The reliability of in-training assessment when performance improvement is taken into account

During in-training assessment students are frequently assessed over a longer period of time and therefore it can be expected that their performance will improve. We studied whether there really is a measurable performance improvement when students are assessed over an extended period of time and how this improvement affects the reliability of the overall judgement. In-training assessment results were obtained from 104 students on rotation at our university hospital or at one of the six affiliated hospitals. Generalisability theory was used in combination with multilevel analysis to obtain reliability coefficients and to estimate the number of assessments needed for reliable overall judgement, both including and excluding performance improvement. Students’ clinical performance ratings improved significantly from a mean of 7.6 at the start to a mean of 7.8 at the end of their clerkship. When taking performance improvement into account, reliability coefficients were higher. The number of assessments needed to achieve a reliability of 0.80 or higher decreased from 17 to 11. Therefore, when studying reliability of in-training assessment, performance improvement should be considered

Link-Prediction to Tackle the Boundary Specification Problem in Social Network Surveys

Diffusion processes in social networks often cause the emergence of global phenomena from individual behavior within a society. The study of those global phenomena and the simulation of those diffusion processes frequently require a good model of the global network. However, survey data and data from online sources are often restricted to single social groups or features, such as age groups, single schools, companies, or interest groups. Hence, a modeling approach is required that extrapolates the locally restricted data to a global network model. We tackle this Missing Data Problem using Link-Prediction techniques from social network research, network generation techniques from the area of Social Simulation, as well as a combination of both. We found that techniques employing less information may be more adequate to solve this problem, especially when data granularity is an issue. We validated the network models created with our techniques on a number of real-world networks, investigating degree distributions as well as the likelihood of links given the geographical distance between two nodes