Introduction Telepsychiatry is becoming an increasingly appealing option for mental health treatment due to its ability to overcome barriers which prevent certain demographics from having access to mental health services. There is a surprising lack of research being done on this promising mode of health care delivery. The aim of this study is to evaluate the existing literature in order to determine the clinical effectiveness and cost-effectiveness of telepsychiatry in resource-constrained environments. Methods Literature searches were performed in PsychINFO, PubMed, Medline, EMBASE, Centre for Reviews and Dissemination, and the Cochrane Library Controlled Trial Registry databases (2000 - May 2017). A search of the following terms was used: telemedicine; telemedical; telepsychiatry; telepsychiatric; teleconsultation; e-health; video conference; and telecare. Type of mental disorder and intervention, along with the clinical outcome or patient satisfaction, were all identified. Exclusion criteria included studies with a sample size of fewer than 10 cases, as well as studies which failed to analyze intervention outcomes. Results Of the 1,477 identified articles, 14 randomized controlled trials were included for review. Despite the methodological limitations and the small number of existing studies, there appears to be limited evidence pointing towards the efficacy of telepsychiatry in resource-constrained environments, although patients and providers tend to prefer face-to-face treatment over video conferencing. Two of the studies included in this paper found video conferencing to be more effective than face-to-face treatment, while none reported the opposite. At the very least, we hypothesize that psychotherapeutic treatment delivered via video conferencing is just as effective as a traditional treatment, albeit less desirable. Conclusion More research is required in order to further evaluate the efficacy of telepsychiatry in the management of mental illness, as there is a current lack of scientific evidence to draw any conclusions. However, there exists a strong hypothesis that telepsychiatric treatment yields the same results as the traditional, in-person therapy and that telepsychiatry is a useful alternative when traditional therapy is not possible. Countries with substantial numbers of refugees living in resource-constrained areas, such as camps, should be encouraged to develop telepsychiatry programs.
Business process models are the conceptual models to depict the workflow of an organization. Process model matching (PMM) refers to the automatic identification of corresponding activities between a pair of process models that show similar or the same behavior. During the last few years, PMM has received much of the researchers' attention due to its wide range of applications, such as clone detection and harmonization of process models. Consequently, a plethora of PMM techniques has been developed. In order to evaluate the effectiveness of these techniques, experts have developed three benchmark datasets, formally called PMMC'15 datasets. Furthermore, the process models in the datasets have been converted into OAEI'17 ontologies. These resources are a valuable asset for the PMM community to evaluate process model matching techniques. However, these resources (PMMC'15 and OAEI'17) are limited to fewer models and a handful collection of corresponding activities among these models that may not be sufficient to rigorously evaluate the PMM techniques. To fill this gap, this paper provides a large, diverse, and a carefully handcrafted collection of process models, along with their benchmark correspondences. The process model collection and benchmark correspondences between these models are freely available for the community [1]. Our newly developed dataset, together with the existing resources, can be used for a thorough evaluation of PMM techniques, especially in the context of the vocabulary mismatch problem. At last, we have evaluated the characteristics of our dataset by a series of experiments while involving widely used similarity measures in PMM research. The results reveal that our dataset is larger, diverse, and challenging as compared to existing datasets in the PMM domain.
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