Predicting the behavioral intentions of hospice and palliative care providers from real-world data using supervised learning: A cross-sectional survey study.
Front Public Health
; 10: 927874, 2022.
Article
in English
| MEDLINE | ID: covidwho-2080283
ABSTRACT
Background:
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.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 predict 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 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.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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Hospice Care
/
Hospices
Type of study:
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Aged
/
Humans
Language:
English
Journal:
Front Public Health
Year:
2022
Document Type:
Article
Affiliation country:
Fpubh.2022.927874
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