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Many US biomedical PhD programs receive more applications for admissions than they can accept each year, necessitating a selective admissions process. Typical selection criteria include standardized test scores, undergraduate grade point average, letters of recommendation, a resume and/or personal statement highlighting relevant research or professional experience, and feedback from interviews with training faculty. Admissions decisions are often founded on assumptions that these application components correlate with research success in graduate school, but these assumptions have not been rigorously tested. We sought to determine if any application components were predictive of student productivity measured by first-author student publications and time to degree completion. We collected productivity metrics for graduate students who entered the umbrella first-year biomedical PhD program at the University of North Carolina at Chapel Hill from 2008–2010 and analyzed components of their admissions applications. We found no correlations of test scores, grades, amount of previous research experience, or faculty interview ratings with high or low productivity among those applicants who were admitted and chose to matriculate at UNC. In contrast, ratings from recommendation letter writers were significantly stronger for students who published multiple first-author papers in graduate school than for those who published no first-author papers during the same timeframe. We conclude that the most commonly used standardized test (the general GRE) is a particularly ineffective predictive tool, but that qualitative assessments by previous mentors are more likely to identify students who will succeed in biomedical graduate research. Based on these results, we conclude that admissions committees should avoid over-reliance on any single component of the application and de-emphasize metrics that are minimally predictive of student productivity. We recommend continual tracking of desired training outcomes combined with retrospective analysis of admissions practices to guide both application requirements and holistic application review. [ABSTRACT FROM AUTHOR]

A common policy approach to reducing childhood obesity aims to shape the environment in which children spend most of their time: neighborhoods and schools. This paper uses richly detailed data on the body mass index (BMI) of all New York City public school students in grades K-8 to assess the potential for place-based approaches to reduce child obesity. We document variation in the prevalence of obesity across NYC public schools and census tracts, and then estimate the extent to which this variation can be explained by differences in individual-level predictors (such as race and household income). Both unadjusted and adjusted variability across neighborhoods and schools suggest place-based policies have the potential to meaningfully reduce child obesity, but under most realistic scenarios the improvement would be modest. [ABSTRACT FROM AUTHOR]