Design and implementation of a clinical communication skills training course for dental students at Mashhad University of Medical Sciences

Background: Given the importance of communication skills education in medical sciences and the need for an appropriate curriculum for clinical education, this study aimed to design and implement a clinical communication skills curriculum for third-year dental students at Mashhad University of Medical Sciences.Method: This action research study included 60 third-year dental students. The educational program was developed based on the Communication Skills Training-3 Elements Model and Instructional Development Institute (IDI) model. Interactive student-centered teaching methods were designed by including team-based teaching, group discussion, action-based interactive lectures, role-playing, and multimedia application strategies. The course was delivered over 18 sessions, each 90 minutes long, by communication skills and dentistry professors, as a workshop-based course worth 1 credit in the fifth semester of the general dentistry program. The students were evaluated regarding their knowledge and skills at the end of the course.Results: Most of the expected communication skills, including starting the session and establishing a connection, gathering data, considering the patient's perspective, providing information, mutual agreement, and ending the session were at a good level after the implementation of this program. Female students performed better than their male counterparts in various communication aspects, although statistically significant differences were only observed for the skills of providing information, mutual agreement, and ending the session (P=0.032).Conclusion: Using this clinical communication skills course for dental students can be beneficial, and with further studies in this field and examining its effectiveness in more dental schools, it can be presented to other dental schools as well

Abstraction Mechanisms Towards Large-Scale Agent-Based Simulations

The typically large degrees of interaction in agent-based simulations come at considerable computational costs. In this thesis, we propose an abstraction framework to reduce the run-time of the simulations by learning recurring patterns. We employ machine learning techniques to abstract groups of agents or their behaviours to cut down computational complexity, while preserving the inherent flexibility of agent-based models. The learned abstractions, which subsume the underlying model agents' interactions, are constantly tested for their validity---after all, the dynamics of a system may change over time to such an extent that previously learned patterns would not reoccur. An invalid abstraction is, therefore, removed from the simulation. The creation and removal of abstractions continues throughout the course of a simulation in order to ensure an adequate adaptation to the system dynamics. Experimental results on biological agent-based simulations show that our proposed framework can successfully boost the simulation speed while maintaining the freedom of arbitrary interactions

Optimization of Swarm-Based Simulations

In computational swarms, large numbers of reactive agents are simulated. The swarm individuals may coordinate their movements in a “search space” to create efficient routes, to occupy niches, or to find the highest peaks. From a more general perspective though, swarms are a means of representation and computation to bridge the gap between local, individual interactions, and global, emergent phenomena. Computational swarms bear great advantages over other numeric methods, for instance, regarding their extensibility, potential for real-time interaction, dynamic interaction topologies, close translation between natural science theory and the computational model, and the integration of multiscale and multiphysics aspects. However, the more comprehensive a swarm-based model becomes, the more demanding its configuration and the more costly its computation become. In this paper, we present an approach to effectively configure and efficiently compute swarm-based simulations by means of heuristic, population-based optimization techniques. We emphasize the commonalities of several of our recent studies that shed light on top-down model optimization and bottom-up abstraction techniques, culminating in a postulation of a general concept of self-organized optimization in swarm-based simulations.Peer Reviewe