Nurses' media competency: A concept analysis.

OBJECTIVES: To analyze and define the concept of nurses' media competency. DESIGN: Concept analysis. DATA SOURCES: We conducted a literature search in PubMed, CINAHL, PsycInfo, and RISS International, as well as a hand-search, for relevant articles published between January 1990 and April 2023. A total of 57 articles related to nurses' media competency, published in English or Korean, were included in this study. REVIEW METHODS: We used Walker and Avant's method of concept analysis to identify the defining attributes, antecedents, and consequences of the concept. RESULTS: The seven defining attributes of nurses' media competency were networking with members of the media, selecting the appropriate media platform, considering that the target audience is the public, creating one's own media products, delivering intended messages through the media, monitoring and responding to the media, and maintaining professional dignity. Antecedents of the concept included cultivation of desirable nursing professionalism, critical evaluation of policies and issues, recognition of media influence, establishment of education and guidelines for media use, and having facilities and availability of technologies for media use. Consequences of the concept were improved influence of individual nurses, enhanced public awareness of the nursing profession, stronger networks and collaboration among professionals, and contributions to the promotion of public health. CONCLUSIONS: This concept analysis presents a theoretical definition of nurses' media competency that can provide guidance on how to educate nurses to develop media competency and how to measure nurses' media competency.

Korea Nurses' Health Study and the health of reproductive-aged women: a cohort profile.

The Korea Nurses' Health Study (KNHS) is an ongoing, large-scale, prospective cohort study of female nurses, focusing on the effects of occupational, environmental, and lifestyle factors on the health of women. The first KNHS survey was performed in 2013-2014 (n=20,613). As of December 2023, 11 follow-up surveys have been conducted. Participants who were pregnant were asked to participate in the early pregnancy survey (n=2,179) and postpartum survey after giving birth (n=2,790). The main variables included sociodemographic, work-related, lifestyle, physical, mental, and women's health factors. Blood, urine, and toenail samples were collected from a participant subgroup of the first survey (n=1,983). The subgroups of the second survey completed a food frequency questionnaire in 2019 (n=300) and 2021 (n=871). In 2020, a subgroup of the first survey answered a coronavirus disease 2019 (COVID-19)-related survey (n=975). To examine various health-related factors in young adults, new participants were added to the KNHS cohort in the 11th (n=1,000) and 12th (n=1,002) surveys. The KNHS cohort will help identify health and illness determinants in Korean women. Data can be accessed at https://coda.nih.go.kr/frt/index.do.

Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening.

Introduction: Sleep is an essential function to sustain a healthy life, and sleep dysfunction can cause various physical and mental issues. In particular, obstructive sleep apnea (OSA) is one of the most common sleep disorders and, if not treated in a timely manner, OSA can lead to critical problems such as hypertension or heart disease. Methods: The first crucial step in evaluating individuals' quality of sleep and diagnosing sleep disorders is to classify sleep stages using polysomnographic (PSG) data including electroencephalography (EEG). To date, such sleep stage scoring has been mainly performed manually via visual inspection by experts, which is not only a time-consuming and laborious process but also may yield subjective results. Therefore, we have developed a computational framework that enables automatic sleep stage classification utilizing the power spectral density (PSD) features of sleep EEG based on three different learning algorithms: support vector machine, k-nearest neighbors, and multilayer perceptron (MLP). In particular, we propose an integrated artificial intelligence (AI) framework to further inform the risk of OSA based on the characteristics in automatically scored sleep stages. Given the previous finding that the characteristics of sleep EEG differ by age group, we employed a strategy of training age-specific models (younger and older groups) and a general model and comparing their performance. Results: The performance of the younger age-specific group model was similar to that of the general model (and even higher than the general model at certain stages), but the performance of the older age-specific group model was rather low, suggesting that bias in individual variables, such as age bias, should be considered during model training. Our integrated model yielded an accuracy of 73% in sleep stage classification and 73% in OSA screening when MLP algorithm was applied, which indicates that patients with OSA could be screened with the corresponding accuracy level only with sleep EEG without respiration-related measures. Discussion: The current outcomes demonstrate the feasibility of AI-based computational studies that when combined with advances in wearable devices and relevant technologies could contribute to personalized medicine by not only assessing an individuals' sleep status conveniently at home but also by alerting them to the risk of sleep disorders and enabling early intervention.

Health Promotion Behavior among Older Korean Family Caregivers of People with Dementia.

People adopt health promotion behaviors to promote their health as they interact within the environment. The purpose of this study was to examine factors influencing health promotion behaviors among older adults caring for family members with dementia. For this cross-sectional study, data from 135 older adults who were the main caregivers were collected at an outpatient clinic at a university hospital in the capital city of South Korea between September and October in 2020. Sociodemographic characteristics, caregiver-related characteristics, dementia knowledge, fear of dementia, and health promotion behaviors were measured. Univariate analysis revealed that the level of health promotion behaviors differed by age, sex, educational level, monthly income, relationship with the family member with dementia, and cohabitation with family members with dementia. In the multivariate analysis, a hierarchical multiple regression model explained 33.9% of the variance. Sex, duration of caregiving, use of long-term care service, and fear of dementia predicted health promotion behavior. A strategic tailored care plan for target population is needed to improve the health promotion behavior of older adults caring for family members with dementia.

A phenome-based functional analysis of transcription factors in the cereal head blight fungus, Fusarium graminearum.

Fusarium graminearum is an important plant pathogen that causes head blight of major cereal crops. The fungus produces mycotoxins that are harmful to animal and human. In this study, a systematic analysis of 17 phenotypes of the mutants in 657 Fusarium graminearum genes encoding putative transcription factors (TFs) resulted in a database of over 11,000 phenotypes (phenome). This database provides comprehensive insights into how this cereal pathogen of global significance regulates traits important for growth, development, stress response, pathogenesis, and toxin production and how transcriptional regulations of these traits are interconnected. In-depth analysis of TFs involved in sexual development revealed that mutations causing defects in perithecia development frequently affect multiple other phenotypes, and the TFs associated with sexual development tend to be highly conserved in the fungal kingdom. Besides providing many new insights into understanding the function of F. graminearum TFs, this mutant library and phenome will be a valuable resource for characterizing the gene expression network in this fungus and serve as a reference for studying how different fungi have evolved to control various cellular processes at the transcriptional level.