: This research focuses on the experiences of social workers who assist socially isolated households to prevent solitary deaths. The study aims to understand their support-related experiences, examining both the perceptions and practices of these workers. It highlights the importance of tailored support for isolated households, especially considering the unique challenges faced by middle-aged individuals in this demographic. Methods : The study employed purposive sampling to recruit social workers in Seoul who are actively engaged in supporting socially isolated households. Semi-structured interviews were conducted to gather in-depth insights into their experiences. The research methodology was rooted in qualitative analysis, specifically using Giorgi’s method. Results : Analysis of the interview data led to the identification of 12 sub-components and 5 upper components: “implementation of support for socially isolated households different from previous experience,” “feeling helpless in the face of inevitability,” “sympathy and communication with the heart,” “discovering challenges and opportunities in the field,” and “slowly, waiting for change.” These findings underscored the complexities and emotional challenges faced by social workers. Conclusion : The study highlights a significant gap in resources and manpower for supporting isolated households. It suggests the need for long-term, specially designed support systems, emphasizing improvements to better aid socially isolated individuals and the social workers who support them.

There is a more urgent call for educational methods of machine learning in medical education, and therefore, new approaches of teaching and researching machine learning in medicine are needed. This paper presents a case using machine learning through text analysis. Topic modeling of news articles with the keyword ‘asbestos’ were examined. Two hypotheses were tested using this method, and the process of machine learning of texts is illustrated through this example. Using an automated text analysis method, all the news articles published from January 1, 1990 to November 15, 2016 in South Korea which included ‘asbestos’ in the title and the body were collected by web scraping. Differences in topics were analyzed by structured topic modelling (STM) and compared by press companies and periods. More articles were found in liberal media outlets. Differences were found in the number and types of topics in the articles according to the partisanship and period. STM showed that the conservative press views asbestos as a personal problem, while the progressive press views asbestos as a social problem. A divergence in the perspective for emphasizing the issues of asbestos between the conservative press and progressive press was also found. Social perspective influences the main topics of news stories. Thus, the patients' uneasiness and pain are not presented by both sources of media. In addition, topics differ between news media sources based on partisanship, and therefore cause divergence in readers' framing. The method of text analysis and its strengths and weaknesses are explained, and an application for the teaching and researching of machine learning in medical education using the methodology of text analysis is considered. An educational method of machine learning in medical education is urgent for future generations.
Asbestos ; Education, Medical ; Humans ; Korea ; Machine Learning ; Methods ; Social Problems ; Social Responsibility