How can we teach EBM in clinical practice? : an analysis of barriers to implementation of on-the-job EBM teaching and learning.

Introduction: Evidence-based medicine (EBM) improves the quality of health care. Courses on how to teach EBM in practice are available, but knowledge does not automatically imply its application in teaching. We aimed to identify and compare barriers and facilitators for teaching EBM in clinical practice in various European countries. Methods: A questionnaire was constructed listing potential barriers and facilitators for EBM teaching in clinical practice. Answers were reported on a 7-point Likert scale ranging from not at all being a barrier to being an insurmountable barrier. Results: The questionnaire was completed by 120 clinical EBM teachers from 11 countries. Lack of time was the strongest barrier for teaching EBM in practice (median 5). Moderate barriers were the lack of requirements for EBM skills and a pyramid hierarchy in health care management structure (median 4). In Germany, Hungary and Poland, reading and understanding articles in English was a higher barrier than in the other countries. Conclusion: Incorporation of teaching EBM in practice faces several barriers to implementation. Teaching EBM in clinical settings is most successful where EBM principles are culturally embedded and form part and parcel of everyday clinical decisions and medical practice.

Agency Fact Sheet, Department of College Aid, FY2007

This document produced by the Iowa Department of Administrative Services has been developed to provide a multitude of information about executive branch agencies/department on a single sheet of paper. The facts provides general information, contact information, workforce data, leave and benefits information and affirmative action data.

Agency Fact Sheet, Department of College Aid, FY2008

This document produced by the Iowa Department of Administrative Services has been developed to provide a multitude of information about executive branch agencies/department on a single sheet of paper. The facts provides general information, contact information, workforce data, leave and benefits information and affirmative action data.

Agency Fact Sheet, Department of College Aid, FY2009

This document produced by the Iowa Department of Administrative Services has been developed to provide a multitude of information about executive branch agencies/department on a single sheet of paper. The facts provides general information, contact information, workforce data, leave and benefits information and affirmative action data.

Agency Fact Sheet, Department of College Aid, FY2010

This document produced by the Iowa Department of Administrative Services has been developed to provide a multitude of information about executive branch agencies/department on a single sheet of paper. The facts provides general information, contact information, workforce data, leave and benefits information and affirmative action data.

Agency Fact Sheet, Department of College Aid, FY2011

This document produced by the Iowa Department of Administrative Services has been developed to provide a multitude of information about executive branch agencies/department on a single sheet of paper. The facts provides general information, contact information, workforce data, leave and benefits information and affirmative action data.

Agency Fact Sheet, Department of College Aid, FY2002

This document produced by the Iowa Department of Administrative Services has been developed to provide a multitude of information about executive branch agencies/department on a single sheet of paper. The facts provides general information, contact information, workforce data, leave and benefits information and affirmative action data.

Agency Fact Sheet, Department of College Aid, FY2003

This document produced by the Iowa Department of Administrative Services has been developed to provide a multitude of information about executive branch agencies/department on a single sheet of paper. The facts provides general information, contact information, workforce data, leave and benefits information and affirmative action data.

Agency Fact Sheet, Department of College Aid, FY2004

This document produced by the Iowa Department of Administrative Services has been developed to provide a multitude of information about executive branch agencies/department on a single sheet of paper. The facts provides general information, contact information, workforce data, leave and benefits information and affirmative action data.

Agency Fact Sheet, Department of College Aid, FY2005

This document produced by the Iowa Department of Administrative Services has been developed to provide a multitude of information about executive branch agencies/department on a single sheet of paper. The facts provides general information, contact information, workforce data, leave and benefits information and affirmative action data.

Audit report on the Iowa Federal Family Education Loan Program Division, a Division of the Iowa College Student Aid Commission, for the year ended June 30, 2011

Audit report on the Iowa Federal Family Education Loan Program Division, a Division of the Iowa College Student Aid Commission, for the year ended June 30, 2011

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.