Rural Curricular Guidelines Based on Practice Scope of Recent Residency Graduates Practicing in Small Communities.

BACKGROUND AND OBJECTIVES: The optimal curriculum for training family physicians for rural practice within a traditional urban-based residency is not defined. We used the scope of practice among recent family medicine graduates of residencies associated with Preparing the Personal Physician for Practice (P4), practicing in small communities, to identify rural curriculum components. METHODS: We surveyed graduates 18 months after residency between 2007 and 2014. The survey measured self-reported practice characteristics, including community size, and scope of practice. We compared the subgroups according to practice community size. RESULTS: Compared to graduates in larger communities, those practicing in small communities were more likely to report a broader scope of clinical practice including: adult hospital care (59% vs 35%), vaginal deliveries (23% vs 12%), C sections as primary surgeon (14% vs 5%) and assistant (21% vs 8%), newborn hospital care (45% vs 24%), and procedures such as endometrial biopsy (46% vs 33%), joint injections and aspirations (89% vs 79%), and fracture care (58% vs 42%). Graduates in small communities were also more often engaged in assessing community health needs (78% vs 64%) and developing community interventions (67% vs 51%) compared to graduates in larger communities. In contrast, graduates in small communities were less likely to have integrated behavioral health (26% vs 46%) and case management support (37% vs 52%). CONCLUSIONS: A rural practice curriculum should include training toward a broad medical scope of practice as well as skills in community-oriented primary care and integrated behavioral health.

Gaussian Processes for Data-Efficient Learning in Robotics and Control.

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

South Dakota accidental childhood deaths, 2000-2007: what can we do?

INTRODUCTION: Unintentional injuries are the leading cause of death in children around the world and are an under-recognized public health problem in the United States. PURPOSE: The purpose of this study was to highlight the nature of the problem in South Dakota and outline interventions that have been successful in reducing childhood injuries in other states. METHODS: This quantitative retrospective study examined mortality files in South Dakota for children birth to 19 years of age who died between January 1, 2000 to December 28, 2007. RESULTS: Although the number of deaths declined considerably from 2006 to 2007, South Dakota had the second-highest rate in the nation of childhood unintentional injury deaths from all causes between 2000-2005. The majority of deaths occurred in males and were associated with transportation-related deaths. Suffocation was the leading cause of death for newborns to age 1 year. CONCLUSION: Childhood accidental death in South Dakota is clearly a critical public health problem. Intervention efforts to reduce deaths from unintentional injuries amongst children should be targeted as the leading causes of accidental death for specific age groups and American Indian youth. Physicians, health educators and policymakers must play a role in prevention targeting the high-risk groups in addition to advocating for policy changes to protect childhood safety. More stringent child restraint laws, graduated driving laws, smoking cessation programs for parents, creation of safer sleep environments and further investigation of why a high proportion of American Indian children die accidentally in South Dakota are all warranted.

Modeling and visualizing uncertainty in gene expression clusters using dirichlet process mixtures.

Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data.