What’s in a Name?

Numerous concerns have been raised about the sustainability of the biomedical research enterprise in the United States. Improving the postdoctoral training experience is seen as a priority in addressing these concerns, but even identifying who the postdocs are is made difficult by the multitude of different job titles they can carry. Here, we summarize the detrimental effects that current employment structures have on training, compensation and benefits for postdocs, and argue that academic research institutions should standardize the categorization and treatment of postdocs. We also present brief case studies of two institutions that have addressed these challenges and can provide models for other institutions attempting to enhance their postdoctoral workforces and improve the sustainability of the biomedical research enterprise

Exact Bayesian curve fitting and signal segmentation.

We consider regression models where the underlying functional relationship between the response and the explanatory variable is modeled as independent linear regressions on disjoint segments. We present an algorithm for perfect simulation from the posterior distribution of such a model, even allowing for an unknown number of segments and an unknown model order for the linear regressions within each segment. The algorithm is simple, can scale well to large data sets, and avoids the problem of diagnosing convergence that is present with Monte Carlo Markov Chain (MCMC) approaches to this problem. We demonstrate our algorithm on standard denoising problems, on a piecewise constant AR model, and on a speech segmentation problem

Developing a conceptual framework for an evaluation system for the NIAID HIV/AIDS clinical trials networks

Globally, health research organizations are called upon to re-examine their policies and practices to more efficiently and effectively address current scientific and social needs, as well as increasing public demands for accountability

The American Science Pipeline: Sustaining Innovation in a Time of Economic Crisis

Significant limitations have emerged in America's science training pipeline, including inaccessibility, inflexibility, financial limitations, and lack of diversity. We present three effective programs that collectively address these challenges. The programs are grounded in rigorous science and integrate through diverse disciplines across undergraduate, graduate, and postdoctoral students, and resonate with the broader community. We discuss these models in the context of current economic constraints on higher education and the urgent need for our institutions to recruit and retain diverse student populations and sustain the successful American record in scientific education and innovation

Engineering PhD Returners and Direct‐Pathway Students: Comparing Expectancy, Value, and Cost

BackgroundProfessionals who pursue a doctorate after significant post‐baccalaureate work experience, a group we refer to as returners, represent an important but understudied group of engineering doctoral students. Returners are well situated to leverage their applied work experiences in their advanced engineering training.Purpose/HypothesisWe drew on results from the Graduate Student Experiences and Motivations Survey to explore the dimensionality of our scales measuring value and cost constructs. We used these scales, as well as measures of student expectancy of success, to compare returners with direct‐pathway students.Design/MethodWe surveyed 179 returners and 297 direct‐pathway domestic engineering doctoral students. We first conducted Exploratory Factor Analysis on our cost and value measures. We then used both Ordinary Least Squares and Ordinal Regression Model analyses to assess the relationships of various student characteristics and experiences (including returner status) with student expectancy of success and the emergent cost and values factors associated with doctoral study in engineering.ResultsFactor analysis revealed three categories of values (interest, attainment, and career utility) that were largely consistent with those in Eccles’ expectancy‐value framework. A similar analysis identified three categories of costs (balance, financial, and academic) associated with pursuing a PhD. Returners felt significantly less confident in their ability to complete their degrees prior to enrolling and perceived higher levels of all cost types than direct‐pathway students.ConclusionsGiven the differences between returning and direct‐pathway students, it is important to consider how universities might best recruit and retain returners. Tracking returner status could be critical in better supporting these students.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140046/1/jee20182.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140046/2/jee20182_am.pd

A suggested framework and guidelines for learning GIS in interdisciplinary research

Interdisciplinary research with geographic information systems (GIS) can be rewarding as researchers from different disciplines have the opportunity to create something novel. GIS, though, is known to be difficult to use and learn. It is imperative for its successful use in projects that those who need to use GIS are able to learn it quickly and easily. To better support interdisciplinary research with GIS, it is necessary to understand what researchers with interdisciplinary experience wanted to use it for and how they learned it. The aim would be to advise geography educators on creating learning resources that could compliment or supplement existing learning approaches used by interdisciplinary researchers to improve the learning experience and uptake of GIS. This article explores the results from an online survey and interviews conducted between July 2014 and August 2015 with participants from the UK, the US and Europe on how interdisciplinary researchers learned GIS and which resources and platforms were utilised. Guidelines and a framework are presented, modifying the Technological Pedagogical and Content Knowledge framework, incorporating informal and context-based learning and GIS concepts from the Geographic Information Science and Technology Body of Knowledge. Findings show that interdisciplinary researchers want to use GIS to capture, analyse and visualise information; they largely use informal learning approaches (e.g. internet searches, watching a video, ask a more experienced person); and they predominantly use ArcGIS, QGIS and web GIS platforms. Future work suggests resources use contextually relevant learning activities and bear in mind nuances of disciplinary language

Setting priorities for EU healthcare workforce IT skills competence improvement

A major challenge for healthcare quality improvement is the lack of IT skills and knowledge of healthcare workforce as well as their ambivalent attitudes towards IT. This paper identifies and prioritises actions needed to improve the IT skills of healthcare workforce across the EU. 46 experts, representing different fields of expertise in healthcare and geolocations systematically list and scored actions that would improve IT skills among healthcare workforce. The Child Health and Nutrition Research Initiative methodology was used for research priority-setting. The participants evaluated the actions using the following criteria: feasibility, effectiveness, deliverability, and maximum impact on IT skills improvement. The leading priority actions were related to appropriate training, integrating eHealth in curricula, involving healthcare workforce in the eHealth solution development, improving awareness of eHealth and learning arrangement. As the different professionals’ needs are prioritised, healthcare workforce should be actively and continuously included in the development of eHealth solutions