Curriculum on Resident Education in Care of Older Adults in Acute, Transitional and Extended Care Settings

Most geriatric care is provided in non-hospital settings. Internal Medicine and Family Medicine residents should therefore learn about these different clinical sites and acuity levels of care. To help facilitate this learning, a geriatrics training curriculum for internal medicine residents was developed that focused on cognition, function, goals of care and medication management in both in-hospital and non-hospital settings. Residents rotated through both in-hospital and non-hospital settings as one block rotation. They took a test of geriatric learning before the rotation and then took the same test at the end of the rotation. Residents showed an improvement in several geriatric domains on completion of a combined in-hospital and non-hospital rotation curriculum. We concluded that the development and implementation of a combined rotation curriculum has practical application to resident learning and the potential to improve geriatrics care outside of hospital settings

Warped Riemannian metrics for location-scale models

The present paper shows that warped Riemannian metrics, a class of Riemannian metrics which play a prominent role in Riemannian geometry, are also of fundamental importance in information geometry. Precisely, the paper features a new theorem, which states that the Rao-Fisher information metric of any location-scale model, defined on a Riemannian manifold, is a warped Riemannian metric, whenever this model is invariant under the action of some Lie group. This theorem is a valuable tool in finding the expression of the Rao-Fisher information metric of location-scale models defined on high-dimensional Riemannian manifolds. Indeed, a warped Riemannian metric is fully determined by only two functions of a single variable, irrespective of the dimension of the underlying Riemannian manifold. Starting from this theorem, several original contributions are made. The expression of the Rao-Fisher information metric of the Riemannian Gaussian model is provided, for the first time in the literature. A generalised definition of the Mahalanobis distance is introduced, which is applicable to any location-scale model defined on a Riemannian manifold. The solution of the geodesic equation is obtained, for any Rao-Fisher information metric defined in terms of warped Riemannian metrics. Finally, using a mixture of analytical and numerical computations, it is shown that the parameter space of the von Mises-Fisher model of $n$-dimensional directional data, when equipped with its Rao-Fisher information metric, becomes a Hadamard manifold, a simply-connected complete Riemannian manifold of negative sectional curvature, for $n = 2,\ldots,8$. Hopefully, in upcoming work, this will be proved for any value of $n$.Comment: first version, before submissio