在COVID-19疫苗决策和健康信念的背景下,使用大型语言模型评估医护人员的职业倦怠:回顾性队列研究

JMIR nursing Pub Date : 2025-07-04 DOI:10.2196/73672
Samaneh Omranian, Lu He, AkkeNeel Talsma, Arielle A J Scoglio, Susan McRoy, Janet W Rich-Edwards
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引用次数: 0

摘要

背景:卫生保健工作者的职业倦怠影响他们的幸福感和决策,影响患者和公共卫生结果。卫生保健工作者的健康信念和COVID-19疫苗决策可能会影响倦怠风险。因此,了解这些关键因素之间的相互作用对于识别有风险的员工,提供有针对性的支持,解决工作场所的挑战,防止倦怠相关问题的进一步升级至关重要。目的:本研究探讨卫生保健工作者健康信念和COVID-19疫苗决策对职业倦怠的影响。基于我们之前开发的基于HBM框架的健康信念模型(HBM)分类器,该分类器解释了个人对健康风险和益处的看法如何影响行为,我们将重点放在关键的HBM结构上,包括COVID-19的感知严重程度,疫苗接种的感知障碍及其与倦怠的关系。我们的目标是利用自然语言处理技术,从大规模的全国调查中护士撰写的评论中自动识别理论上有根据的倦怠症状,并评估其与疫苗犹豫和健康信念的关联。方法:我们使用护士健康研究调查的数据,分析了1501名疫苗犹豫护士撰写的1944份开放式评论。我们对LLaMA 3进行了微调,它是一个开源的大型语言模型,具有较少的提示,并通过结构化注释指导和推理感知推理增强了性能。基于Maslach倦怠量表框架,将评价分为情绪耗竭、人格解体和效率低下三个维度。结果:该模型获得了92%的高加权准确率和91%的去人格化f1得分。在52%(1003/1944)的评论中发现了情绪衰竭,与感知到的严重程度(189/323,59%)和疫苗接种障碍(281/650,43%)密切相关。人口统计分析揭示了倦怠患病率的显著差异,年龄越大的年龄组报告的倦怠程度越高。结论:本研究突出了医护人员职业倦怠与疫苗接种决策的关系,揭示了进一步探索的领域。通过探索心理压力和疫苗犹豫之间复杂的相互作用,本研究为制定变革性干预措施和政策奠定了基础,这些干预措施和政策可以重新定义劳动力弹性和公共卫生战略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Large Language Models to Assess Burnout Among Health Care Workers in the Context of COVID-19 Vaccine Decisions and Health Beliefs: Retrospective Cohort Study.

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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