the instrument in a retrospective study at Advice and Reporting Centers on Child Abuse (ARCCA) in The Netherlands. The ARCCA files contained limited information on Parental risk factors, while parental characteristics, such as mental disorder and substance abuse, are the most important risk factors for (repeated) child abuse. On the other hand, the majority of the files included ample information on child and family characteristics. The CARE-NL could be reliably coded by trained raters and the predictive accuracy for out-of-home placement (AUC = .73) and placement of the child under court supervision (AUC = .78), at two years follow-up, was adequate. Use of the CARE-NL ensures that the assessment of the risk of child abuse focuses on the most important, empirically based risk factors in a structured and coherent way.     

Childhood exposure to the Second World War and financial risk taking in adult life

Adverse childhood experiences might have long-lasting effects on decisions under uncertainty in adult life. Merging the European Survey on Health, Ageing and Retirement with data on conflict events during the Second World War, and relying on region-by-cohort variation in war exposure, we show that warfare exposure during childhood is associated with lower financial risk taking in later life. Individuals who experienced war episodes as children hold less – and are less likely to hold – stocks, but are more likely to hold life insurance, compared to non-exposed individuals. Effects are robust to the inclusion of potential mediating factors, and are tested for nonlinearity and heterogeneity. Moreover, we provide evidence of hedonic adaptation to war, as high and low intensity of war exposure have comparable long-term effects. We also document that war exposure in childhood increases sensitivity to financial uncertainty since exposed-to-war individuals are less likely to hold stocks after periods of high volatility. Finally, we shed light on the most likely mechanism in the relationship between war exposure and financial risk taking – i.e., enhanced sensitivity to uncertainty – and we show that preferences, and not beliefs, channel this relationship.     

Spatial Risk Analysis of Power Systems Resilience During Extreme Events

The increased frequency of extreme events in recent years highlights the emerging need for the development of methods that could contribute to the mitigation of the impact of such events on critical infrastructures, as well as boost their resilience against them. This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events. A Severity Risk Index (SRI) with the support of real‐time monitoring assesses the impact of the extreme events on the power system resilience, with application to the effect of windstorms on transmission networks. The index considers the spatial and temporal evolution of the extreme event, system operating conditions, and the degraded system performance during the event. SRI is based on probabilistic risk by condensing the probability and impact of possible failure scenarios while the event is spatially moving across a power system. Due to the large number of possible failures during an extreme event, a scenario generation and reduction algorithm is applied in order to reduce the computation time. SRI provides the operator with a probabilistic assessment that could lead to effective resilience‐based decisions for risk mitigation. The IEEE 24‐bus Reliability Test System has been used to demonstrate the effectiveness of the proposed online risk analysis, which was embedded in a sequential Monte Carlo simulation for capturing the spatiotemporal effects of extreme events and evaluating the effectiveness of the proposed method.     

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose–response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.     

Learning to Manage the Multirisk World

Risk assessment, perception, and management tend to focus on one risk at a time. But we live in a multirisk world. This essay in honor of the 40th anniversary of the Society for Risk Analysis (SRA) and the journal Risk Analysis suggests that we can—and have already begun to—strengthen risk analysis and policy outcomes by moving from a focus on the single to the multiple—multiple stressors, multiple impacts, and multiple decisions. This evolution can improve our abilities to assess actual risks, to confront and weigh risk-risk trade-offs and innovate risk-superior moves, and to build learning into adaptive regulation that adjusts over time. Recognizing the multirisk reality can help us understand complex systems, foresee unintended consequences, design better policy solutions, and learn to improve.     

Rural-urban differences in college students’ cardiovascular risk perceptions

Abstract

Objective: To examine rural-urban differences in college students’ cardiovascular risk perceptions. Participants: College students in rural (n?=?61) and urban (n?=?57) Kentucky counties were recruited from November 2012 to May 2014. Methods: This was a secondary data analysis of a cross-sectional study examining rural-urban differences in cardiovascular risk factors. Students rated their risk for developing high blood pressure, diabetes, high cholesterol, heart disease, having a stroke, and gaining excess weight. Chi-square and logistic regression were used for data analysis. Results: Rural students had lower odds of perceived high risk for developing high blood pressure compared to urban students (odds ratio (OR): 0.32, 95% CI: 0.11–0.96) after adjusting for race, sex, and body mass index. This association was not observed after adjusting for healthcare access variables. No other significant differences were observed. Conclusions: Efforts to raise perceived risk for developing hypertension among rural college students may be warranted.     

Knowledge of Suicide Risk Factors,But Not Suicide Ideation Severity,Is Greater Among College Students Who Viewed 13 Reasons Why

Abstract

Objective: To test associations between viewing 13 Reasons Why, Season 1 and past week suicide ideation severity, behavior risk, stigma, and knowledge in college students. To explore whether personal exposure to suicide and depressive symptom severity moderated these associations. Participants: Eight-hundred and eighteen college students, 64% (n?=?522) of whom watched 13 Reasons Why. Methods: Students completed surveys online. Multivariate negative binomial regressions were used to test associations between watching 13 Reasons Why and suicide-related variables, and interaction terms. Results: Suicide ideation severity and suicide behavior risk were not significantly associated with viewing 13 Reasons Why; however, there was limited statistical power to detect associations. The association between watching 13 Reasons Why and greater suicide knowledge was stronger among those who did not have personal exposure to suicide. Conclusions: 13 Reasons Why may be a platform for psychoeducation on suicide, particularly among those who do not have personal exposure.     

‘Seeing through their Eyes’: Towards Understanding Risk and Protection Perspectives of Young Bedouin Children in the Unrecognised Villages of the Naqab

This article is based on a participatory, context-informed study that examined perception of ‘risk’ and ‘protection’ among 30 Bedouin children aged four to five. It was conducted in the Bedouin unrecognised villages — Southern Israel and utilised photography, drawings and verbal explanations. The analysis yielded seven themes representing children’s perceptions of risk and protection. It indicated that children facing extreme adversity were aware of numerous risk conditions, including a lack of infrastructure and the fear of losing their homes, thus adversely affecting their well-being. Children’s insights, and suggested modes of protection, as the study findings reveal, are crucial for promoting children’s welfare.