AUTHOR=Chu Tianshu , Zhang Huiwen , Xu Yifan , Teng Xiaohan , Jing Limei 

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

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.927874

DOI=10.3389/fpubh.2022.927874

ISSN=2296-2565

ABSTRACT=Background: Hospice and Palliative Care (HPC) aims to improve end-of-life quality and has caught much more attention under the background of population aging and 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 and barriers to excavating potential human resources and improving HPC accessibility.
Methods: 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 predicting individual behavioral intentions.
Results: 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 encourage behavioral intentions. Continuous practice could as well improve individual confidence and willingness to provide HPC. In addition, scientific knowledge and related skills were the foundation of implementing HPC.
Conclusion: 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.