Investigating Psychological Differences between Nurses and Other Healthcare Workers from Asia-Pacific Region during the Early Phase of the Coronavirus Disease 2019 (COVID-19): A Machine Learning Approach
ABSTRACT
Background:
As the pandemic evolves, frontline work challenges continue to impose significant psychological impact on nurses. However, there is a lack of data how nurses fared compared to other healthcare workers in Asia-Pacific region.
Objective:
This study aims to investigate 1) psychological differences between nurses, doctor and non-medical healthcare workers, and 2) psychological outcome characteristics of nurses from different Asia-Pacific countries.
Methods:
Decision-tree type machine learning models (LIghtGBM, Gradientboost, and RandomForest) were adopted to predict psychological impact on nurses. The SHAP (SHapley Additive exPlanations) values of these models were extracted to identify the distinctive psychological distress characteristic.
Results:
Nurses had relatively higher percentages of normal or no-change in psychological distress symptoms relative to other healthcare workers (86.3% - 96.8% vs 80.7% - 92.3%). Among those without psychological symptoms, nurses constituted a higher proportion than doctors and non-medical healthcare workers (40.8%, 25.8%, and 33.4%, respectively).
Conclusions:
Different contexts, cultures, and points in pandemic curve may have contributed to differing patterns of psychological outcomes amongst nurses in various Asia-Pacific countries. It is important that all healthcare workers practise self-care and render peer support to bolster psychological resilience for effective coping. Clinical Trial: Not applicable
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