Evidence map›Paper›PMID 40041549›Full record

ArticleFrontiers in public health2024

Exploring public attitudes of continuing care retirement communities in China: a sentiment analysis of China's social media Weibo.

Xuechun Wang, Bo Xia, Qing Chen, Martin Skitmore, Huiming Liu

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xuechun WangDepartment of Engineering Management, Shanxi Vocational University of Engineering Science and Technology, Taiyuan, Shanxi Provience, China.
Bo XiaDepartment of Engineering Management, Shanxi Vocational University of Engineering Science and Technology, Taiyuan, Shanxi Provience, China.
Qing ChenDepartment of Engineering Management, Shanxi Vocational University of Engineering Science and Technology, Taiyuan, Shanxi Provience, China.
Martin SkitmoreUniversity Professorial Fellow, Faculty of Society and Design, Bond University, Robina, QLD, Australia.
Huiming LiuFaculty of Humanities and Art, Macau University of Science and Technology, Taipa, Macau, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The traditional family responsibility system faces challenges as China undergoes rapid demographic shifts with an increasingly older population. Recognizing the potential of market-driven senior care, Continuing Care Retirement Communities (CCRCs) have emerged as a significant alternative. However, cultural stigmas and concerns about the quality, services, and health of older adults in these facilities raise questions over their broad acceptance. Methods: This study examines public sentiment toward CCRCs through sentiment analysis of 1,027,295 pre-processed Weibo posts. Utilizing Natural Language Processing (NLP) combined with fine-grained sentiment analysis and the Term Frequency-Inverse Document Frequency algorithm, the attitudes and emotions reflected in each data point are analyzed, identifying key contributing factors, and exploring the underlying reasons. Results and discussion: The results reveal a predominantly positive sentiment toward CCRCs, emphasizing factors such as the living environment and government involvement. However, areas of concern, such as potential fraud and health and safety issues, remain. These findings both shed light on the public's acceptance or resistance to CCRCs for stakeholders and highlight the potential of social media analysis in shaping older people's care perceptions in today's China.

Indexed as

AttitudePublic OpinionRetirementSocial MediaAgedChinaFemaleHumansMaleagingcontinuing care retirement communitiesnatural language processingpublic attitudessentiment analysissocial mediaWeibo

Identifiers

PMID40041549
PMCPMC11875845

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.