Qualitative content analysis of the highly cited articles in medical education: Trends and characteristics associated with citation of published studies in medical education research.

PURPOSE: To explore and describe the highly cited articles' themes of research in medical education and to provide an insight into and reflection on which the elites of medical education society invested their energies from 2009 to 2018. METHODS: An in-depth content analysis as a research technique for the objective, systematic, and quantitative description of the manifest content of communication was used to quantitatively assess subject interests, methods, and other characteristics associated with citation of published studies in medical education research. Meaning units were compacted and coded with labels and categories in two phases. RESULTS: Among a variety of topics, methods, and strategies, 764 codes, 24 descriptive themes, and seven categories were extracted from the content analysis as the most prominent. Categories of medical education research were: modern technologies updating in medical education; learner performance improvement; sociological aspects of medical education; clinical reasoning; research methodology concerns of medical education; instructional design educational models; and professional aspects of medical education. CONCLUSIONS: Commitment to continuous revision of educational emphasis and concerns on technological, sociological, and methodological concerns were the most repeated components of the highly cited articles that were ascertained through increased structure course designs and instructional strategies of the flipped classrooms to realize clinical reasoning and performance improvement.[Box: see text].

Emotion recognition support system: Where physicians and psychiatrists meet linguists and data engineers.

An important factor in the course of daily medical diagnosis and treatment is understanding patients' emotional states by the caregiver physicians. However, patients usually avoid speaking out their emotions when expressing their somatic symptoms and complaints to their non-psychiatrist doctor. On the other hand, clinicians usually lack the required expertise (or time) and have a deficit in mining various verbal and non-verbal emotional signals of the patients. As a result, in many cases, there is an emotion recognition barrier between the clinician and the patients making all patients seem the same except for their different somatic symptoms. In particular, we aim to identify and combine three major disciplines (psychology, linguistics, and data science) approaches for detecting emotions from verbal communication and propose an integrated solution for emotion recognition support. Such a platform may give emotional guides and indices to the clinician based on verbal communication at the consultation time.