Omranian Samaneh, He Lu, Talsma AkkeNeel, Scoglio Arielle A J, McRoy Susan, Rich-Edwards Janet W
Brigham and Women's Hospital and Harvard Medical School, Harvard University, Boston, MA, United States.
College of Engineering and Applied Science, University of Wisconsin-Milwaukee, 3200 Cramer Street, Milwaukee, WI, 53211, United States, 1 4142294000.
JMIR Nurs. 2025 Jul 4;8:e73672. doi: 10.2196/73672.
Burnout among health care workers affects their well-being and decision-making, influencing patient and public health outcomes. Health care workers' health beliefs and COVID-19 vaccine decisions may affect the risks of burnout. Therefore, understanding the interplay between these crucial factors is essential for identifying at-risk staff, providing targeted support, and addressing workplace challenges to prevent further escalation of burnout-related issues.
This study examines how burnout is impacted by health beliefs and COVID-19 vaccine decisions among health care workers. Building on our previously developed Health Belief Model (HBM) classifier based on the HBM framework, which explains how individual perceptions of health risks and benefits influence behavior, we focused on key HBM constructs, including the perceived severity of COVID-19, perceived barriers to vaccination, and their relationship to burnout. We aim to leverage natural language processing techniques to automatically identify theoretically grounded burnout symptoms from comments authored by nurses in a large-scale, national survey and assess their associations with vaccine hesitancy and health beliefs.
We analyzed 1944 open-ended comments written by 1501 vaccine-hesitant nurses, using data from the Nurses' Health Study surveys. We fine-tuned LLaMA 3, an open-source large language model with few-shot prompts and enhanced performance with structured annotation guidance and reasoning-aware inference. Comments were classified into burnout dimensions-Emotional Exhaustion, Depersonalization, and Inefficacy-based on the Maslach Burnout Inventory framework.
The model achieved a high weighted accuracy of 92% and an F1-score of 91% for Depersonalization. Emotional Exhaustion was identified in 52% (1003/1944) of comments, correlating strongly with perceived severity (189/323, 59%) and barriers to vaccination (281/650, 43%). Demographic analyses revealed significant variations in burnout prevalence, with older age groups reporting greater burnout.
This study highlights the relationship between burnout and vaccine decision-making among health care workers, uncovering areas for further exploration. By exploring the complex interplay between psychological strain and vaccine hesitancy, this study sets the stage for developing transformative interventions and policies that could redefine workforce resilience and public health strategies.
医护人员的职业倦怠会影响他们的幸福感和决策,进而影响患者和公众的健康结果。医护人员的健康信念和新冠疫苗接种决策可能会影响职业倦怠的风险。因此,了解这些关键因素之间的相互作用对于识别高危员工、提供有针对性的支持以及应对工作场所挑战以防止职业倦怠相关问题进一步升级至关重要。
本研究探讨医护人员的健康信念和新冠疫苗接种决策如何影响职业倦怠。基于我们先前基于健康信念模型(HBM)框架开发的HBM分类器,该模型解释了个人对健康风险和益处的认知如何影响行为,我们聚焦于HBM的关键构成要素,包括对新冠病毒严重程度的认知、疫苗接种的感知障碍及其与职业倦怠的关系。我们旨在利用自然语言处理技术,从一项大规模全国性调查中护士撰写的评论中自动识别基于理论的职业倦怠症状,并评估它们与疫苗犹豫和健康信念的关联。
我们使用护士健康研究调查的数据,分析了1501名有疫苗犹豫情绪的护士撰写的1944条开放式评论。我们对LLaMA 3进行了微调,这是一个开源的大语言模型,采用少样本提示,并通过结构化注释指导和推理感知推理提高了性能。根据马氏职业倦怠量表框架,将评论分为职业倦怠维度——情感耗竭、去个性化和无效能感。
该模型在去个性化方面实现了92%的高加权准确率和91%的F1分数。在52%(1003/1944)的评论中识别出了情感耗竭,与感知严重程度(189/323,59%)和疫苗接种障碍(281/6,50,43%)密切相关。人口统计学分析显示职业倦怠患病率存在显著差异,年龄较大的群体报告的职业倦怠程度更高。
本研究突出了医护人员职业倦怠与疫苗接种决策之间的关系,揭示了有待进一步探索的领域。通过探索心理压力与疫苗犹豫之间的复杂相互作用,本研究为制定变革性干预措施和政策奠定了基础,这些措施和政策可能会重新定义劳动力的复原力和公共卫生策略。