The Higher Education White Paper: The Good,the Bad,the Unspeakable – and the Next White Paper1

This article argues that reforms of higher education finance for undergraduates in England introduced by the Blair government in 2006 provided a progressive strategy for achieving the central objectives of higher education of quality (better), access (wider) and size (larger). Reforms in 2012 are a not a strategy but a collection of ad hoc arrangements. They include the good (a higher fees cap, a higher interest rate on student loans, better information and improved support for part‐time study), the bad (abolishing most taxpayer support for teaching in the arts and humanities and the social sciences, and raising excessively the threshold at which loan repayments start) and the unspeakable (abolishing Education Maintenance Allowances and AimHigher). The reforms are fiscally costly and hence perpetuate the central problem of capped student numbers, and will not stand the test of time. The concluding section outlines the next White Paper.     

Design and analysis considerations for comparing dynamic treatment regimens with binary outcomes from sequential multiple assignment randomized trials

In behavioral, educational and medical practice, interventions are often personalized over time using strategies that are based on individual behaviors and characteristics and changes in symptoms, severity, or adherence that are a result of one's treatment. Such strategies that more closely mimic real practice, are known as dynamic treatment regimens (DTRs). A sequential multiple assignment randomized trial (SMART) is a multi-stage trial design that can be used to construct effective DTRs. This article reviews a simple to use ‘weighted and replicated’ estimation technique for comparing DTRs embedded in a SMART design using logistic regression for a binary, end-of-study outcome variable. Based on a Wald test that compares two embedded DTRs of interest from the ‘weighted and replicated’ regression model, a sample size calculation is presented with a corresponding user-friendly applet to aid in the process of designing a SMART. The analytic models and sample size calculations are presented for three of the more commonly used two-stage SMART designs. Simulations for the sample size calculation show the empirical power reaches expected levels. A data analysis example with corresponding code is presented in the appendix using data from a SMART developing an effective DTR in autism.     

Extrapolative simulation of neighborhood networks based on population spatial distribution: Do they predict crime?

Objectives

Previous criminological scholarship has posited that network ties among neighborhood residents may impact crime rates, but has done little to consider the specific ways in which network structure may enhance or inhibit criminal activity. A lack of data on social ties has arguably led to this state of affairs. We propose to avoid this limitation by demonstrating a novel approach of extrapolatively simulating network ties and constructing structural network measures to assess their effect on neighborhood crime rates.

Methods

We first spatially locate the households of a city into their constituent blocks. Then, we employ spatial interaction functions based on prior empirical work and simulate a network of social ties among these residents. From this simulated network, we compute network statistics that more appropriately capture the notions of cohesion and information diffusion that underlie theories of networks and crime.

Results

We show that these network statistics are robust predictors of the levels of crime in five separate cities (above standard controls) at the very micro geographic level of blocks and block groups.

Conclusions

We conclude by considering extensions of the approach that account for homophily in the formation of network ties.     

Real-life closeness of social media contacts and depressive symptoms among university students

ABSTRACT

Objective: To examine the association between degree of real-life closeness of social media (SM) contacts and depressive symptoms. Participants: Students ages 18–30 (N = 1124) were recruited in August 2016. Methods: Participants completed an online survey assessing SM use and depression. We used multivariable logistic regression to assess associations between real-life closeness of SM contacts and depressive symptoms. Results: After controlling for covariates, each 10% increase in the proportion of SM friends with whom participants had no face-to-face relationship was associated with a 9% increase in odds of depressive symptoms (AOR = 1.09; 95% CI = 1.05–1.13). However, each 10% increase in the proportion of SM friends with whom participants had a close face-to-face relationship was associated with a 7% decrease in depressive symptoms (AOR = 0.93; 95% CI = 0.89–0.97). Conclusions: Having no in-person relationship with SM contacts is associated with increased depressive symptoms; however, having close in-person relationships with SM contacts is associated with decreased depressive symptoms.     

A Further Opinion on Reimagining Nursing Homes

A commentary of the article discussing the possible reimagining of long-term care (LTC) is provided. The reimagining article examines how to diminish the role of nursing homes in the LTC system. The text is provocative. It is also thought-provoking. In this commentary I provide a further opinion that we could do better within the current system using the current resources.     

Staffing-Related Deficiency Citations in Nursing Homes

There is evidence that staffing characteristics influence quality of care in nursing homes. Federal and state surveyors conduct inspections of homes to assess their compliance with regulatory standards, including requirements related to staffing. Deficiency citations are issued when these standards are not met. This article examines the relationship between operational, facility, and market characteristics and organizational performance measured as staffing-related deficiency citations. Online Survey Certification of Automated Records (OSCAR) data from 2000 through 2007 were used with multinomial logistic regression analyses to identify factors associated with deficiency citations for staffing. Chain members and facilities with poor quality of care were more likely to receive deficiency citations for staffing. Greater bed count and competition between nursing homes were associated with a decreased likelihood of deficiency citations for staffing. Staffing-related deficiencies within nursing homes vary according to various operational, facility, and market characteristics.