How did the latest increase in fees in England affect student enrolment and inequality?

This paper presents a first analysis of the increase of undergraduate tuition fees to £9,000 (€11.000) in English higher education in 2012. I use a semi-experimental research design to estimate the effect of the reforms, based on student enrolment data drawn from the Higher Education Statistics Agency (HESA). Taking into account possible anticipation effects of the fee increase, I find that enrolment declined by 15 % in the treated groups as a result of the tuition fee increase. This number is almost three times higher than what previous studies have found, and may represent a serious long term cost for the English economy. The decline in enrolments is particularly pronounced for students in older age groups and students from the service class and the middle class. No effect is visible for students from the working class, indicating that the reforms did not lead to a much-feared increase in class bias in higher education enrolment. The reforms also seem not to have exacerbated ethnic inequality in higher education, as all ethnic groups were negatively affected by the reforms. These results are consistent with earlier research in the United States and the United Kingdom, although they expand our understanding of student price responsiveness in other important ways. The paper argues that younger and older students face different costs and benefits. Older students may be less certain about their benefits, and therefore be more sensitive towards price increases. The strong decrease in mature learners may require a policy response, taking into account these differing incentives

Association between Interpersonal Trust, Reciprocity, and Depression in South Korea: A Prospective Analysis

Background: A growing body of empirical evidence indicates that low-level social capital is related to poor mental health outcomes. However, the prospective association between social capital and depression remains unclear, and no published studies have investigated the association with longitudinal data in East-Asian countries. Methods: We analyzed data from the ongoing Korean Welfare Panel Study to prospectively investigate association between social capital and depression. Social capital was measured at the individual level by two items specific to interpersonal trust and reciprocity. Depression was annually assessed as a dichotomous variable using the Center for Epidemiologic Studies Depression Scale. After excluding participants who had depression in 2006, logistic regression models were applied to estimate the association between each social capital indicator and new-onset depression developed in 2007 or long-term depression in both 2007 and 2008. We also examined the association in a subpopulation restricted to healthy participants after excluding individuals with any pre-existing disability, chronic disease, or poor self-rated health condition. Results: Compared to the high interpersonal trust group, the odds ratios of developing new-onset and long-term depression among the low interpersonal trust group were 1.22 (95% CI: 1.08∼1.38) and 1.23 (95% CI: 1.03∼1.50), respectively, and increased to 1.32 (95% CI: 1.10∼1.57) and 1.47 (95% CI: 1.05∼2.08) in the subpopulation analyses restricted to healthy individuals. Although the low and intermediate reciprocity group also had significantly higher odds of developing new-onset depression compared to the high reciprocity group, the effects were attenuated and statistically non-significant in the subpopulation analyses. Conclusion: Low interpersonal trust appears to be an independent risk factor for new-onset and long-term depression in South Korea

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing