Oral health among student veterans: Effects on mental and physical health

Abstract

Objective: The purpose of this study was to explore differences in oral health among students by military service status in postsecondary settings. Participants: Secondary data were obtained from the American College Health Association’s 2011–2014 National College Health Assessment II. Method: Demographic characteristics of the study sample were explored by calculating frequencies and percentages by military service status. Research questions were explored with maximum likelihood multiple logistic regression. Results: Service member and veteran students are at greater risk for neglecting health behaviors associated with positive oral health, including that they were less likely to get dental exams and that those who deployed in the past were the least likely to be practicing good oral health. Conclusions: The results suggest that improving oral health will contribute to improving health and mental health outcomes in this population, and provide important information for health specialists working with service members and veterans on college campuses.     

Toward General Principles for Resilience Engineering

Maintaining the performance of infrastructure-dependent systems in the face of surprises and unknowable risks is a grand challenge. Addressing this issue requires a better understanding of enabling conditions or principles that promote system resilience in a universal way. In this study, a set of such principles is interpreted as a group of interrelated conditions or organizational qualities that, taken together, engender system resilience. The field of resilience engineering identifies basic system or organizational qualities (e.g., abilities for learning) that are associated with enhanced general resilience and has packaged them into a set of principles that should be fostered. However, supporting conditions that give rise to such first-order system qualities remain elusive in the field. An integrative understanding of how such conditions co-occur and fit together to bring about resilience, therefore, has been less clear. This article contributes to addressing this gap by identifying a potentially more comprehensive set of principles for building general resilience in infrastructure-dependent systems. In approaching this aim, we organize scattered notions from across the literature. To reflect the partly self-organizing nature of infrastructure-dependent systems, we compare and synthesize two lines of research on resilience: resilience engineering and social-ecological system resilience. Although some of the principles discussed within the two fields overlap, there are some nuanced differences. By comparing and synthesizing the knowledge developed in them, we recommend an updated set of resilience-enhancing principles for infrastructure-dependent systems. In addition to proposing an expanded list of principles, we illustrate how these principles can co-occur and their interdependencies.     

People,Pipelines, and Probabilities: Clarifying Significance and Uncertainty in Environmental Impact Assessments

Determinations of significance play a pivotal role in environmental impact assessments because they point decision makers to the predicted effects of an action most deserving of attention and further study. Impact predictions are always subject to uncertainty because they rely on estimates of future consequences. Yet uncertainty is often neglected or treated in a perfunctory manner as part of the characterization, evaluation, and communication of anticipated consequences and their significance. Proposals to construct fossil fuel pipelines in North America provide a highly visible example; casual treatment of how uncertainty affects significance determinations has resulted in poorly informed stakeholders, frustrated industry proponents, and inconsistent choices on the part of public decision makers. Using environmental assessments for recent pipeline proposals as examples, we highlight five ways in which uncertainty is often neglected when determining impact significance and suggest that a mix of known methods, new guidelines, and appropriate oversight could greatly improve current practices.     

Communicating with the Public About Marauding Terrorist Firearms Attacks: Results from a Survey Experiment on Factors Influencing Intention to “Run,Hide, Tell” in the United Kingdom and Denmark

Effective risk communication is an integral part of responding to terrorism, but until recently, there has been very little pre‐event communication in a European context to provide advice to the public on how to protect themselves during an attack. Following terrorist attacks involving mass shootings in Paris, France, in November 2015, the U.K. National Police Chiefs’ Council released a Stay Safe film and leaflet that advises the public to “run,” “hide,” and “tell” in the event of a firearms or weapons attack. However, other countries, including Denmark, do not provide preparedness information of this kind, in large part because of concern about scaring the public. In this survey experiment, 3,003 U.K. and Danish participants were randomly assigned to one of three conditions: no information, a leaflet intervention, and a film intervention to examine the impact of “Run, Hide, Tell” advice on perceptions about terrorism, the security services, and intended responses to a hypothetical terrorist firearms attack. Results demonstrate important benefits of pre‐event communication in relation to enhancing trust, encouraging protective health behaviors, and discouraging potentially dangerous actions. However, these findings also suggest that future communications should address perceived response costs and target specific problem behaviors. Cross‐national similarities in response suggest this advice is suitable for adaptation in other countries.     

Minimum spectral connectivity projection pursuit

Statistics and Computing - We study the problem of determining the optimal low-dimensional projection for maximising the separability of a binary partition of an unlabelled dataset, as measured by...     

Likelihood-free approximate Gibbs sampling

Likelihood-free methods such as approximate Bayesian computation (ABC) have extended the reach of statistical inference to problems with computationally intractable likelihoods. Such approaches perform well for small-to-moderate dimensional problems, but suffer a curse of dimensionality in the number of model parameters. We introduce a likelihood-free approximate Gibbs sampler that naturally circumvents the dimensionality issue by focusing on lower-dimensional conditional distributions. These distributions are estimated by flexible regression models either before the sampler is run, or adaptively during sampler implementation. As a result, and in comparison to Metropolis-Hastings-based approaches, we are able to fit substantially more challenging statistical models than would otherwise be possible. We demonstrate the sampler’s performance via two simulated examples, and a real analysis of Airbnb rental prices using a intractable high-dimensional multivariate nonlinear state-space model with a 36-dimensional latent state observed on 365 time points, which presents a real challenge to standard ABC techniques.     

Detecting Directionality in Time Series

Abstract

Directionality can be seen in many stationary time series from various disciplines, but it is overlooked when fitting linear models with Gaussian errors. Moreover, we cannot rely on distinguishing directionality by comparing a plot of a time series in time order with a plot in reverse time order. In general, a statistical measure is required to detect and quantify directionality. There are several quite different qualitative forms of directionality, and we distinguish: rapid rises followed by slow recessions; rapid increases and rapid decreases from the mean followed by slow recovery toward the mean; directionality above or below some threshold; and intermittent directionality. The first objective is to develop a suite of statistical measures that will detect directionality and help classify its nature. The second objective is to demonstrate the potential benefits of detecting directionality. We consider applications from business, environmental science, finance, and medicine. Time series data are collected from many processes, both natural and anthropogenic, by a wide range of organizations, and directionality can easily be monitored as part of routine analysis. We suggest that doing so may provide new insights to the processes.     

HIGH SCHOOL CHOICES AND THE GENDER GAP IN STEM

Women are less likely than men to graduate with a degree in science, technology, engineering, or math (STEM). We use detailed administrative data for a recent cohort of Ontario high school students, combined with data from the province's university admission system, to analyze the sources of this gap. We show that entry to STEM programs is mediated through an index of STEM readiness that depends on end‐of‐high school courses in math and science. Most of the gender gap in STEM entry can be traced to differences in the share of college entrants who are STEM‐ready; only a small share is attributable to differences in the choice of major conditional on readiness. We then use high school course data to decompose the gender gap in STEM readiness into two channels: one reflecting the gap in the fraction of high school students with the necessary prerequisites to enter STEM, the other arising from differences in the overall fractions of females and males who enter university. The gender gap in the fraction of males and females with STEM prerequisites is small. The primary driver of the gap in STEM readiness is the low rate of university entry by nonscience‐oriented males. (JEL I21, 28, I20)