Predicting the Counterproductive Employee in a Child-to-Adult Prospective Study

Abstract The present research tested the relations between a battery of background factors and counterproductive work behaviors in a 23-year longitudinal study of young adults (N = 930). Background information, such as diagnosed adolescent conduct disorder, criminal conviction records, intelligence, and personality traits, was assessed before participants entered the labor force. These background factors were combined with work conditions at age 26 to predict counterproductive work behaviors at age 26. The results showed that people diagnosed with childhood conduct disorder were more prone to commit counterproductive work behaviors in young adulthood and that these associations were partially mediated by personality traits measured at age 18. Contrary to expectations, criminal convictions that occurred prior to entering the workforce were unrelated to counterproductive work behaviors. Job conditions and personality traits had independent effects on counterproductive work behaviors, above and beyond background factors

Perspectives of Dermatology Program Directors on the Impact of Step 1 Pass/Fail.

INTRODUCTION: The shift of Step 1 to Pass/Fail has generated several questions and concerns about obtaining residency positions among allopathic and osteopathic students alike. Determining the perspectives of Dermatology Program Directors in regards to post-Step 1 Pass/Fail is critical for students to better prepare for matching into dermatology. METHODS: After receiving Institutional Review Board (IRB) exemption status, the program directors were chosen from 144 Accreditation Council for Graduate Medical Education (ACGME) and 27 American Osteopathic Association (AOA) Dermatology programs using contact information from their respective online website databases. An eight-item survey was constructed on a three-point Likert scale, one free text response, and four demographic questions. The anonymous survey was sent out over the course of three weeks with weekly individualized reminder requests for participation. RESULTS: A total of 54.54% of responders had Letters of Recommendation in their top 3. Forty-five percent of responders had Completed Audition Rotation at Program in their top 3. And, 38.09% of responders had USMLE Step 2 CK Scores in their top 3. CONCLUSION: Approximately 50% of responders agreed that all medical students will have more difficulty matching dermatology. Based on the survey study, Dermatology program directors want to focus more on letters of recommendation, audition rotations, and Step 2 CK scores. Because each field seems to prioritize different aspects of an application, students should attempt to gain as much exposure to different fields such as through research and shadowing to narrow down their ideal specialties. Consequently, the student will have more time to tailor their applications to what residency admissions are looking for

Recent Decisions

Commentaries on recent decisions by Robert W. Cox, Peter O. Kelly, Louis N. Roberts, James K. Stucko, Thomas J. Kelly, Joseph P. Albright, Daniel J. Manelli, and James E. Gould

Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease

The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Quantitative electroencephalography (QEEG) measures have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to facilitate the objectivization of AD assessment. Here, we present a novel framework combining Riemannian tangent space mapping and elastic net regression for the development of brain atrophy markers. While most AD QEEG studies are based on small sample sizes and psychological test scores as outcome measures, here we train and test our models using data of one of the largest prospective EEG AD trials ever conducted, including MRI biomarkers of brain atrophy.Comment: Presented at NIPS 2017 Workshop on Machine Learning for Healt

The politics of accelerating low-carbon transitions: towards a new research agenda

Meeting the climate change targets in the Paris Agreement implies a substantial and rapid acceleration of low-carbon transitions. Combining insights from political science, policy analysis and socio-technical transition studies, this paper addresses the politics of deliberate acceleration by taking stock of emerging examples, mobilizing relevant theoretical approaches, and articulating a new research agenda. Going beyond routine appeals for more ‘political will’, it organises ideas and examples under three themes: 1) the role of coalitions in supporting and hindering acceleration; 2) the role of feedbacks, through which policies may shape actor preferences which, in turn, create stronger policies; and 3) the role of broader contexts (political economies, institutions, cultural norms, and technical systems) in creating more (or less) favourable conditions for deliberate acceleration. We discuss the importance of each theme, briefly review previous research and articulate new research questions. Our concluding section discusses the current and potential future relationship between transitions theory and political science

Characteristics of high-resolution versions of the Met Office unified model for forecasting convection over the United Kingdom

With many operational centers moving toward order 1-km-gridlength models for routine weather forecasting, this paper presents a systematic investigation of the properties of high-resolution versions of the Met Office Unified Model for short-range forecasting of convective rainfall events. The authors describe a suite of configurations of the Met Office Unified Model running with grid lengths of 12, 4, and 1 km and analyze results from these models for a number of convective cases from the summers of 2003, 2004, and 2005. The analysis includes subjective evaluation of the rainfall fields and comparisons of rainfall amounts, initiation, cell statistics, and a scale-selective verification technique. It is shown that the 4- and 1-km-gridlength models often give more realistic-looking precipitation fields because convection is represented explicitly rather than parameterized. However, the 4-km model representation suffers from large convective cells and delayed initiation because the grid length is too long to correctly reproduce the convection explicitly. These problems are not as evident in the 1-km model, although it does suffer from too numerous small cells in some situations. Both the 4- and 1-km models suffer from poor representation at the start of the forecast in the period when the high-resolution detail is spinning up from the lower-resolution (12 km) starting data used. A scale-selective precipitation verification technique implies that for later times in the forecasts (after the spinup period) the 1-km model performs better than the 12- and 4-km models for lower rainfall thresholds. For higher thresholds the 4-km model scores almost as well as the 1-km model, and both do better than the 12-km model

The Behaviour of Both Listeria monocytogenes and Rat Ciliated Ependymal Cells Is Altered during Their Co-Culture

Ciliated ependymal cells line the cerebral ventricles and aqueducts separating the infected CSF from the brain parenchyma in meningitis