Analytical Psychology in Psychiatric Clinics (진료현장에서의 분석심리학 : 정신건강의학과 진료실에서 접하는 문제들의 분석심리학적 접근 경험)
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- Sim-seong Yeon-gu
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- v.35 no.2
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- pp.85-112
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- 2020
How does analytical psychology help understand patients at general psychiatric clinics? It's necessary to think about how knowledge of analytical psychology can help young psychiatrists who are in training. Patients who come to us bring symptoms(problems). Symptoms can be compared to tickets to a movie theater. Symptoms accompanied by complaints of pain are not only pathological phenomena to be eliminated, but an important pathway to access the patients' inner problems. In terms of seeing the whole, the point of view in analytical psychology is to see the unconscious as well as the consciousness, even the elements the patients do not speak or know of. When determining indications and contra-indications during the initial process of treating a patient, it is more important to acknowledge the therapist's capabilities and limitations than the patient's condition or limitations The approach to complaints of the same symptoms may differ depending on whether the patient is in the first half or the second half of one's life. Analytical psychology is empirical psychology that experiences and it adheres to a phenomenological position that recognizes the phenomenon as true in itself, not logically right or wrong. The analytical psychological view of understanding mental phenomena asks the causal perspective of why the symptoms occurred. At the same time, the therapist, along with the patient, must seek answers to the question of why now and for what purpose. A therapist is a person who experiences the patient's personal development process together. In analytical psychotherapy, the therapist's attitude is more emphasized than the treatment method or technique; it is regarded as of the utmost importance. In this regard, analytical psychology is a practical and useful therapeutic tool, and is a field of study that can be widely used in actual psychiatric clinics. In addition to understanding the patient, it is also the most important discipline for the therapists, especially for the education and growth of those who want to become a treatment tool themselves.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (