Semantic Network Analysis of Iussues Related to Mental Illness in Korea Media: Focusing on the Five Major Media from 2016 to 2018 / 신경정신의학

OBJECTIVES: This study examined the trend of press reports on mental illness in Korea to help set a desirable direction for future press reports on the subject.METHODS: This study adopted the method of semantic network analysis to analyze the trend of press reporting on mental illness in Korea. The study analyzed 1253 newspaper articles from 2016 to 2018 published by the five major Korean press: The Kyunghyang Shinmun, The Dong-A Ilbo, The Chosun Ilbo, The JoongAng Ilbo, and The Hankyoreh.RESULTS: The number of articles on mental illness is on the decline year by year, with 465 articles in 2016, 406 in 2017, and 382 in 2018. According to keyword analysis, the order of the words of the highest frequency is as follows: treatment (659), patients (486), mental health (466), incidents (436), and children and adolescents (413). In terms of the Degree Centrality, Closeness Centrality, and Betweenness Centrality, the following words are commonly ranked within the top 10 on the lists: treatment, children and adolescents, patient, mental health, depressive disorder, family, suicide, and counselling.CONCLUSION: The press should pay more attention to mental illness, and its reporting should guarantee accuracy, objectivity, fairness, diversity, and social responsibility. In addition, reports on mental illness need to provide balanced health information, considering the readers, from basic information on mental illness to detailed treatment methods. Establishing reporting guidelines for mental illness would help reduce harm and prejudice related to mental illness and increase access to treatment.

The Workplace Experiences of Breast Cancer Survivors: A Survey of an Online Community

PURPOSE: This study attempted to examine those who experienced working after breast cancer diagnosis or job retention after timeoff or resignation. METHODS: Data collection and analysis were conducted using the biggest community website which contains articles written and discussed by breast cancer survivors. We used semantic network analysis to identify context of various keywords from collected 1,186 articles. After refining, we analyzed 212 keywords. Data were analyzed using two methodological approaches, ‘network analysis and data visualization’ (NodeXl & PFnet). RESULTS: As a result, 6 groups were classified; ‘conflict between breast cancer and job’, ‘difficulty of working and treatment parallel’, ‘breast cancer management while working’, ‘busy and hard life but showing gratitude’, ‘my body needs’, and ‘my heart needs’. We found that even though they had difficulties of working and treatment at the same time, they were thankful for their life. and they also reprogrammed their mind for more positive thinking through exercise for the health management and establishing a good eating habit. CONCLUSION: This paper will argue that we need to identify the experiences of cancer survivors. This allows the growth of the improvement working environment, the direction of policy, and the concern and understanding of nurses to needs of breast canner patients was definitely needed.