Background: Health professionals face significant psychological burdens including burnout, anxiety, and depression. These can negatively impact their well-being and patient care. Traditional psychological health interventions often encounter limitations such as a lack of accessibility and privacy. Artificial intelligence (AI) chatbots are being explored as potential solutions to these challenges, offering available and immediate support. Therefore, it is necessary to systematically evaluate the characteristics and effectiveness of AI chatbots designed specifically for health professionals. Objective: This scoping review aims to evaluate the existing literature on the use of AI chatbots for psychological health support among health professionals. Methods: Following Arksey and O’Malley’s framework, a comprehensive literature search was conducted across eight databases, covering studies published before 2024, including backward and forward citation tracking and manual searching from the included studies. Studies were screened for relevance based on inclusion and exclusion criteria, among 2465 studies retrieved, 10 studies met the criteria for review. Results: Among the 10 studies, six chatbots were delivered via mobile platforms, and four via web-based platforms, all enabling one-on-one interactions. Natural language processing algorithms were used in six studies and cognitive behavioral therapy techniques were applied to psychological health in four studies. Usability was evaluated in six studies through participant feedback and engagement metrics. Improvements in anxiety, depression, and burnout were observed in four studies, although one reported an increase in depressive symptoms. Conclusions: AI chatbots show potential tools to support the psychological health of health professionals by offering personalized and accessible interventions. Nonetheless, further research is required to establish standardized protocols and validate the effectiveness of these interventions. Future studies should focus on refining chatbot designs and assessing their impact on diverse health professionals.

Background/Objectives: Burnout, caused by chronic workplace stress, encompasses emotional exhaustion, depersonalization, and low personal accomplishment. Over half of healthcare professionals experience burnout, leading to increased turnover. Third-wave cognitive behavioral therapies are effective in managing burnout. This study aimed to synthesize existing evidence on third-wave cognitive behavioral therapies and evaluate its effectiveness in reducing burnout among healthcare professionals. Methods: We systematically searched eight databases for studies published through May 2024. The eligible studies included randomized controlled trials and quasi-experimental designs evaluating third-wave cognitive behavioral therapies on burnout in healthcare professionals. A meta-analysis was conducted by applying fixed- and random-effects models. Results: A total of 5005 records were identified, of which 29 were reviewed and summarized, and 11 were included in the meta-analysis. Most interventions utilized mindfulness-based techniques (n = 26) with delivery methods including on-site (n = 16), online (n = 12), and hybrid (n = 1) formats. Third-wave cognitive behavioral therapies significantly reduced emotional exhaustion (standardized mean difference [SMD] = −0.686, confidence interval [CI] = −1.237, −0.136, p = 0.0145, I² = 92.5%) and depersonalization (SMD = −0.529, CI = −0.975, −0.083, p = 0.0202, I² = 89.3%), but the effect on low personal accomplishment was not statistically significant (SMD = 0.311, 95% CI = −0.319 to 0.941, p = 0.3338, I² = 89.4%). Conclusions: Third-wave cognitive behavioral therapies should be implemented to reduce emotional exhaustion and depersonalization; future research should target low personal accomplishment and explore approaches beyond mindfulness.

BACKGROUND Health professionals face significant psychological burdens including burnout, anxiety, and depression, which can negatively impact their well-being and patient care. Traditional psychological health interventions often encounter limitations such as a lack of accessibility and privacy. Artificial intelligence (AI) chatbots are emerging as promising solutions to these challenges by providing accessible and immediate support. Therefore, it is necessary to systematically evaluate the characteristics and effectiveness of AI chatbots designed specifically for health professionals. OBJECTIVE This scoping review aims to evaluate the existing literature on the use of AI chatbots for psychological health support among health professionals. METHODS Following Arksey and O’Malley’s framework, a comprehensive literature search was conducted across eight databases, covering articles published from 2018 to 2024, including backward and forward citation tracking and manual searching from the included articles. Articles were screened for relevance based on inclusion and exclusion criteria, among 2,465 articles retrieved, 10 met the criteria for review. RESULTS Among the 10 articles, three employed a development study design. Six chatbots were delivered via mobile platforms, and four via web-based platforms, all enabling one-on-one interactions. Natural language processing (NLP) algorithms were commonly used and cognitive behavioral therapy (CBT) techniques were applied to psychological health in four articles. Usability was evaluated in six articles, with generally high satisfaction rates; however, dropout rates remained high, particularly in emotionally intense situations. Improvements in anxiety, depression, and burnout were observed in four studies, although one reported an increase in depressive symptoms. CONCLUSIONS AI chatbots show promise as tools to support the psychological health of health professionals by offering personalized and accessible interventions. Nonetheless, further research is required to establish standardized protocols and validate the long-term effectiveness of these interventions. Future studies should focus on refining chatbot designs and assessing their impact on diverse health professionals.

Background: This study aimed to understand the attributes and concepts of role conflict among nurses.Methods: Using Walker & Avant’s (2014) concept analysis framework, a literature search was conducted in the PubMed, cumulative index to nursing and allied health literature (CINAHL), PsycINFO®, regional information sharing systems (RISS), and DBpia databases using the search terms ‘Nurse’ or ‘Nursing’ and ‘Role conflict’ or ‘Role ambiguity’ or ‘Role strain.’ A total of 55 articles were included in this study.Results: Role conflict among nurses was defined as conflicts occurring across various dimensions, including individual, interpersonal, role- and system-related. The antecedents of role conflict included excessive workload, job stress, and manpower shortages. The consequences of role conflict included lower quality of nursing care, emotional burnout, and increased turnover.Conclusions: This concept analysis provides a clear definition of role conflict among nurses, enhances understanding of the concept, and provides foundational data for nursing practice and systems.

Purpose: The purpose of this study was to understand the concept of metacognition and related attributes in nursing students. Methods: This study used the concept analysis framework of Walker & Avant (2014). A literature search was conducted using the PubMed, CINAHL, EBSCOhost, RISS, and DBpia databases with the keywords “nursing student” and “metacognition.” In total, 49 articles were selected for this study. Results: Metacognition in nursing students is defined as understanding the acquired knowledge, environment, and self-awareness among nursing students. The antecedents of metacognition in nursing students include the teaching and learning method and personal attributes. The consequences of metacognition in nursing students are positive self-assessment, improved learning achievement, and improved practice ability. Conclusion: This concept analysis provides a definition of metacognition in nursing students. It will support a better understanding of the concept and provide basic data for nursing education and practice.

Purpose: This study aimed to compare the physical activity, psychological health, and food group intake of women in their first trimester of pregnancy before and during the coronavirus disease 2019 (COVID-19) pandemic, and to assess these differences according to annual income levels.Methods: Using pregnancy survey data from the Korea Nurses’ Health Study, we analyzed 800 cases (381 pre-COVID-19, 419 COVID-19) for physical activity and psychological health, and 592 cases (296 pre-COVID-19, 296 COVID-19) for food group intake. Data were matched using 1:1 propensity score matching. Physical activity, psychological health, and food group intake were compared between periods and income groups.Results: During the pandemic, cycling (t=1.48, p=.003), aerobic exercise (t=0.98, p=.046), and light exercise (t=3.91, p<.001) increased while swimming decreased (t=–1.81, p<.001). Lower-income groups showed decreased running (t=–1.50, p=.004), swimming (t=–1.76, p<.001), and aerobic exercise (t=–1.10, p=.042), while higher-income groups showed increased participation in various physical activities. Depression scores decreased significantly in the lower-income group (t=–1.22, p=.022). Regarding food group intake, consumption of soups, stews (t=–1.63, p=.018), vegetables, kimchi, seaweed, and root (t=–1.60, p=.044) decreased during the pandemic. Lower-income groups showed decreased vegetable consumption (t=–1.43, p=.026) and alcohol intake (t=–1.34, p=.039), while higher-income groups showed increased alcohol consumption (t=1.29, p=.010).Conclusion: The COVID-19 pandemic differently affected health behaviors in early pregnancy based on income levels, highlighting socioeconomic disparities in maintaining healthy behaviors during public health crises. These findings suggest the need for tailored interventions considering income levels when promoting health behaviors among pregnant women during future public health crises.