Knowledge, perceptions and expectations of nurses in e-health: results of a survey in a children's hospital

We surveyed all nurses working at a tertiary paediatric hospital (except casual staff and those who were on leave) from 27 hospital departments. A total of 365 questionnaires were distributed. There were 40 questions in six sections: demographic details, knowledge of e-health, relevance of e-health to nursing profession, computing skills, Internet use and access to e-health education. A total of 253 surveys were completed (69%). Most respondents reported that that they had never had e-health education of any sort (87%) and their e-health knowledge and skills were low (71%). However, 11% of nurses reported some exposure to e-health through their work. Over half (56%) of respondents indicated that e-health was important, very important or critical for health professions while 26% were not sure. The lack of education and training was considered by most respondents (71%) to be the main barrier to adopting e-health. While nurses seemed to have moderate awareness of the potential benefits of e-health, their practical skills and knowledge of the topic were very limited.

Discovering user access pattern based on probabilistic latent factor model

There has been an increased demand for characterizing user access patterns using web mining techniques since the informative knowledge extracted from web server log files can not only offer benefits for web site structure improvement but also for better understanding of user navigational behavior. In this paper, we present a web usage mining method, which utilize web user usage and page linkage information to capture user access pattern based on Probabilistic Latent Semantic Analysis (PLSA) model. A specific probabilistic model analysis algorithm, EM algorithm, is applied to the integrated usage data to infer the latent semantic factors as well as generate user session clusters for revealing user access patterns. Experiments have been conducted on real world data set to validate the effectiveness of the proposed approach. The results have shown that the presented method is capable of characterizing the latent semantic factors and generating user profile in terms of weighted page vectors, which may reflect the common access interest exhibited by users among same session cluster.