{"title":"人工智能辅助护士职业倦怠的针对性干预：一项三组随机对照试验。","authors":"Gumhee Baek, Chiyoung Cha","doi":"10.1111/wvn.70003","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>High-stress environments, heavy workloads, and the emotional demands of patient care, which are common challenges faced by nurses, are factors that can lead to burnout. Shift work can make traditional burnout interventions costly and difficult to implement. Artificial intelligence (AI) could offer solutions that are less constrained by time, resources, and labor.</p><p><strong>Aim: </strong>To investigate the effectiveness of an AI-assisted intervention in reducing nurse burnout.</p><p><strong>Methods: </strong>A single-blind, three-group, randomized controlled trial of 120 nurses (40 per group) was conducted from June 2023 to July 2023. The AI-assisted tailored intervention included two 2-week programs, delivering one of four programs to the intervention group: mindfulness meditation, acceptance commitment therapy, storytelling and reflective writing, or laughter therapy. The experimental group received tailored programs based on demographic and work-related characteristics, job stress, stress response, coping strategy, and burnout dimensions (client-related, personal, and work-related). Control Group 1 self-selected their programs, while Control Group 2 was provided with online information on burnout reduction. Primary outcomes, client-related, personal, and work-related burnout, were measured at baseline, week 2, and week 4. Secondary outcomes, job stress, stress responses, and coping strategies, were assessed at baseline and week 4. Data were analyzed using ANOVA, repeated measures ANOVA, and the Scheffé test for post hoc analysis.</p><p><strong>Results: </strong>The experimental group showed significant reductions in client-related burnout (F = 7.725, p = 0.001) and personal burnout (F = 10.967, p < 0.0001) compared to the other groups. Significant effects of time and time × group interactions were observed for client-related and personal burnout, with time effects noted for work-related burnout. Stress response reduction was highest in Control Group 1, followed by the experimental group and Control Group 2 (F = 3.07, p = 0.017).</p><p><strong>Linking evidence to action: </strong>AI algorithms could provide tailored programs to mitigate nurse burnout, particularly in client-related and personal burnout. 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引用次数: 0
摘要
背景：高压力的环境、繁重的工作量和护理病人的情感需求是护士面临的共同挑战，是导致倦怠的因素。轮班工作会使传统的倦怠干预措施成本高昂且难以实施。人工智能（AI）可以提供不受时间、资源和劳动力限制的解决方案。目的：探讨人工智能辅助干预减少护士职业倦怠的效果。方法：于2023年6月至2023年7月对120名护士（每组40人）进行单盲、三组随机对照试验。人工智能辅助的量身定制干预包括两个为期两周的项目，向干预组提供四个项目中的一个：正念冥想、接受承诺疗法、讲故事和反思性写作，或笑声疗法。实验组接受了基于人口统计学和工作相关特征、工作压力、压力反应、应对策略和倦怠维度（客户相关、个人和工作相关）的定制方案。控制组1自行选择项目，控制组2在网上提供减少倦怠的信息。在基线、第2周和第4周测量主要结果，客户相关、个人和工作相关的倦怠。次要结果，工作压力，压力反应和应对策略，在基线和第4周进行评估。数据分析采用方差分析、重复测量方差分析和事后分析的scheff检验。结果：实验组显示出与客户相关的倦怠（F = 7.725, p = 0.001）和个人倦怠（F = 10.967, p）的显著降低。将证据与行动联系起来：人工智能算法可以提供量身定制的方案来减轻护士的倦怠，特别是与客户相关的倦怠和个人倦怠。减少护士倦怠有助于提高护理质量。试验注册：本试验已在临床研究信息服务中心注册（KCT0008546）。
AI-Assisted Tailored Intervention for Nurse Burnout: A Three-Group Randomized Controlled Trial.
Background: High-stress environments, heavy workloads, and the emotional demands of patient care, which are common challenges faced by nurses, are factors that can lead to burnout. Shift work can make traditional burnout interventions costly and difficult to implement. Artificial intelligence (AI) could offer solutions that are less constrained by time, resources, and labor.
Aim: To investigate the effectiveness of an AI-assisted intervention in reducing nurse burnout.
Methods: A single-blind, three-group, randomized controlled trial of 120 nurses (40 per group) was conducted from June 2023 to July 2023. The AI-assisted tailored intervention included two 2-week programs, delivering one of four programs to the intervention group: mindfulness meditation, acceptance commitment therapy, storytelling and reflective writing, or laughter therapy. The experimental group received tailored programs based on demographic and work-related characteristics, job stress, stress response, coping strategy, and burnout dimensions (client-related, personal, and work-related). Control Group 1 self-selected their programs, while Control Group 2 was provided with online information on burnout reduction. Primary outcomes, client-related, personal, and work-related burnout, were measured at baseline, week 2, and week 4. Secondary outcomes, job stress, stress responses, and coping strategies, were assessed at baseline and week 4. Data were analyzed using ANOVA, repeated measures ANOVA, and the Scheffé test for post hoc analysis.
Results: The experimental group showed significant reductions in client-related burnout (F = 7.725, p = 0.001) and personal burnout (F = 10.967, p < 0.0001) compared to the other groups. Significant effects of time and time × group interactions were observed for client-related and personal burnout, with time effects noted for work-related burnout. Stress response reduction was highest in Control Group 1, followed by the experimental group and Control Group 2 (F = 3.07, p = 0.017).
Linking evidence to action: AI algorithms could provide tailored programs to mitigate nurse burnout, particularly in client-related and personal burnout. Reducing nurse burnout could contribute to the quality of care.
Trial registration: This trial is registered with the Clinical Research Information Service (KCT0008546).
期刊介绍：
The leading nursing society that has brought you the Journal of Nursing Scholarship is pleased to bring you Worldviews on Evidence-Based Nursing. Now publishing 6 issues per year, this peer-reviewed journal and top information resource from The Honor Society of Nursing, Sigma Theta Tau International, uniquely bridges knowledge and application, taking a global approach in its presentation of research, policy and practice, education and management, and its link to action in real world settings.
Worldviews on Evidence-Based Nursing is written especially for:
Clinicians
Researchers
Nurse leaders
Managers
Administrators
Educators
Policymakers
Worldviews on Evidence-Based Nursing is a primary source of information for using evidence-based nursing practice to improve patient care by featuring:
Knowledge synthesis articles with best practice applications and recommendations for linking evidence to action in real world practice, administra-tive, education and policy settings
Original articles and features that present large-scale studies, which challenge and develop the knowledge base about evidence-based practice in nursing and healthcare
Special features and columns with information geared to readers’ diverse roles: clinical practice, education, research, policy and administration/leadership
Commentaries about current evidence-based practice issues and developments
A forum that encourages readers to engage in an ongoing dialogue on critical issues and questions in evidence-based nursing
Reviews of the latest publications and resources on evidence-based nursing and healthcare
News about professional organizations, conferences and other activities around the world related to evidence-based nursing
Links to other global evidence-based nursing resources and organizations.
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