A hazard model of the probability of medical school drop-out in the UK

Summary.  From individual level longitudinal data for two entire cohorts of medical students in UK universities, we use multilevel models to analyse the probability that an individual student will drop out of medical school. We find that academic preparedness—both in terms of previous subjects studied and levels of attainment therein—is the major influence on withdrawal by medical students. Additionally, males and more mature students are more likely to withdraw than females or younger students respectively. We find evidence that the factors influencing the decision to transfer course differ from those affecting the decision to drop out for other reasons.     

Higher education outcomes, graduate employment and university performance indicators

Summary.  Official employment-related performance indicators in UK higher education are based on the population of students responding to the 'First destination supplement' (FDS). This generates potentially biased performance indicators as this population of students is not necessarily representative of the full population of leavers from each institution. University leavers who do not obtain qualifications and those who do not respond to the FDS are not included within the official analysis. We compare an employment-related performance indicator based on those students who responded to the FDS with alternative approaches which address the potential non-random nature of this subgroup of university leavers.     

Semiparametric Stochastic Modeling of the Rate Function in Longitudinal Studies

In longitudinal biomedical studies, there is often interest in the rate functions, which describe the functional rates of change of biomarker profiles. This paper proposes a semiparametric approach to model these functions as the realizations of stochastic processes defined by stochastic differential equations. These processes are dependent on the covariates of interest and vary around a specified parametric function. An efficient Markov chain Monte Carlo algorithm is developed for inference. The proposed method is compared with several existing methods in terms of goodness-of-fit and more importantly the ability to forecast future functional data in a simulation study. The proposed methodology is applied to prostate-specific antigen profiles for illustration. Supplementary materials for this paper are available online.     

Resilience of Cyber Systems with Over‐ and Underregulation

Recent cyber attacks provide evidence of increased threats to our critical systems and infrastructure. A common reaction to a new threat is to harden the system by adding new rules and regulations. As federal and state governments request new procedures to follow, each of their organizations implements their own cyber defense strategies. This unintentionally increases time and effort that employees spend on training and policy implementation and decreases the time and latitude to perform critical job functions, thus raising overall levels of stress. People's performance under stress, coupled with an overabundance of information, results in even more vulnerabilities for adversaries to exploit. In this article, we embed a simple regulatory model that accounts for cybersecurity human factors and an organization's regulatory environment in a model of a corporate cyber network under attack. The resulting model demonstrates the effect of under‐ and overregulation on an organization's resilience with respect to insider threats. Currently, there is a tendency to use ad‐hoc approaches to account for human factors rather than to incorporate them into cyber resilience modeling. It is clear that using a systematic approach utilizing behavioral science, which already exists in cyber resilience assessment, would provide a more holistic view for decisionmakers.     

A Test of Basic Assumptions of Affective Events Theory (AET) in Call Centre Work1

Based on data from 2091 call centre representatives working in 85 call centres in the UK, central assumptions of affective events theory (AET) are tested. AET predicts that specific features of work (e.g. autonomy) have an impact on the arousal of emotions and moods at work that, in turn, co‐determine job satisfaction of employees. AET further proposes that job satisfaction is an evaluative judgement that mainly explains cognitive‐based behaviour, whereas emotions and moods better predict affective‐based behaviour. The results support these assumptions. A clear separation of key constructs (job satisfaction, positive and negative emotions) was possible. Moreover, correlations between several work features (e.g. supervisory support) and job satisfaction were, in part, mediated by work emotions, even when controlling for gender, age, call centre type (in‐house versus outsourced centres) and call centre size. Predictions regarding consequences of satisfaction and affect were partly corroborated as continuance commitment was more strongly related to job satisfaction than to positive emotions. In addition, affective commitment and health complaints were related to both emotions and job satisfaction to the same extent. Thus, AET is a fruitful framework for explaining why and how specific management strategies used for designing work features influence important organizational attitudes and well‐being of employees.     

Comparison of conflict of interest policies and reported practices in academic medical centers in the United States

The authors reviewed the conflict of interest policies of 9 academic medical centers in the United States and interviewed members of the Institutional Review Boards (IRBs) and Conflict of Interest Committees (COICs) at those institutions. They found that many institutions used processes for reporting and managing conflicts of interest that were more decentralized than the processes described in their policies. Also, most institutions had no clear and comprehensive policy to guide investigators regarding disclosure of conflicts of interest to potential research participants. Considerable differences in understanding of conflict of interest policies were observed between IRB and COIC officials.     

Urban politics and the study of urban poverty: Promising developments and future directions

The study of urban poverty is alive and well in sociology. The study of urban politics, by contrast, has stagnated. Though scholars agree that politics shapes the creation and durability of urban poverty, analytical connections between the two subfields are rarely made explicit. In this article, I make the case for a more integrated body of research. I first illustrate how urban poverty scholars implicitly discuss politics, and conversely, how urban politics scholars implicitly discuss poverty. I then highlight recent developments in the literature and propose two paths forward—by no means the only paths forward, but two ways to jumpstart greater conversation across both subfields. For the urban poverty literature, a focus on organizations can help scholars analyze political dynamics more directly. And for the urban politics literature, an emphasis on political mechanisms rather than overarching perspectives can disrupt the current theoretical malaise. These two moves can advance both literatures while drawing them closer together.     

Disconnected Geography: A Spatial Analysis of Disconnected Youth in the United States

Since the Great Recession, US policy and advocacy groups have sought to better understand its effect on a group of especially vulnerable young adults who are not enrolled in school or training programs and not participating in the labor market, so called ‘disconnected youth.’ This article distinguishes between disconnected youth and unemployed youth and examines the spatial clustering of these two groups across counties in the US. The focus is to ascertain whether there are differences in underlying contextual factors among groups of counties that are mutually exclusive and spatially disparate (non-adjacent), comprising two types of spatial clusters – high rates of disconnected youth and high rates of unemployed youth. Using restricted, household-level census data inside the Census Research Data Center (RDC) under special permission by the US Census Bureau, we were able to define these two groups using detailed household questionnaires that are not available to researchers outside the RDC. The geospatial patterns in the two types of clusters suggest that places with high concentrations of disconnected youth are distinctly different in terms of underlying characteristics from places with high concentrations of unemployed youth. These differences include, among other things, arrests for synthetic drug production, enclaves of poor in rural areas, persistent poverty in areas, educational attainment in the populace, children in poverty, persons without health insurance, the social capital index, and elders who receive disability benefits. This article provides some preliminary evidence regarding the social forces underlying the two types of observed geospatial clusters and discusses how they differ.     

Editorial

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations -