Making it easy to do the right thing in healthcare: Advancing improvement science education through accredited pan European higher education modules

Background: Numerous international policy drivers espouse the need to improve healthcare. The application of Improvement Science has the potential to restore the balance of healthcare and transform it to a more person-centred and quality improvement focussed system. However there is currently no accredited Improvement Science education offered routinely to healthcare students. This means that there are a huge number of healthcare professionals who do not have the conceptual or experiential skills to apply Improvement Science in everyday practise. Methods: This article describes how seven European Higher Education Institutions (HEIs) worked together to develop four evidence informed accredited inter-professional Improvement Science modules for under and postgraduate healthcare students. It outlines the way in which a Policy Delphi, a narrative literature review, a review of the competency and capability requirements for healthcare professionals to practise Improvement Science, and a mapping of current Improvement Science education informed the content of the modules. Results: A contemporary consensus definition of Healthcare Improvement Science was developed. The four Improvement Science modules that have been designed are outlined. A framework to evaluate the impact modules have in practise has been developed and piloted. Conclusion: The authors argue that there is a clear need to advance healthcare Improvement Science education through incorporating evidence based accredited modules into healthcare professional education. They suggest that if Improvement Science education, that incorporates work based learning, becomes a staple part of the curricula in inter-professional education then it has real promise to improve the delivery, quality and design of healthcare.

Research-action Methodology between Computer Science and Educational Psychology: a Multidisciplinary Approach

Computer science studies possess a strong multidisciplinary aptitude since most graduates do their professional work outside of a computing environment, in close collaboration with professionals from many different areas. However, the training offered in computer science studies lacks that multidisciplinary factor, focusing more on purely technical aspects. In this paper we present a novel experience where computer studies and educational psychology find a common ground and realistic working through laboratory practices. Specifically, the work enables students of computer science education the development of diagnosis support systems, with artificial intelligence techniques, which could then be used for future educational psychologists. The applications developed by computer science students are the creation of a model for the diagnosis of pervasive developmental disorders (PDD), sometimes also commonly called the autism spectrum disorders (ASD). The complexity of this diagnosis, not only by the exclusive characteristics of every person who suffers from it, but also by the large numbers of variables involved in it, requires very strong and close interdisciplinary participation. This work demonstrates that it is possible to intervene in a curricular perspective, in the university, to promote the development of interpersonal skills. What can be shown, in this way, is a methodology for interdisciplinary practices design and a guide for monitoring and evaluation. The results are very encouraging since we obtained significant differences in academic achievement between students who attended a course using the new methodology and those who did not use it.

The Influence of Gender, Intellectual Ability, Academic Self-Concept, Self-Regulation, Learning Strategies, Popularity and Parent Involvement in Early Adolescence

The present study examined the predictive effects of gender, intellectual ability, self-concept, motivation, learning strategies, popularity and parent involvement on academic achievement. Hiearchical regression analysis were performed with six steps in which each variable was included, among a sample of 1398 high school students (mean age = 12.5; standard deviation = .67) of eight education centers from the province of Alicante (Spain). The results revealed significant predictive effects of all of the variables, explaining 59.1% of the total variance.