In this study, we examined the changes in the demographic characteristics of foreign residents in Japan (FRJ) and the current status of FRJ from a global health perspective. We also considered child maltreatment that occurred in FRJ families and language problems in child welfare. Japan's official statistics in the end of 2017 indicated that there were more than 2.56 million FRJ from over 190 countries. This population was diverse with heterogeneous characteristics, such as age structure, dwelling place, marital status, and childbirth. At the end of 2017, there were 219,982 FRJ children aged 0-14 of various nationalities, including Chinese, Brazilian, South Korean, North Korean, Filipino, Vietnamese, Peruvian, Nepalese, and Indian. In 2010, we conducted our first survey of child maltreatment in FRJ families, targeting 219 child protection centers across Japan. Between April 2007 and August 2010, 1,639 child maltreatment cases were reported from 56% of these centers. Details of 1,111 cases were collected and descriptive analyses were conducted. The male-to-female ratio was 0.88 and the median age was 8 years: however, the age distribution showed that females were significantly older than males (P < 0.01). The proportions of physical abuse, child neglect, emotional abuse, and sexual abuse were 38%, 33%, 21%, and 7%, respectively. Native language problems created numerous challenges and required a large amount of effort from child welfare practitioners. However, most solutions to identified problems were still at the beginning stage and some were found to be ineffective. More interdisciplinary and integrated researches are needed targeting child welfare of FRJ. An ethical framework for good counseling practices should be developed. Key words: foreign residents in Japan(在日外国人) ,native language(母語) ,child maltreatment(児童虐 待/マルトリートメント) , child protection(子ども保護) , descriptive epidemiology(記述疫学)
The study suggests that elderly immigrants need day care support that provides an environment where they can enjoy their culture.
Aim This study aimed to construct and evaluate prediction models using deep learning to explore the impact of attributes and lifestyle factors on research activities of nursing researchers during the COVID‐19 pandemic. Methods A secondary data analysis was conducted from a cross‐sectional online survey by the Japanese Society of Nursing Science at the inception of the COVID‐19 pandemic. A total of 1089 respondents from nursing faculties were divided into a training dataset and a test dataset. We constructed two prediction models with the training dataset using artificial intelligence (AI) predictive analysis tools; motivation and time were used as predictor items for negative impact on research activities. Predictive factors were attributes, lifestyle, and predictor items for each other. The models' accuracy and internal validity were evaluated using an ordinal logistic regression analysis to assess goodness‐of‐fit; the test dataset was used to assess external validity. Predicted contributions by each factor were also calculated. Results The models' accuracy and goodness‐of‐fit were good. The prediction contribution analysis showed that no increase in research motivation and lack of increase in research time strongly influenced each other. Other factors that negatively influenced research motivation and research time were residing outside the special alert area and lecturer position and living with partner/spouse and associate professor position, respectively. Conclusions Deep learning is a research method enabling early prediction of unexpected events, suggesting new applicability in nursing science. To continue research activities during the COVID‐19 pandemic and future contingencies, the research environment needs to be improved, workload corrected by position, and considered in terms of work‐life balance.
Aim Patients with severe heart failure undergo highly invasive and advanced therapies with uncertain treatment outcomes. For these patients, shared decision-making is necessary. To date, the nursing perspective of the decision-making process for patients facing difficulties and how nurses can support patients in this process have not been fully elucidated. This study aimed to clarify the perceptions of critical care nurses regarding situations with patients with severe heart failure that require difficult decision-making, and their role in supporting these patients. Methods Individual semi-structured interviews were conducted with 10 certified nurse specialists in critical care nursing at nine hospitals in Japan. A qualitative inductive method was used and the derived relationships among the themes were visually structured and represented. Results The nurses’ perceptions on patients’ difficult situations in decision-making were identified as follows: painful decisions under uncertainties; tense relationships; wavering emotions during decision-making; difficulties in coping with worsening medical conditions; patients’ wishes that are difficult to realize or estimate; and difficulties in transitioning from advanced medical care. Critical care nurses’ roles were summarized into six themes and performed collaboratively within the nursing team. Of these, the search for meaning and value was fundamental. Two positions underpin the role of critical care nurses. The first aims to provide direct support and includes partnerships and rights advocacy. The second aims to provide a holistic perspective to enable necessary adjustments, as indicated by situation assessments and mediation. By crossing various boundaries, co-creating, and forming a good circular relationship in the search for meaning and values, the possibility of expanding treatment and recuperation options may be considered. Conclusions Patients with severe heart failure have difficulty participating in shared decision-making. Critical care nurses should collaborate within the nursing team to improve interprofessional shared decision-making by providing decisional support to patients that focuses on values and meaning.
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