Evidence map›Paper›PMID 38584918›Full record

ArticleFrontiers in public health2024

Influencing factors of health screening among retirees: an extended TPB approach.

Jiayi Xu, Younghwan Pan, Qiongzhen Li

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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Social Marketing for Health Equity: Promoting Preventive Health Behavior Among Women in Rural Communities.International journal of environmental research and public health · 2026
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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.

Jiayi XuDepartment of Smart Experience Design, Graduate School of Techno Design, Kookmin University, Seoul, Republic of Korea.
Younghwan PanDepartment of Smart Experience Design, Graduate School of Techno Design, Kookmin University, Seoul, Republic of Korea.
Qiongzhen LiShizong County People's Hospital, Yunnan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Health screenings are promoted worldwide as they help detect and prevent overall health issues. Despite expanding coverage, the participation rate among the retired population has not significantly increased. Given the special role of health screenings in promoting health and healthy aging, understanding the behavioral intentions, and influencing factors of retirees' voluntary participation in health screenings is crucial. This study aims to explore the participation intentions in health screenings among the Chinese retired population by integrating the Theory of Planned Behavior (TPB) and Self-Efficacy (SE). Methods: This study used a cross-sectional design to conduct an online questionnaire among 311 retirees in 2023. The questionnaire, tailored for the Chinese retired population, combines the TPB theory and Self-Efficacy theory, including demographic structure, the basic structure of TPB, and SE. Results: A Structural Equation Modeling (SEM) approach was used to identify factors related to health screening behaviors. Of the respondents, 311 completed the survey (88.9% response rate). The most crucial determinant of health examination behavior was behavioral intention, with a correlation score of (1.524, Conclusion: This study provides insights for enhancing the willingness and behavior of retirees to participate in health screenings.

Indexed as

AttitudeTheory of Planned BehaviorCross-Sectional StudiesHumansIntentionSurveys and Questionnaireshealth screeningparticipate behaviorretirementself-efficacythe theory of planned behavior

Identifiers

PMID38584918
PMCPMC10998445

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.