A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 197

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML

File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 317
Function: require_once

Understanding and Predicting End-of-Life Care Preferences Among Urban-Dwelling Older Adults in China. | LitMetric

Understanding and Predicting End-of-Life Care Preferences Among Urban-Dwelling Older Adults in China.

J Pain Symptom Manage

School of Psychology (R.J.Z.), Nanjing Normal University, Nanjing, China. Electronic address:

Published: October 2025


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Context: Understanding older adults' preferences for end-of-life care (EoLC) is vital for respecting their wishes and informing effective service planning and policy development. Previous research has examined factors influencing different dimensions of EoLC preferences separately, but few studies have explored these dimensions as interconnected patterns and viewed older adults as heterogeneous using a person-centered approach.

Objectives: This study aims to: 1) identify heterogeneous latent patterns across seven dimensions of EoLC preferences among Chinese older adults; 2) describe and explain these patterns; and 3) predict membership within these patterns.

Methods: Survey data from 646 urban-dwelling older adults aged 60 and above across 26 provincial-level administrative divisions in Mainland China were analyzed. EoLC preferences regarding willingness to know diagnosis, willingness to know prognosis, decision-maker, treatment goals, place of care, caregiver, and setting advance directives were assessed alongside demographics, resources, knowledge and attitudes, and caregiving/bereavement experiences. Latent class analysis (LCA), 3-step regressions, and Catboost machine learning models were employed to identify subgroups, examine between-group differences, and predict subgroup membership, respectively.

Results: LCA identified three latent patterns: "low self-determination, quality-goal, family-oriented care" (9.1%), "high self-determination, quality-goal, family-oriented care" (54.0%), and "high self-determination, quantity-goal, professional-oriented care" (36.9%). Significant between-group differences were found in education, marital status, living arrangements, family income, social support, EoLC knowledge, general trust, and professional-patient trust. Machine learning models revealed that high general trust predicts membership in the high self-determination, quality-goal, family-oriented care group, while low filial piety expectations predict membership in the high self-determination, quantity-goal, professional-oriented care group.

Conclusion: Among Chinese older adults, three EoLC preference patterns were found, which were characterized by low family connections, low trust in professionals combined with adequate resources, and extensive knowledge, respectively. High general trust and low filial piety expectations were key predictors for two of the three patterns.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jpainsymman.2025.07.009DOI Listing

Publication Analysis

Top Keywords

older adults
20
eolc preferences
12
self-determination quality-goal
12
quality-goal family-oriented
12
general trust
12
end-of-life care
8
urban-dwelling older
8
dimensions eolc
8
latent patterns
8
chinese older
8

Similar Publications