ArticleFrontiers in public health2022
Predicting the behavioral intentions of hospice and palliative care providers from real-world data using supervised learning: A cross-sectional survey study.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Associations Between Short Video Exposure, Empathy and Attitudes Toward End-Of-Life Care Among Nursing Students: A Cross-Sectional Study.Nursing open · 2026Article
- Review
- Exploring Spiritual Care Competence Among Geriatric Nurses: A Cross-Sectional Study with a Multicultural Perspective from China.Journal of religion and health · 2026Article
- Disparities in hospice and palliative care services: evidence of healthcare provider practice in various regions of China.BMC public health · 2025Article
- Artificial intelligence-based approaches for advance care planning: a scoping review.BMC palliative care · 2025Article
- Attitudes and influencing factors of nursing assistants towards hospice and palliative care nursing in chinese nursing homes: a cross-sectional study.BMC palliative care · 2023Article
- Machine learning models in post-stroke aphasia: a scoping review.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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