Predicting the behavioral intentions of hospice and palliative care providers from real-world data using supervised learning: A cross-sectional survey study

Hospice and palliative care (HPC) aims to improve end-of-life quality and has received much more attention through the lens of an aging population in the midst of the coronavirus disease pandemic. However, several barriers remain in China due to a lack of professional HPC providers with positive beh...

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Bibliographic Details
Published in:Frontiers in public health 2022-09, Vol.10, p.927874-927874
Main Authors: Chu, Tianshu, Zhang, Huiwen, Xu, Yifan, Teng, Xiaohan, Jing, Limei
Format: Article
Language:English
Subjects:
Aged
behavioral intention
Cross-Sectional Studies
cross-sectional study
healthcare providers
hospice and palliative care
Hospice Care
Hospices
Humans
Intention
machine learning
Palliative Care
Public Health
random forest classifier
Supervised Machine Learning
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Summary:Hospice and palliative care (HPC) aims to improve end-of-life quality and has received much more attention through the lens of an aging population in the midst of the coronavirus disease pandemic. However, several barriers remain in China due to a lack of professional HPC providers with positive behavioral intentions. Therefore, we conducted an original study introducing machine learning to explore individual behavioral intentions and detect factors of enablers of, and barriers to, excavating potential human resources and improving HPC accessibility. A cross-sectional study was designed to investigate healthcare providers' behavioral intentions, knowledge, attitudes, and practices in hospice care (KAPHC) with an indigenized KAPHC scale. Binary Logistic Regression and Random Forest Classifier (RFC) were performed to model impacting and predict individual behavioral intentions. The RFC showed high sensitivity (accuracy = 0.75; F1 score = 0.84; recall = 0.94). Attitude could directly or indirectly improve work enthusiasm and is the most efficient approach to reveal behavioral intentions. Continuous practice could also improve individual confidence and willingness to provide HPC. In addition, scientific knowledge and related skills were the foundation of implementing HPC. Individual behavioral intention is crucial for improving HPC accessibility, particularly at the initial stage. A well-trained RFC can help estimate individual behavioral intentions to organize a productive team and promote additional policies.
ISSN:2296-2565
2296-2565
DOI:10.3389/fpubh.2022.927874