The effect of immigration on religious belief and practice: A theologizing or alienating experience?

Using data from the New Immigrant Survey, we examine the religious beliefs and practices of new legal immigrants to the United States. We find that Christian immigrants are more Catholic, more Orthodox, and less Protestant than American Christians, and that those immigrants who are Protestant are more likely to be evangelical. In addition to being more Catholic and more Orthodox than American Christians, the new immigrants are also paradoxically less Christian, with a fifth reporting some other faith. Detailed analysis of reported church attendance at places of origin and in the United States suggest that immigration is a disruptive event that alienates immigrants from religious practice rather than “theologizing” them. In addition, our models clearly show that people who join congregations in the United States are highly selected and unrepresentative of the broader population of immigrants in any faith. In general, congregational members were more observant both before and after emigration, were more educated, had more cumulative experience in the United States, and were more likely to have children present in the household and be homeowners and therefore yield biased representations of all adherents to any faith. The degree of selectivity and hence bias also varies markedly both by religion and nationality.     

Dose‐Response Models Incorporating Aerosol Size Dependency for Francisella tularensis

The effect of bioaerosol size was incorporated into predictive dose‐response models for the effects of inhaled aerosols of Francisella tularensis (the causative agent of tularemia) on rhesus monkeys and guinea pigs with bioaerosol diameters ranging between 1.0 and 24 μm. Aerosol‐size‐dependent models were formulated as modification of the exponential and β‐Poisson dose‐response models and model parameters were estimated using maximum likelihood methods and multiple data sets of quantal dose‐response data for which aerosol sizes of inhaled doses were known. Analysis of F. tularensis dose‐response data was best fit by an exponential dose‐response model with a power function including the particle diameter size substituting for the rate parameter k scaling the applied dose. There were differences in the pathogen's aerosol‐size‐dependence equation and models that better represent the observed dose‐response results than the estimate derived from applying the model developed by the International Commission on Radiological Protection (ICRP, 1994) that relies on differential regional lung deposition for human particle exposure.     

Perceptions of Randomized Security Schedules

Security of infrastructure is a major concern. Traditional security schedules are unable to provide omnipresent coverage; consequently, adversaries can exploit predictable vulnerabilities to their advantage. Randomized security schedules, which randomly deploy security measures, overcome these limitations, but public perceptions of such schedules have not been examined. In this experiment, participants were asked to make a choice between attending a venue that employed a traditional (i.e., search everyone) or a random (i.e., a probability of being searched) security schedule. The absolute probability of detecting contraband was manipulated (i.e., 1/10, 1/4, 1/2) but equivalent between the two schedule types. In general, participants were indifferent to either security schedule, regardless of the probability of detection. The randomized schedule was deemed more convenient, but the traditional schedule was considered fairer and safer. There were no differences between traditional and random schedule in terms of perceived effectiveness or deterrence. Policy implications for the implementation and utilization of randomized schedules are discussed.     

Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data

The benchmark dose (BMD) approach has gained acceptance as a valuable risk assessment tool, but risk assessors still face significant challenges associated with selecting an appropriate BMD/BMDL estimate from the results of a set of acceptable dose‐response models. Current approaches do not explicitly address model uncertainty, and there is an existing need to more fully inform health risk assessors in this regard. In this study, a Bayesian model averaging (BMA) BMD estimation method taking model uncertainty into account is proposed as an alternative to current BMD estimation approaches for continuous data. Using the “hybrid” method proposed by Crump, two strategies of BMA, including both “maximum likelihood estimation based” and “Markov Chain Monte Carlo based” methods, are first applied as a demonstration to calculate model averaged BMD estimates from real continuous dose‐response data. The outcomes from the example data sets examined suggest that the BMA BMD estimates have higher reliability than the estimates from the individual models with highest posterior weight in terms of higher BMDL and smaller 90th percentile intervals. In addition, a simulation study is performed to evaluate the accuracy of the BMA BMD estimator. The results from the simulation study recommend that the BMA BMD estimates have smaller bias than the BMDs selected using other criteria. To further validate the BMA method, some technical issues, including the selection of models and the use of bootstrap methods for BMDL derivation, need further investigation over a more extensive, representative set of dose‐response data.     

State of the Art in Risk Analysis of Workforce Criticality Influencing Disaster Preparedness for Interdependent Systems

The objective of this article is to discuss a needed paradigm shift in disaster risk analysis to emphasize the role of the workforce in managing the recovery of interdependent infrastructure and economic systems. Much of the work that has been done on disaster risk analysis has focused primarily on preparedness and recovery strategies for disrupted infrastructure systems. The reliability of systems such as transportation, electric power, and telecommunications is crucial in sustaining business processes, supply chains, and regional livelihoods, as well as ensuring the availability of vital services in the aftermath of disasters. There has been a growing momentum in recognizing workforce criticality in the aftermath of disasters; nevertheless, significant gaps still remain in modeling, assessing, and managing workforce disruptions and their associated ripple effects to other interdependent systems. The workforce plays a pivotal role in ensuring that a disrupted region continues to function and subsequently recover from the adverse effects of disasters. With this in mind, this article presents a review of recent studies that have underscored the criticality of workforce sectors in formulating synergistic preparedness and recovery policies for interdependent infrastructure and regional economic systems.     

Anti-N-methyl-D-aspartate receptor antibody encephalitis: An important cause of encephalitis in young adults. A report of two cases

Encephalitis is a clinical syndrome which can include altered mental status, motor and sensory deficits, altered behavior including personality changes, speech and movement disorders and seizures. While the overall incidence of encephalitis is not known, it is common enough that most pediatric and adolescent medicine physicians will have seen at least one case. Peak times of risk include the newborn period and middle-to-late adolescence.¹ Dalmau J, Tüzün E, Wu HY, et al. Paraneoplastic anti-N-methyl-D-aspartate receptor encephalitis associated with ovarian teratoma. Ann Neurol. 2007 Jan;61(1):25–36. doi:10.1002/ana.21050. PMID: 17262855.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar] It is important for clinicians to have a working knowledge of the broad range of encephalitis etiologies: viral, post-viral, toxic, auto-immune, and paraneoplastic. We discuss two cases of encephalitis in young adult women with ovarian teratoma and production of anti-N-methyl-D-aspartate receptor (NMDAR) antibodies.     

Integrating self-care into clinical practice with trauma clients

ABSTRACT

Self-care is recognized as an important aspect of social work practice. Beginning in educational settings, developing social workers are encouraged to learn about and practice self-care. However, self-care is typically promoted through strategies and approaches outside of the practice context. Social workers are oftentimes encouraged to use individualized self-care plans, which often feature a variety of relaxation and secondary techniques; focus on self-awareness and self-reflection; and have proper use of supervision. Although these strategies are invaluable, they do little to directly benefit the social worker during direct practice or while in session with a client which is a time period when clinicians are particularly vulnerable to distress or burnout. The importance of real-time self-care strategies is particularly critical for clinicians, especially those who engage in trauma work, given the sensitivity of topics that clients may have endured or benefit from assistance in processing. Further, little information has been published on the use of self-care techniques during a session with a client or in a clinical context. The purpose of the present paper is to discuss self-care strategies that can be useful both outside of and within a clinical setting and present a model of such strategies that focus on integrating self-care in clinical practice particularly for those engaging in trauma work. The paper will conclude by discussing the relevancy of the developed model in practice.     

Acceptance of automation manufacturing technology in China: an examination of perceived norm and organizational efficacy

Abstract

With a shift to more automation technology, social acceptance of technology plays an important role in the manufacturing sector. To what extent this occurs, and affects the adoption of technology, has been less researched, but is important in deciding how such technology is introduced, and the nature of the shift from labour-intensive manufacturing to automation. This research applies the revised technology acceptance model (TAM) to examine the impact of social and individual antecedents on the acceptance of automation manufacturing technology. Survey data are collected from 258 Chinese manufacturers. Results suggest that perceived norms significantly affect organizational intention to use automation manufacturing technology both directly and via perceived usefulness; organizational efficacy explains the intention to use via mediating effect of perceived usefulness and perceived ease of use. This research is one of the first extending and applying TAM from individuals to organizations.     

Academic Entrepreneurship: Time for a Rethink?

Academic entrepreneurship, which refers to efforts undertaken by universities to promote commercialization on campus and in surrounding regions of the university, has changed dramatically in recent years. Two key consequences of this change are that more stakeholders have become involved in academic entrepreneurship and that universities have become more ‘strategic’ in their approach to this activity. The authors assert that the time is ripe to rethink academic entrepreneurship. Specifically, theoretical and empirical research on academic entrepreneurship needs to take account of these changes, so as to improve the rigour and relevance of future studies on this topic. We outline such a framework and provide examples of key research questions that need to be addressed to broaden understanding of academic entrepreneurship.