Dong YanHong, Yeo Mei Chun, Tham Xiang Cong, Danuaji Rivan, Nguyen Thang H, Sharma Arvind K, Rn Komalkumar, Pv Meenakshi, Tai Mei-Ling Sharon, Ahmad Aftab, Tan Benjamin Yq, Ho Roger C, Chua Matthew Chin Heng, Sharma Vijay K
Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Institute of Systems Science, National University of Singapore, Singapore, Singapore.
JMIR Nurs. 2022 Jun 1;5(1):e32647. doi: 10.2196/32647.
As the COVID-19 pandemic evolves, challenges in frontline work continue to impose a significant psychological impact on nurses. However, there is a lack of data on how nurses fared compared to other health care workers in the Asia-Pacific region.
This study aims to investigate (1) the psychological outcome characteristics of nurses in different Asia-Pacific countries and (2) psychological differences between nurses, doctors, and nonmedical health care workers.
Exploratory data analysis and visualization were conducted on the data collected through surveys. A machine learning modeling approach was adopted to further discern the key psychological characteristics differentiating nurses from other health care workers. Decision tree-based machine learning models (Light Gradient Boosting Machine, GradientBoost, and RandomForest) were built to predict whether a set of psychological distress characteristics (ie, depression, anxiety, stress, intrusion, avoidance, and hyperarousal) belong to a nurse. Shapley Additive Explanation (SHAP) values were extracted to identify the prominent characteristics of each of these models. The common prominent characteristic among these models is akin to the most distinctive psychological characteristic that differentiates nurses from other health care workers.
Nurses had relatively higher percentages of having normal or unchanged psychological distress symptoms relative to other health care workers (n=233-260 [86.0%-95.9%] vs n=187-199 [74.8%-91.7%]). Among those without psychological symptoms, nurses constituted a higher proportion than doctors and nonmedical health care workers (n=194 [40.2%], n=142 [29.5%], and n=146 [30.3%], respectively). Nurses in Vietnam showed the highest level of depression, stress, intrusion, avoidance, and hyperarousal symptoms compared to those in Singapore, Malaysia, and Indonesia. Nurses in Singapore had the highest level of anxiety. In addition, nurses had the lowest level of stress, which is the most distinctive psychological outcome characteristic derived from machine learning models, compared to other health care workers. Data for India were excluded from the analysis due to the differing psychological response pattern observed in nurses in India. A large number of female nurses emigrating from South India could not have psychologically coped well without the support from family members while living alone in other states.
Nurses were least psychologically affected compared to doctors and other health care workers. Different contexts, cultures, and points in the pandemic curve may have contributed to differing patterns of psychological outcomes amongst nurses in various Asia-Pacific countries. It is important that all health care workers practice self-care and render peer support to bolster psychological resilience for effective coping. In addition, this study also demonstrated the potential use of decision tree-based machine learning models and SHAP value plots in identifying contributing factors of sophisticated problems in the health care industry.
随着新冠疫情的演变，一线工作中的挑战继续对护士造成重大心理影响。然而，在亚太地区，与其他医护人员相比，护士的情况如何，目前缺乏相关数据。
本研究旨在调查（1）亚太地区不同国家护士的心理结果特征，以及（2）护士、医生和非医疗医护人员之间的心理差异。
对通过调查收集的数据进行探索性数据分析和可视化。采用机器学习建模方法，进一步辨别区分护士与其他医护人员的关键心理特征。构建基于决策树的机器学习模型（轻梯度提升机、梯度提升和随机森林），以预测一组心理困扰特征（即抑郁、焦虑、压力、侵入、回避和过度警觉）是否属于护士。提取夏普利值（SHAP值）以识别每个模型的突出特征。这些模型中共同的突出特征类似于区分护士与其他医护人员的最显著心理特征。
与其他医护人员相比，护士出现正常或未改变心理困扰症状的比例相对较高（n = 233 - 260 [86.0% - 95.9%] 对 n = 187 - 199 [74.8% - 91.7%]）。在没有心理症状的人群中，护士所占比例高于医生和非医疗医护人员（分别为n = 194 [40.2%]、n = 142 [29.5%] 和 n = 146 [30.3%]）。与新加坡、马来西亚和印度尼西亚的护士相比，越南护士的抑郁、压力、侵入、回避和过度警觉症状水平最高。新加坡护士的焦虑水平最高。此外，与其他医护人员相比，护士的压力水平最低，这是机器学习模型得出的最显著心理结果特征。由于观察到印度护士的心理反应模式不同，印度的数据被排除在分析之外。大量从印度南部移民的女性护士，在独自生活在其他邦时，如果没有家人的支持，心理上可能无法很好地应对。
与医生和其他医护人员相比，护士受到的心理影响最小。不同的背景、文化以及疫情曲线的不同阶段，可能导致亚太地区各国护士心理结果模式存在差异。所有医护人员进行自我关怀并提供同伴支持以增强心理韧性从而有效应对，这一点很重要。此外，本研究还展示了基于决策树的机器学习模型和SHAP值图在识别医疗行业复杂问题影响因素方面的潜在用途。
