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ORIGINAL RESEARCH article

Front. Med.

Sec. Geriatric Medicine

Volume 12 - 2025 | doi: 10.3389/fmed.2025.1615192

This article is part of the Research TopicUrban AgingView all 7 articles

What might influence the Elderly willingness to participate in 'Shared Elderly Care'? A mixed Methods study

Provisionally accepted
Hejia  WanHejia Wan1*Zilin  ZhaoZilin Zhao1Xinghui  LiXinghui Li2Tianyue  XiangTianyue Xiang1Yifan  QiYifan Qi3Jing  ZhangJing Zhang1*Yuanmei  QinYuanmei Qin1*
  • 1School of Nursing, Henan University of Chinese Medicine, Zhengzhou, Henan Province, China
  • 2School of Information Management, Zhengzhou University, Zhengzhou, Henan Province, China
  • 3School of Acupuncture and Massage, Henan University of Chinese Medicine, Zhengzhou, Henan Province, China

The final, formatted version of the article will be published soon.

Purpose: This study aimed to explore the core factors influencing participation in the "shared elderly care" model among urban Chinese seniors and propose targeted solutions to address the challenges of an aging society. Methods: A mixed-methods study was conducted. A questionnaire survey was conducted among 533 seniors in Zhengzhou. Data on demographic characteristics, health literacy, and environmental factors were analyzed using four machine learning algorithms: logistic regression, random forest, K-nearest neighbor, and support vector machine. Approximately three years later, qualitative validation was conducted through six focus group interviews. Themes were extracted using Colaizzi phenomenological analysis, and the predictions were validated. Results: 500 valid questionnaires were collected. The machine learning algorithm results showed that the random forest model had the best predictive performance (AUC = 0.652), revealing that e-health literacy and policy awareness were key drivers (jointly explaining 24.1% of the variance in participation intention), with age, environmental sensitivity, and social influence as significant cofactors. Qualitative analysis confirmed that technology usability and a sense of social belonging were core experiential elements of deep participation. Frontiers in Medicine* For Peer Review — 2 — Conclusion: Addressing the primary obstacles of digital literacy gaps and limited technological accessibility, we propose three countermeasures: increasing publicity and promotion of shared elderly care models; conducting community digital health literacy training; and increasing resource allocation to precisely match needs, thus providing an implementation path for building an inclusive shared elderly care ecosystem.

Keywords: Shared elderly care, e-health literacy, machine learning, Age-Friendly Technology, Resource Allocation, aging society

Received: 20 Apr 2025; Accepted: 23 Oct 2025.

Copyright: © 2025 Wan, Zhao, Li, Xiang, Qi, Zhang and Qin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Hejia Wan, 2240536681@qq.com
Jing Zhang, 1839640376@qq.com
Yuanmei Qin, qinyuanmei69@163.com

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