Where and how to find data on safety: what do systematic reviews of complementary therapies tell us?

Background: Successfully identifying relevant data for systematic reviews with a focus on safety may require retrieving information from a wider range of sources than for ‘effectiveness’ systematic reviews. Searching for safety data continues to prove a major challenge. Objectives: To examine search methods used in systematic reviews of safety and to investigate indexing. Methods: Systematic reviews focusing on safety of complementary therapies and related interventions were retrieved from comprehensive searches of major databases. Data was extracted on search strategies, sources used and indexing in major databases. Safety related search terms were compared against index terms available on major databases. Data extraction by one researcher using a pre-prepared template was checked for accuracy by a second researcher. Results: Screening of 2563 records resulted in 88 systematic reviews being identified. Information sources used varied with the type of intervention being addressed. Comparison of search terms with available index terms revealed additional potentially relevant terms that could be used in constructing search strategies. Seventy-nine reviews were indexed on PubMed, 84 on EMBASE, 21 on CINAHL, 15 on AMED, 6 on PsycINFO, 2 on BNI and HMIC. The mean number of generic safety-related indexing terms on PubMed records was 2.6. For EMBASE the mean number was 4.8 with at least 61 unique terms being employed. Most frequently used indexing terms and subheadings were adverse effects, side effects, drug interactions and herb-drug interactions. Use of terms specifically referring to safety varied across databases. Conclusions: Investigation of search methods revealed the range of information sources used, a list of which may prove a valuable resource for those planning to conduct systematic reviews of safety. The findings also indicated that there is potential to improve safety-related search strategies. Finally, an insight is provided into indexing of and most effective terms for finding safety studies on major databases.

Interaction Rituals and Jumbled Emotions Among “Relative Strangers”: Simulated Patient Work on a Trainee Complementary Therapy Practitioner Program

Learning games such as role-play (which we refer to as “simulated interaction rituals”) are commonly used as social tools to develop trainee health practitioners. However, the effect of such rituals on individual and group participant emotions has not been carefully studied. Using a heuristic approach, we explore the experiences of complementary therapy practitioner trainees (and their trainers) participating in a personal development course. Ten trainees and two tutors were interviewed, observational notes taken, and a secondary qualitative analysis undertaken. Participants and tutors described a medley of disparate emotional and moral responses to group rituals, conceptualized in this article as “jumbled emotions.” Such emotions required disentangling, and both trainees and staff perceived participating in unfamiliar rituals “with relative strangers” as challenging. Front of stage effects are frequently processed “backstage,” as rituals threaten social embarrassment and confusion. Concerns around emotional triggers, authenticity, and outcomes of rituals arise at the time, yet trainees can find ways to work through these issues in time.

Business Models for Cloud Computing: Experiences from Developing Modeling & Simulation as a Service Applications in Industry

The potential of cloud computing is gaining significant interest in Modeling & Simulation (M&S). The underlying concept of using computing power as a utility is very attractive to users that can access state-of-the-art hardware and software without capital investment. Moreover, the cloud computing characteristics of rapid elasticity and the ability to scale up or down according to workload make it very attractive to numerous applications including M&S. Research and development work typically focuses on the implementation of cloud-based systems supporting M&S as a Service (MSaaS). Such systems are typically composed of a supply chain of technology services. How is the payment collected from the end-user and distributed to the stakeholders in the supply chain? We discuss the business aspects of developing a cloud platform for various M&S applications. Business models from the perspectives of the stakeholders involved in providing and using MSaaS and cloud computing are investigated and presented.

Analyzing and Modeling Medical Data on Distributed Computing Infrastructures

Researchers want to analyse Health Care data which may requires large pools of compute and data resources. To have them they need access to Distributed Computing Infrastructures (DCI). To use them it requires expertise which researchers may not have. Workflows can hide infrastructures. There are many workflow systems but they are not interoperable. To learn a workflow system and create workflows in a workflow system may require significant effort. Considering these efforts it is not reasonable to expect that researchers will learn new workflow systems if they want to run workflows of other workflow systems. As a result, the lack of interoperability prevents workflow sharing and a vast amount of research efforts is wasted. The FP7 Sharing Interoperable Workflow for Large-Scale Scientific Simulation on Available DCIs (SHIWA) project developed the Coarse-Grained Interoperability (CGI) to enable workflow sharing. The project created the SHIWA Simulation Platform (SSP) to support CGI as a production-level service. The paper describes how the CGI approach can be used for analysis and simulation in Health Care.

Affective Computing to Enhance E-Learning in Segregated Societies

According to UN Women, to build stronger economies, it is essential to empower women to participate fully in economic life across all sectors. Increasing women and girls’ education enhances their chances to participate in the labor market. In certain cultures, like in Saudi Arabia, women contribution to the public economy growth is very limited. According to the World Bank, less than 20 percent of the female population participate in the labor force. This low participation rate has many reasons. One of them, is the educational level and educational quality for females. Although Saudi Arabia has about thirty three universities, opportunities are still limited for women because of the restrictions of access put upon them. A mixture of local norms, traditions, social beliefs, and principles preventing women from receiving full benefits from the educational system. Gender segregation is one of the challenges that limits the women access for education. It causes a problem due to the shortage of female faculty throughout the country. To overcome this problem, male faculty are allowed to teach female students under certain regulations and following a certain method of education delivery and interaction. However, most of these methods lack face-to-face communication between the teacher and students, which lowers the interactivity level and, accordingly, the students’ engagement, and increases the need for other alternatives. The e-learning model is one of high benefit for female students in such societies. Recognizing the students’ engagement is not straightforward in the e-learning model. To measure the level of engagement, the learner’s mood or emotions should be taken into consideration to help understanding and judging the level of engagement. This paper is to investigate the relationship between emotions and engagement in the e-learning environment, and how recognizing the learner’s emotions and change the content delivery accordingly can affect the efficiency of the e-learning process. The proposed experiment alluded to herein should help to find ways to increase the engagement of the learners, hence, enhance the efficiency of the learning process and the quality of learning, which will increase the chances and opportunities for women in such societies to participate more effectively in the labor market.

Distributed Agent-Based Load Balancer for Cloud Computing

In this paper we present a concept of an agent-based strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. To provide a base for our research we specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. We also present an early version of simulation environment and a prototype of agent-based load balancer implemented in functional language Scala and Akka framework.

A Meta-Heuristic Load Balancer for Cloud Computing Systems

This paper introduces a strategy to allocate services on a cloud system without overloading the nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded with the outputs of other meta-heuristic algorithms.