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Acceptance level of advance care planning and its associated factors among the public: A nationwide survey.
- Source :
-
BMC Palliative Care . 8/6/2024, Vol. 23 Issue 1, p1-11. 11p. - Publication Year :
- 2024
-
Abstract
- Background: Advance care planning (ACP) can contribute to individuals making decisions about their healthcare preferences in advance of serious illness. Up to now, the acceptance level and associated factors of ACP among the public in China remain unclear. This study aims to investigate the acceptance level of ACP in China and identify factors associated with it based on the socioecological model. Methods: A total of 19,738 participants were included in this survey. We employed a random forest regression analysis to select factors derived from the socioecological model. Multivariate generalized linear model analysis was then conducted to explore the factors that were associated with the acceptance level of ACP. Results: On a scale ranging from 0 to 100, the median score for acceptance level of ACP was 64.00 (IQR: 48.00–83.00) points. The results of the multivariate generalized linear model analysis revealed that participants who scored higher on measures of openness and neuroticism personality traits, as well as those who had greater perceptions of social support, higher levels of health literacy, better neighborly relationships, family health, and family social status, were more likely to accept ACP. Conversely, participants who reported higher levels of subjective well-being and greater family communication levels demonstrated a lower likelihood of accepting ACP. Conclusions: This study identified multiple factors associated with the acceptance level of ACP. The findings offer valuable insights that can inform the design and implementation of targeted interventions aimed at facilitating a good death and may have significant implications for the formulation of end-of-life care policies and practices in other countries facing similar challenges. [ABSTRACT FROM AUTHOR]
- Subjects :
- *HEALTH literacy
*RANDOM forest algorithms
*PALLIATIVE treatment
*SELF-efficacy
*RESEARCH funding
*QUESTIONNAIRES
*PERSONALITY assessment
*MULTIVARIATE analysis
*DESCRIPTIVE statistics
*SURVEYS
*SOCIAL status
*MATHEMATICAL models
*PATIENT decision making
*PUBLIC health
*THEORY
*SOCIAL support
*DATA analysis software
*ADVANCE directives (Medical care)
*REGRESSION analysis
Subjects
Details
- Language :
- English
- ISSN :
- 1472684X
- Volume :
- 23
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- BMC Palliative Care
- Publication Type :
- Academic Journal
- Accession number :
- 178855808
- Full Text :
- https://doi.org/10.1186/s12904-024-01533-0