Disability citizenship and independence through mobile technology? A study exploring adoption and use of a mobile technology platform

This article explores the use of a mobile technology platform as experienced by people with disability, their significant other and service providers. An interpretive qualitative study design was adopted involving observations and interviews. The data were analysed using the combined lenses of the social approach to disability and the PHAATE model which represents the factors for consideration in service design for assistive technology. The findings suggest that the adoption of the technology by those in the study could be characterised by a typology of users. The implications of the typology are discussed together with the influencing factors that affected social participation and disability citizenship.     

“First Train Out”: Marriage and Cohabitation in the Context of Poverty,Deprivation, and Trauma

There has been a steep rise in the proportion of children born to and living with unmarried parents. Unmarried parents are increasingly likely to cohabitate, especially low-income couples, placing their children at elevated psychosocial risk. This life history study of poor, White single mothers suggests that the current focus on differences between married and cohabiting poor women may overstate underlying similarities in factors associated with their partner formation and dissolution and that poor women's decisions about marriage and cohabitation must be understood in a developmental context that reflects the stacking, over time, of multiple forms of vulnerability to unstable partnerships, single motherhood, and continuing poverty into adulthood.     

Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber‐Physical Attacks on Electricity Distribution Infrastructure Networks

In December 2015, a cyber‐physical attack took place on the Ukrainian electricity distribution network. This is regarded as one of the first cyber‐physical attacks on electricity infrastructure to have led to a substantial power outage and is illustrative of the increasing vulnerability of Critical National Infrastructure to this type of malicious activity. Few data points, coupled with the rapid emergence of cyber phenomena, has held back the development of resilience analytics of cyber‐physical attacks, relative to many other threats. We propose to overcome data limitations by applying stochastic counterfactual risk analysis as part of a new vulnerability assessment framework. The method is developed in the context of the direct and indirect socioeconomic impacts of a Ukrainian‐style cyber‐physical attack taking place on the electricity distribution network serving London and its surrounding regions. A key finding is that if decision‐makers wish to mitigate major population disruptions, then they must invest resources more‐or‐less equally across all substations, to prevent the scaling of a cyber‐physical attack. However, there are some substations associated with higher economic value due to their support of other Critical National Infrastructures assets, which justifies the allocation of additional cyber security investment to reduce the chance of cascading failure. Further cyber‐physical vulnerability research must address the tradeoffs inherent in a system made up of multiple institutions with different strategic risk mitigation objectives and metrics of value, such as governments, infrastructure operators, and commercial consumers of infrastructure services.     

Women in prison: A comparative assessment

This article examines women prisoners as a percent of the prison populations of twentysix countries. In this study, we hypothesize that in countries where women have greater social, educational, and economic advancements, women will also comprise the larger percentages of the total prison population. Findings from this study indicate that the countries where women constitute the highest percentages of the prison population are also those where women have high literacy rates. The percent of certain crimes committed by women correlates significantly and positively with educational and economic indicators, as well as measures of women's advancements in the workforce. Negative and significant correlations are seen between fertility rates and the number of women as a percent of the prison population, as well as educational variables. Ultimately, the findings indicate that in countries where women have advanced the greatest with regards to education, labor, and social status, the percent of women in the prison population and of all offenders for certain crime categories is also higher.     

Bias modelling in evidence synthesis

Summary.  Policy decisions often require synthesis of evidence from multiple sources, and the source studies typically vary in rigour and in relevance to the target question. We present simple methods of allowing for differences in rigour (or lack of internal bias) and relevance (or lack of external bias) in evidence synthesis. The methods are developed in the context of reanalysing a UK National Institute for Clinical Excellence technology appraisal in antenatal care, which includes eight comparative studies. Many were historically controlled, only one was a randomized trial and doses, populations and outcomes varied between studies and differed from the target UK setting. Using elicited opinion, we construct prior distributions to represent the biases in each study and perform a bias-adjusted meta-analysis. Adjustment had the effect of shifting the combined estimate away from the null by approximately 10%, and the variance of the combined estimate was almost tripled. Our generic bias modelling approach allows decisions to be based on all available evidence, with less rigorous or less relevant studies downweighted by using computationally simple methods.     

Accounting for uncertainty in health economic decision models by using model averaging

Summary.  Health economic decision models are subject to considerable uncertainty, much of which arises from choices between several plausible model structures, e.g. choices of covariates in a regression model. Such structural uncertainty is rarely accounted for formally in decision models but can be addressed by model averaging. We discuss the most common methods of averaging models and the principles underlying them. We apply them to a comparison of two surgical techniques for repairing abdominal aortic aneurysms. In model averaging, competing models are usually either weighted by using an asymptotically consistent model assessment criterion, such as the Bayesian information criterion, or a measure of predictive ability, such as Akaike's information criterion. We argue that the predictive approach is more suitable when modelling the complex underlying processes of interest in health economics, such as individual disease progression and response to treatment.     

Modeling Event Times with Multiple Outcomes Using the Wiener Process with Drift

Length of stay in hospital (LOS) is a widely used outcome measure in Health Services research, often acting as a surrogate for resource consumption or as a measure of efficiency. The distribution of LOS is typically highly skewed, with a few large observations. An interesting feature is the presence of multiple outcomes (e.g. healthy discharge, death in hospital, transfer to another institution). Health Services researchers are interested in modeling the dependence of LOS on covariates, often using administrative data collected for other purposes, such as calculating fees for doctors. Even after all available covariates have been included in the model, unexplained heterogeneity usually remains. In this article, we develop a parametric regression model for LOS that addresses these features. The model is based on the time, T, that a Wiener process with drift (representing an unobserved health level process) hits one of two barriers, one representing healthy discharge and the other death in hospital. Our approach to analyzing event times has many parallels with competing risks analysis (Kalbfleisch and Prentice, The Statistical Analysis of Failure Time Data, New York: John Wiley and Sons, 1980)), and can be seen as a way of formalizing a competing risks situation. The density of T is an infinite series, and we outline a proof that the density and its derivatives are absolutely and uniformly convergent, and regularity conditions are satisfied. Expressions for the expected value of T, the conditional expectation of T given outcome, and the probability of each outcome are available in terms of model parameters. The proposed regression model uses an approximation to the density formed by truncating the series, and its parameters are estimated by maximum likelihood. An extension to allow a third outcome (e.g. transfers out of hospital) is discussed, as well as a mixture model that addresses the issue of unexplained heterogeneity. The model is illustrated using administrative data.     

Facets of dispositional mindfulness versus sources of social support predicting college students’ psychological adjustment

Abstract

Objective: This study examined the relative contribution of five dispositional mindfulness (DM) facets and two aspects of social support along with sex in predicting psychological adjustment. Participants: Three hundred fifty-three undergraduates (72% female; M?=?18.82?years) participated with data collected from September 2014 through March 2016. Methods: Self-report measures of DM, social support, perceived stress, and emotional well-being were completed. Results: Sex and higher scores on specific mindfulness facets (ie, nonreactivity, nonjudging) predicted lower stress and greater emotional well-being. Higher family support predicted lower stress, whereas friend support predicted greater emotional well-being. The mindfulness facets were stronger predictors of adjustment than the social support domains. Females reported higher perceived stress and lower emotional well-being than males, and males scored significantly higher on total mindfulness, nonjudging, and nonreactivity. Conclusions: Results have implications for mindfulness-based interventions with college students such that focusing on the nonjudging and nonreactivity facets may enhance effectiveness.