Evidence map›Paper›PMID 41337746›Full record

ArticleJMIR formative research2025

The Mechanism of Online Health Information Seeking Switching to Online Medical Consultation: Cross-Sectional Study.

Lijiang Zhang, Jingjing Xia, Hui Chen, Yang Bai, Jun Wang, Liuan Wang, Wenjie Ren

Erratum issuedAbstract read
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Lijiang Zhang *School of Health Management, Henan Medical University, Xinxiang, Henan, China.ORCID 0009-0008-2935-065X
Jingjing Xia *School of Health Management, Henan Medical University, Xinxiang, Henan, China.ORCID 0009-0001-8992-3228
Hui Chen *School of Health Management, Henan Medical University, Xinxiang, Henan, China.ORCID 0009-0009-2944-4047
Yang Bai *School of Public Administration and Policy, Renmin University of China, Beijing, China.ORCID 0000-0002-5840-2294
Jun WangCenter for Health Policy Research and Evaluation, Renmin University of China, Beijing, China.ORCID 0000-0003-0872-2238
Liuan WangSchool of Management, Beijing Institute of Technology, Beijing, China.ORCID 0000-0003-1011-4936
Wenjie Ren *Institutes of Health Central Plains, Henan Medical University, Xinxiang, Henan, China.ORCID 0000-0003-4711-2352

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInternet health care plays a crucial role in addressing the challenge of distributing high-quality medical resources and promoting the optimal allocation of these resources and health equity in China. Online medical consultation (OMC) plays a more significant role than online health information seeking (OHIS). Currently, the proportion of Chinese patients using OMC is low. Therefore, it is essential to enhance patient engagement with OMC and fully leverage the role of internet health care in optimizing the allocation of medical resources.

objectiveThis study aims to explore the correlation mechanisms of online medical community users' switching behaviors from OHIS to OMC.

methodsThis study is based on the knowledge-attitude-practice theory, which combines the social support theory and the health belief model to construct a research model of users' willingness to transition from OHIS to OMC. The study adopts a questionnaire survey and structural equation modeling method to conduct an empirical study.

resultsGaining knowledge about information support has a significant positive impact on perceived susceptibility (β=.339, P<.001), perceived severity (β=.348, P<.001), and perceived benefits (β=.361, P<.001), while having a significant negative impact on perceived barriers (β=-.285, P<.001). Gaining knowledge about emotional support positively affects perceived susceptibility (β=.220, P<.001) and perceived benefits (β=.149, P<.01) but does not significantly influence perceived severity (β=-.006, P>.05) or perceived barriers (β=.099, P>.05). Perceived susceptibility (β=.123, P<.05), perceived severity (β=.174, P<.001), and perceived benefits (β=.273, P<.001) positively influence patients' transition to online consultation behavior, whereas perceived barriers (β=-.112, P<.05) negatively impact this switch. In addition, we found that gaining knowledge about information support not only directly affects patients' behavior in switching to online consultations but also impacts patients' OMCs through perceived susceptibility (14.23%), perceived severity (13.17%), and perceived benefits (25.28%). In contrast, gaining knowledge about emotional support does not directly influence patient behavior transfer; it operates only through perceived susceptibility (46.95%) and perceived benefit (52.90%).

conclusionsThis study integrated the knowledge-attitude-practice framework, social support theory, and health belief model to uncover the internal logic of patients' behavioral transfers within online health communities. It confirmed the mediating role of the cognitive-emotional dual-drive pathway and health beliefs. The findings provide a scientific basis for the functional design of online health care platforms and for precise health knowledge dissemination strategies.

Indexed as

Health Belief ModelInformation Seeking BehaviorPatient Acceptance of Health CareSocial SupportTelemedicineAdultChinaCross-Sectional StudiesFemaleHealth Knowledge, Attitudes, PracticeHumansInternetLatent Class AnalysisMaleMiddle AgedReferral and Consultationhealth belief modelknowledge-attitude-practiceonline health information seekingonline medical consultationsocial supportswitching behavior

Identifiers

PMID41337746
PMCPMC12778383

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.