Longitudinal Prevalence and Correlates of Elder Mistreatment Among Older Adults Receiving Home Visiting Nursing

The objectives of this study were to identify elder mistreatment (EM) prevalence among a cohort of older adults receiving visiting nurse care in their homes, determine EM subtypes, and identify factors associated with EM. EM data were collected by nurses during monthly home visits for up to 24 months. It took the nurses a mean of 10.5 visits to discern EM. Fifty-four (7.4%) of 724 patients were identified as mistreated, of which 33 had enough information to subtype the EM. Of these 33, 27 were victims of neglect, 16 of psychological abuse, and 10 of financial exploitation, and 17 suffered more than one type. Among the entire sample, 11 variables were positively correlated with EM presence. Nurses visiting older adults in their homes should be aware that their patients are, as a group, vulnerable to EM, and that the factors identified here may be specific markers of greater risk.     

A Bayesian nonparametric model for Taguchi's on-line quality monitoring procedure for attributes

A Bayesian nonparametric model for Taguchi's on-line quality monitoring procedure for attributes is introduced. The proposed model may accommodate the original single shift setting to the more realistic situation of gradual quality deterioration and allows the incorporation of an expert's opinion on the production process. Based on the number of inspections to be carried out until a defective item is found, the Bayesian operation for the distribution function that represents the increasing sequence of defective fractions during a cycle considering a mixture of Dirichlet processes as prior distribution is performed. Bayes estimates for relevant quantities are also obtained.     

Psychological stress in adoptive parents of special-needs children

This article focuses on the nature and extent of parental stress among adoptive parents of special-needs children. In-depth face-to-face interviews of adoptive parents of 35 children were conducted, on average, four months postplacement (but before adoption). One-year follow-up interviews were conducted with parents of 15 (43%) of these children. Qualitative and quantitative data were collected, including parenting stress scores. Results indicated higher than average levels of stress, particularly on subscales related to parent-child dysfunctional interactions and to raising a difficult child. Increased stress levels were associated with poorer family adjustment and with higher levels of child behavior problems. Stress levels remained mostly unchanged over the year. Responses to open-ended questions identified five stress categories: child characteristics, parent-child interactions, family cohesion, parental adjustment, and adoptions service issues. Practice and research implications are discussed.     

Evaluating Citizen Advisory Boards: The Importance of Theory and Participant-Based Criteria and Practical Implications

The role of risk communication and public participation in environmental and public policy decision making has significantly increased over the last 15 years and remains an important social policy issue. In spite of this emphasis, government officials and participants in the process continue to struggle with what makes for "good" public participation. This study used two frameworks--one theoretical and one participant-based-to evaluate two U.S Army Restoration Advisory Boards (RABs). The theoretical framework explores the extent to which the RABs facilitate Habermas's idealized conditions of speech as related to fairness. Not surprisingly, we found that the two RABs do not consistently foster the idealized aspects of fairness suggested by Habermas. The participant-based criteria were elicited through interviews with participants from the various stakeholder groups represented on the RAB, direct observation of RAB meetings, and a review of RAB-related documents. We found that participants' value outcomes (the results of participatory processes) and not just the process itself, which is the focus of the theoretical framework. We also found that participants in the various stakeholder groups had different perceptions of the goals of the participatory process, which were closely related to their notions of success. Our results illustrate both the complexity and importance of using multiple frameworks for evaluating participatory efforts and the need for more systematic evaluation.     

EXPOSURE TO MARITAL AND FAMILY THERAPY IN MEDICAL SCHOOL: A STUDENT PERSPECTIVE

Most marital and family therapists, both medically and nonmedically trained, regularly interact with physicians on a professional basis. Though some physicians receive specific training in marital and family therapy in their postgraduate residency programs, (notably those in psychiatry and family medicine), for many, the only academic exposure they receive to this field comes during a 6- to 10-week clinical clerkship in psychiatry during their third or fourth year of medical school. No study could be located which examined marital and family therapy training in undergraduate medical education. The purpose of this study was to gain a better understanding of the level of marital and family therapy training in medical school and to provide information concerning physicians who are not family therapists, and how much knowledge about the field they can be expected to have.     

A Framework for Linking Cybersecurity Metrics to the Modeling of Macroeconomic Interdependencies

Hierarchical decision making is a multidimensional process involving management of multiple objectives (with associated metrics and tradeoffs in terms of costs, benefits, and risks), which span various levels of a large-scale system. The nation is a hierarchical system as it consists multiple classes of decisionmakers and stakeholders ranging from national policymakers to operators of specific critical infrastructure subsystems. Critical infrastructures (e.g., transportation, telecommunications, power, banking, etc.) are highly complex and interconnected. These interconnections take the form of flows of information, shared security, and physical flows of commodities, among others. In recent years, economic and infrastructure sectors have become increasingly dependent on networked information systems for efficient operations and timely delivery of products and services. In order to ensure the stability, sustainability, and operability of our critical economic and infrastructure sectors, it is imperative to understand their inherent physical and economic linkages, in addition to their cyber interdependencies. An interdependency model based on a transformation of the Leontief input-output (I-O) model can be used for modeling: (1) the steady-state economic effects triggered by a consumption shift in a given sector (or set of sectors); and (2) the resulting ripple effects to other sectors. The inoperability metric is calculated for each sector; this is achieved by converting the economic impact (typically in monetary units) into a percentage value relative to the size of the sector. Disruptive events such as terrorist attacks, natural disasters, and large-scale accidents have historically shown cascading effects on both consumption and production. Hence, a dynamic model extension is necessary to demonstrate the interplay between combined demand and supply effects. The result is a foundational framework for modeling cybersecurity scenarios for the oil and gas sector. A hypothetical case study examines a cyber attack that causes a 5-week shortfall in the crude oil supply in the Gulf Coast area.     

Gender Pay Differentials in Portugal: Contributions to the Employment Policy Debate in the European Union

Since the implementation of the European Employment Strategy in the 1990s, the issue of gender gaps in the European Union labour markets has been granted a high profile. The Portuguese labour market has performed well on various indicators relating to gender equality, namely, participation and employment rates. Nevertheless, a persistent pay gap remains despite the recent evolution of the labour market, especially concerning the average education level of workers. This article investigates the main factors explaining the gender pay gap across two decades and the way those factors perform along time. We also discuss the means of closing the gap in the context of the European Employment Strategy, considering the lessons from other member states. We used wage decomposition techniques to analyse the relative importance of differences in the productive characteristics of workers, differences in the way men and women are distributed among jobs and the relative importance of discrimination practices. Our findings suggest that most of the pay gap is due to discrimination practices. Individual action by economic agents is insufficient to solve the persistent pay gap. Social partners must incorporate this issue within collective bargaining in order to construct an adequate strategy for reducing the gap, which can only be done by engaging men and women, employees and employers.     

Accuracy of Simulations for Stochastic Dynamic Models

This paper is concerned with accuracy properties of simulations of approximate solutions for stochastic dynamic models. Our analysis rests upon a continuity property of invariant distributions and a generalized law of large numbers. We then show that the statistics generated by any sufficiently good numerical approximation are arbitrarily close to the set of expected values of the model's invariant distributions. Also, under a contractivity condition on the dynamics, we establish error bounds. These results are of further interest for the comparative study of stationary solutions and the estimation of structural dynamic models.     

Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities

Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, (2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI-based applications, (3) What are the synergies between AI and risk science applications, and (4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI-based applications? This study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI-based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI-based applications.