Aim: Previous research has identified that a clinical nurse specialist in epilepsy (CNSE) plays a key role in the management of people with epilepsy (Goodwin et al, 2004). However, there remains a lack of research that focuses on the clinical knowledge and skill base of an individual CNSE working at an advanced level of practice. The aim of this research study is to meet that void. Methods: Nine CNSEs participated in this study. Prospective data on their daily clinical and non-clinical activity over a period of 20 days using a self-completion diary was collected. Following quantitative collation of the data, the researchers obtained qualitative data from the nine respondents using structured interviews to clarify the initial data. Results: Respondents were highly autonomous in nurse-led clinics, and 40% of their time was spent on the clinical aspects of care, which included epilepsy assessment, ordering investigations and drug management. Time spent on non-clinical activities included teaching, education and research was 60%. Conclusion: This study demonstrates the advanced level of practice of the CNSE. As advanced nurse practitioners, the nine participants demonstrated an ability to make independent and complex decisions about patient care using clinical judgment and their unique and expert knowledge base.

This study integrated the Spiral Curriculum approach into the Robust Learning Model as part of a continuous improvement process that was designed to improve educational effectiveness and then assessed the differences between the initial and integrated models as well as the predictability of the first course in the integrated learning model on a wide range of educational effectiveness indicators for online master degree programs. Meaningful improvement in educational effectiveness was validated by the study. The importance of the first course's predictors in predicting and explaining the various degree program educational effectiveness indicators was also very instrumental. The theoretical and practical implications of the study's findings for online faculty, university administrators, and policy makers were examined.

The main focus of this study was the assessment performed by recent alumni as an important component of online degree program outcomes assessment. A model of components of the online learning environment was developed and tested to predictive various levels of educational outcomes of online degree programs separately for bachelor and master degree programs' alumni. The educational outcomes include direct educational outcomes and attributed educational outcomes. The model was then validated in predicting summative outcomes assessment. The model played an important role in understanding degree program's online educational outcomes and its predictive validity across all outcomes and degree levels is very high. The alum assessment of the quality of the learning model was found to be the most dominant predictor of educational outcomes for all assessment criteria and for all levels of degree programs. Finally, the explanations and implications of these findings were discussed.