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.

The Potential of Chatbots for Emotional Support and Promoting Mental Well-Being in Different Cultures: Mixed Methods Study.

BACKGROUND: Artificial intelligence chatbot research has focused on technical advances in natural language processing and validating the effectiveness of human-machine conversations in specific settings. However, real-world chat data remain proprietary and unexplored despite their growing popularity, and new analyses of chatbot uses and their effects on mitigating negative moods are urgently needed. OBJECTIVE: In this study, we investigated whether and how artificial intelligence chatbots facilitate the expression of user emotions, specifically sadness and depression. We also examined cultural differences in the expression of depressive moods among users in Western and Eastern countries. METHODS: This study used SimSimi, a global open-domain social chatbot, to analyze 152,783 conversation utterances containing the terms "depress" and "sad" in 3 Western countries (Canada, the United Kingdom, and the United States) and 5 Eastern countries (Indonesia, India, Malaysia, the Philippines, and Thailand). Study 1 reports new findings on the cultural differences in how people talk about depression and sadness to chatbots based on Linguistic Inquiry and Word Count and n-gram analyses. In study 2, we classified chat conversations into predefined topics using semisupervised classification techniques to better understand the types of depressive moods prevalent in chats. We then identified the distinguishing features of chat-based depressive discourse data and the disparity between Eastern and Western users. RESULTS: Our data revealed intriguing cultural differences. Chatbot users in Eastern countries indicated stronger emotions about depression than users in Western countries (positive: P<.001; negative: P=.01); for example, Eastern users used more words associated with sadness (P=.01). However, Western users were more likely to share vulnerable topics such as mental health (P<.001), and this group also had a greater tendency to discuss sensitive topics such as swear words (P<.001) and death (P<.001). In addition, when talking to chatbots, people expressed their depressive moods differently than on other platforms. Users were more open to expressing emotional vulnerability related to depressive or sad moods to chatbots (74,045/148,590, 49.83%) than on social media (149/1978, 7.53%). Chatbot conversations tended not to broach topics that require social support from others, such as seeking advice on daily life difficulties, unlike on social media. However, chatbot users acted in anticipation of conversational agents that exhibit active listening skills and foster a safe space where they can openly share emotional states such as sadness or depression. CONCLUSIONS: The findings highlight the potential of chatbot-assisted mental health support, emphasizing the importance of continued technical and policy-wise efforts to improve chatbot interactions for those in need of emotional assistance. Our data indicate the possibility of chatbots providing helpful information about depressive moods, especially for users who have difficulty communicating emotions to other humans.

Factors influencing the burnout dimensions among nurses: A cross-sectional study in South Korea.

AIM: To investigate the factors influencing three dimensions of burnout among clinical nurses in South Korea. DESIGN: Descriptive, correlational and cross-sectional study. METHODS: We recruited 300 clinical nurses to participate in an online survey conducted in June 2021. Purposive sampling was used for the survey. Multiple linear regression was used for data analysis. RESULTS: The regression models explained 55.8%, 45.8% and 34.3% variances in emotional exhaustion, depersonalization and personal accomplishment respectively. Emotional exhaustion was influenced by experience in the current department, working overtime, shift type, depression, job stress and emotional labour. Depersonalization was influenced by gender, hospital size, the associated department, overtime, coping strategies, depression and emotional labour. Personal accomplishment was influenced by clinical experience, shift type, resilience and the perceived threat of coronavirus disease 2019.

Symptoms and management of long COVID: A scoping review.

AIM: This scoping review aims to describe published work on the symptoms and management of long COVID conditions. BACKGROUND: Symptoms and management of COVID-19 have focused on the acute stage. However, long-term consequences have also been observed. METHODS: A scoping review was performed based on the framework suggested by Arksey and O'Malley. We conducted a literature search to retrieve articles published from May 2020 to March 2021 in CINHAL, Cochrane library, Embase, PubMed and Web of science, including backward and forward citation tracking from the included articles. Among the 1880 articles retrieved, 34 articles met our criteria for review: 21 were related to symptom presentation and 13 to the management of long COVID. RESULTS: Long COVID symptoms were described in 21 articles. Following COVID-19 treatment, hospitalised patients most frequently reported dyspnoea, followed by anosmia/ageusia, fatigue and cough, while non-hospitalised patients commonly reported cough, followed by fever and myalgia/arthralgia. Thirteen studies described management for long COVID: Focused on a multidisciplinary approach in seven articles, pulmonary rehabilitation in three articles, fatigue management in two articles and psychological therapy in one study. CONCLUSION: People experience varied COVID-19 symptoms after treatment. However, guidelines on evidence-based, multidisciplinary management for long COVID conditions are limited in the literature. The COVID-19 pandemic may extend due to virus mutations; therefore, it is crucial to develop and disseminate evidence-based, multidisciplinary management guidelines. RELEVANCE TO CLINICAL PRACTICE: A rehabilitation care plan and community healthcare plans are necessary for COVID-19 patients before discharge. Remote programmes could facilitate the monitoring and screening of people with long COVID.