Evidence map›Paper›PMID 39457767›Full record

ArticleBehavioral sciences (Basel, Switzerland)2024

Residents' Cognition, Attitudes, and Intentions to Participate in Long-Term Care Insurance: Moderating Effect of Policy Support.

Zhaohui Sun, Yifan Li, Shaokui Gao

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

3 authors.

Zhaohui SunDepartment of Law and Political Science, North China Electric Power University, Baoding 071003, China.
Yifan LiDepartment of Law and Political Science, North China Electric Power University, Baoding 071003, China.
Shaokui GaoDepartment of Law and Political Science, North China Electric Power University, Baoding 071003, China.

Funding

the Fundamental Research Funds for the Central Universities 2023MS159
6 · The paper itself

Abstract

This study took a comprehensive approach to considering residents' demands and investigated the intentions of residents in Hebei Province to participate in LTCI, exploring the potential for individual participation. By integrating the unique Chinese context and existing literature, this study established a theoretical framework for research hypotheses concerning the influencing factors of residents' participation intentions. Leveraging a sample of 516 survey responses, we employed structural equation modeling (SEM) and hierarchical regression analysis (HRA) for validation. The research revealed that the heterogeneity of resident attributes has a significant influence on their participation intentions, and a gap exists between residents' attitudes and intentions, leading to a scenario where attitudes are high but intentions are low. Moreover, perceived usefulness, risk perception, and perceived trust were found to directly affect residents' intentions and could indirectly affect intentions through the mediating effect of attitudes. Additionally, the moderating role of policy support is instrumental in facilitating the translation of attitudes into actual intentions, bridging the gap between attitudes and participation intentions. These findings will assist researchers in gaining a deeper understanding of Chinese residents' participation intentions and their underlying influencing factors, ultimately providing a solid foundation for government policy formulation and insurance companies' strategic adjustments.

Indexed as

intention to participate in long-term care insurancelong-term care insurancepolicy supportstructural equation model

Identifiers

PMID39457767
PMCPMC11505349

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.