In-Sample Inference and Forecasting in Misspecified Factor Models

This article considers in-sample prediction and out-of-sample forecasting in regressions with many exogenous predictors. We consider four dimension-reduction devices: principal components, ridge, Landweber Fridman, and partial least squares. We derive rates of convergence for two representative models: an ill-posed model and an approximate factor model. The theory is developed for a large cross-section and a large time-series. As all these methods depend on a tuning parameter to be selected, we also propose data-driven selection methods based on cross-validation and establish their optimality. Monte Carlo simulations and an empirical application to forecasting inflation and output growth in the U.S. show that data-reduction methods outperform conventional methods in several relevant settings, and might effectively guard against instabilities in predictors’ forecasting ability.     

THE ACADEMIC FLOW MODEL: A MARKOV-CHAIN MODEL FOR FACULTY PLANNING*

A Markov-chain faculty planning model to be used with institution-specific data is presented to describe and better understand the complex phenomena of faculty movement through an institution and on its relationship to salary costs, composition of the faculty, and faculty turnover rate. The model updates the earlier work done at Stanford University and Oregon State University by the addition of states for fixed-term appointments and for part-time FTEs to reflect accurately the current hiring trends at many institutions. The model is implemented and tested at two different institutions. The findings suggest that the model is a viable means of gaining useful insights and quantitative data on the faculty profile, salary costs, expected departures, and part-time trends. And further, when used as a planning tool, and the model apparently is comprehensive and flexible enough to analyze the probable effects, both in the short and long run, of alternative personnel policies on the faculty composition.     

The Mediating Effect of Resilience Between Social Support for Exercise and Resident Satisfaction in Assisted Living

This study examined whether residents' level of resilience mediated the relationship between social support for exercise from staff and resident satisfaction in assisted living. This was a secondary data analysis using baseline data from a function-focused care intervention study including 171 residents from four assisted living facilities. Using structural equation modeling, we found that mood and social support for exercise from staff were the only variables associated with resilience. Mood, gender, cognition, and social support for exercise from staff directly influenced resident satisfaction and explained 31% of the variance in the model. Implications for future research and practice are discussed.     

Open Dialogue: Frequently Asked Questions

Open Dialogue is an approach to working with people and their families experiencing psychosocial distress. Interest in Open Dialogue in Australia has been growing recently, raising questions about its adaption and implementation to local contexts. This article is an attempt to answer some of the frequently asked questions we have encountered in training and discussions about Open Dialogue. We attempt to provide responses to questions of how Open Dialogue is different to what is done already, how it fits with current approaches, how you know if you are doing it, whether it is passive or just about doing reflections, issues about including the social network, and concerns about the evidence base. This article aims to present a variety of viewpoints in relation to these questions and to hopefully further discussions on how Open Dialogue can be implemented and adapted to Australian health care and social care contexts.     

Albinism,spiritual and cultural practices,and implications for health,healthcare, and human rights: a scoping review

Persons with albinism – a disability found worldwide – are particularly at risk for human rights violations in Africa. The purpose of this scoping review was to establish the current state of knowledge on albinism, spiritual and cultural practices, and implications for health and health services. Electronic searches of nine databases were conducted. After screening, 40 articles were reviewed. Four themes emerged: health implications of albinism; health-related cultural and spiritual meanings attached to albinism; health-related experiences of persons with albinism; and human rights and albinism. The findings reveal that African ontologies, including witchcraft beliefs and practices, are implicated by their construction of the person with albinism as an ontologically different entity, although few studies explicated this relationship. How broader social structures influence persons with albinism disproportionately, resulting in health and social inequities, is another under-researched area. This scoping review lays the groundwork for intersectoral, interdisciplinary research.     

Data Science: A Powerful Catalyst for Cross-Sector Collaborations to Transform the Future of Global Health—Developing a New Interactive Relational Mapping Tool (Demo)

ABSTRACT

The increasingly complex and rapidly changing global health and socioeconomic landscape requires fundamentally new ways of thinking, acting, and collaborating to solve growing systems challenges. Cross-sectoral collaborations between governments, businesses, international organizations, private investors, academia, and nonprofits are essential for lasting success in achieving the Sustainable Development Goals (SDGs), and securing a prosperous future for the health and well-being of all people (United Nations, n.d United Nations. (n.d.). SDGs: Sustainable development knowledge platform. Sustainable Development United Nations. Retrieved from https://sustainabledevelopment.un.org/ [Google Scholar].). Our aim is to use data science and innovative technologies to map diverse stakeholders and their initiatives around SDGs and specific health targets—with particular focus on SDG 3 (Good Health & Well Being) and SDG 17 (Partnerships for the Goals)—to accelerate cross-sector and multidisciplinary collaborations. Initially, the mapping tool focuses on Geneva, Switzerland as the world center of global health diplomacy with over 80 key stakeholders and influencers present. As we develop the next level pilot, we aim to build on users’ interests, with a potential focus on non-communicable diseases (NCDs) as one of the emerging and most pressing global health issues that requires new collaborative approaches. Building on this pilot, we can later expand beyond only SDG 3 to other SDGs.