Investigating Psychological Differences Between Nurses and Other Health Care Workers From the Asia-Pacific Region During the Early Phase of COVID-19: Machine Learning Approach.
YanHong DongMei Chun YeoXiang Cong ThamRivan DanuajiThang H NguyenArvind K SharmaKomalkumar RnMeenakshi PvMei-Ling Sharon TaiAftab AhmadBenjamin Yong Qiang TanRoger Chun-Man HoMatthew Chin Heng ChuaVijay Kumar SharmaPublished in: JMIR nursing (2022)
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.
Keyphrases
- healthcare
- machine learning
- mental health
- coronavirus disease
- sars cov
- social support
- artificial intelligence
- depressive symptoms
- sleep quality
- big data
- climate change
- type diabetes
- deep learning
- respiratory syndrome coronavirus
- health information
- decision making
- physical activity
- social media
- weight loss
- health insurance
- patient reported