Nurse safety: How is safety climate related to affect and attitude?

Occupational accidents and injuries continue to be a critical concern for nurses, given the hazardous healthcare environment. This study advances the research on workplace safety by studying the process variables (i.e. job-related negative affect (JRNA) and job satisfaction) in explaining the relationship between safety climate and various safety criteria in nurses. Based on survey data from 326 nurses, our findings suggest that psychological safety climate is negatively related to JRNA, turnover intentions, safety workarounds, and workplace hazards. In addition, structural equation modelling indicated general support for a model in which psychological safety climate influences employee strain through job attitudes, including JRNA and job satisfaction. More specifically, job attitudes were found to mediate the relationship between psychological safety climate and turnover intentions, experience of hazards, and injuries. Safety workarounds did not significantly relate to injuries. The present study contributes to the ongoing improvement of interventions aimed at mitigating nurses’ injuries by integrating job attitudes into the safety climate–safety outcome framework.     

National Support Income Scheme For Australia: An Economic and Statistical Study

This study attempts to demonstrate the financial viability and economic soundness of adopting a National Support Income Scheme for Australia. The concept, designed to create an environment in which jobs are available for all, relies on three basic propositions including a reduction in wages and other incomes in exchange for receipt of the support income. The demonstration, based on the latest published statistics, indicates that adopting this scheme for Australia can be financially and economically justified.     

Towards Developing a Family Therapy for Melanesia

Our principal objective is to call the attention, primarily of Australian‐New Zealand family therapists, to the relevance and the urgency of developing, promoting and providing family and/or couple therapy in the Melanesian context. We emphasise the need to take into consideration Melanesian worldviews, values and social systems. We discuss a traditional ‘mediation‐reconciliation ritual’ model of solving family or couple conflicts, and also point to some of the immediate situations in which a family therapist could intervene. A few recommendations are also made for how the academic context could be utilised to develop Melanesian family therapy.     

Why females live longer than males: control of longevity by sex hormones

Females live longer than males in many species, including humans. We have traced a possible explanation for this phenomenon to the beneficial action of estrogens, which bind to estrogen receptors and increase the expression of longevity-associated genes, including those encoding the antioxidant enzymes superoxide dismutase and glutathione peroxidase. As a result, mitochondria from females produce fewer reactive oxygen species than those from males. Administering estrogens has serious drawbacks, however--they are feminizing (and thus cannot be administered to males) and may increase the incidence of serious diseases such as uterine cancer in postmenopausal women. Phytoestrogens, which are present in soy or wine, may have some of the favorable effects of estrogens without their undesirable effects. Study of gender differences in longevity may help us to understand the basic processes of aging and to devise practical strategies to increase the longevity of both females and males.     

Generalized Laplacian Distributions and Autoregressive Processes

Generalized Laplacian distribution is considered. A new distribution called geometric generalized Laplacian distribution is introduced and its properties are studied. First- and higher-order autoregressive processes with these stationary marginal distributions are developed and studied. Simulation studies are conducted and trajectories of the process are obtained for selected values of the parameters. Various areas of application of these models are discussed.     

Evaluating and Visualizing the Economic Impact of Commercial Districts Due to an Electric Power Network Disruption

Critical infrastructure networks enable social behavior, economic productivity, and the way of life of communities. Disruptions to these cyber–physical–social networks highlight their importance. Recent disruptions caused by natural phenomena, including Hurricanes Harvey and Irma in 2017, have particularly demonstrated the importance of functioning electric power networks. Assessing the economic impact (EI) of electricity outages after a service disruption is a challenging task, particularly when interruption costs vary by the type of electric power use (e.g., residential, commercial, industrial). In contrast with most of the literature, this work proposes an approach to spatially evaluate EIs of disruptions to particular components of the electric power network, thus enabling resilience‐based preparedness planning from economic and community perspectives. Our contribution is a mix‐method approach that combines EI evaluation, component importance analysis, and GIS visualization for decision making. We integrate geographic information systems and an economic evaluation of sporadic electric power outages to provide a tool to assist with prioritizing restoration of power in commercial areas that have the largest impact. By making use of public data describing commercial market value, gross domestic product, and electric area distribution, this article proposes a method to evaluate the EI experienced by commercial districts. A geospatial visualization is presented to observe and compare the areas that are more vulnerable in terms of EI based on the areas covered by each distribution substation. Additionally, a heat map is developed to observe the behavior of disrupted substations to determine the important component exhibiting the highest EI. The proposed resilience analytics approach is applied to analyze outages of substations in the boroughs of New York City.     

Evaluation of Multicriteria Decision Analysis Algorithms in Food Safety: A Case Study on Emerging Zoonoses Prioritization

Decision making in food safety is a complex process that involves several criteria of different nature like the expected reduction in the number of illnesses, the potential economic or health-related cost, or even the environmental impact of a given policy or intervention. Several multicriteria decision analysis (MCDA) algorithms are currently used, mostly individually, in food safety to rank different options in a multifactorial environment. However, the selection of the MCDA algorithm is a decision problem on its own because different methods calculate different rankings. The aim of this study was to compare the impact of different uncertainty sources on the rankings of MCDA problems in the context of food safety. For that purpose, a previously published data set on emerging zoonoses in the Netherlands was used to compare different MCDA algorithms: MMOORA, TOPSIS, VIKOR, WASPAS, and ELECTRE III. The rankings were calculated with and without considering uncertainty (using fuzzy sets), to assess the importance of this factor. The rankings obtained differed between algorithms, emphasizing that the selection of the MCDA method had a relevant impact in the rankings. Furthermore, considering uncertainty in the ranking had a high influence on the results. Both factors were more relevant than the weights associated with each criterion in this case study. A hierarchical clustering method was suggested to aggregate results obtained by the different algorithms. This complementary step seems to be a promising way to decrease extreme difference among algorithms and could provide a strong added value in the decision-making process.     

Application of the Hyper‐Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes

The hyper‐Poisson distribution can handle both over‐ and underdispersion, and its generalized linear model formulation allows the dispersion of the distribution to be observation‐specific and dependent on model covariates. This study's objective is to examine the potential applicability of a newly proposed generalized linear model framework for the hyper‐Poisson distribution in analyzing motor vehicle crash count data. The hyper‐Poisson generalized linear model was first fitted to intersection crash data from Toronto, characterized by overdispersion, and then to crash data from railway‐highway crossings in Korea, characterized by underdispersion. The results of this study are promising. When fitted to the Toronto data set, the goodness‐of‐fit measures indicated that the hyper‐Poisson model with a variable dispersion parameter provided a statistical fit as good as the traditional negative binomial model. The hyper‐Poisson model was also successful in handling the underdispersed data from Korea; the model performed as well as the gamma probability model and the Conway‐Maxwell‐Poisson model previously developed for the same data set. The advantages of the hyper‐Poisson model studied in this article are noteworthy. Unlike the negative binomial model, which has difficulties in handling underdispersed data, the hyper‐Poisson model can handle both over‐ and underdispersed crash data. Although not a major issue for the Conway‐Maxwell‐Poisson model, the effect of each variable on the expected mean of crashes is easily interpretable in the case of this new model.     

Monte Carlo Sensitivity Analysis of Unknown Parameters in Hazardous Materials Transportation Risk Assessment

The US Department of Transportation was interested in the risks associated with transporting Hydrazine in tanks with and without relief devices. Hydrazine is both highly toxic and flammable, as well as corrosive. Consequently, there was a conflict as to whether a relief device should be used or not. Data were not available on the impact of relief devices on release probabilities or the impact of Hydrazine on the likelihood of fires and explosions. In this paper, a Monte Carlo sensitivity analysis of the unknown parameters was used to assess the risks associated with highway transport of Hydrazine. To help determine whether or not relief devices should be used, fault trees and event trees were used to model the sequences of events that could lead to adverse consequences during transport of Hydrazine. The event probabilities in the event trees were derived as functions of the parameters whose effects were not known. The impacts of these parameters on the risk of toxic exposures, fires, and explosions were analyzed through a Monte Carlo sensitivity analysis and analyzed statistically through an analysis of variance. The analysis allowed the determination of which of the unknown parameters had a significant impact on the risks. It also provided the necessary support to a critical transportation decision even though the values of several key parameters were not known.     

Multigenerational punishments on the children of Salvadoran immigrant and deported fathers

Drawing on qualitative research conducted in the United States and in El Salvador, the author examines the experiences of the children of 40 immigrant men and 40 deported men. This study reveals the harmful effects of U.S. immigration policies and enforcement practices on the children of Salvadoran immigrant and deported fathers. Their children were found to have experienced the unintended consequences of U.S. immigration laws and enforcement practices in their own lives and relationships. These findings support Enriquez's (2015) concept of “multigenerational punishments” where children of immigrant parents share the risks and limitations associated with their parent's immigration status. They also experience the negative spill-over effects of immigration policies and enforcement practices even though they were not directly targeted by these laws. This study reveals multigenerational punishments manifested in the form of social, economic, emotional, and physical inequalities which negatively affected the children of Salvadoran immigrant and deported fathers. As a result, many of their children experienced harmful changes in their lives and relationships under the U.S. immigration enforcement regime. This study is significant in that it provides insight into the issues that immigrant families face and the need for policy interventions for immigrant and deported parents and their children.