Evidence map›Paper›PMID 36204749›Full record

ArticleFrontiers in psychology2022

Why should I consult? The impact of social support on patient consultation in online healthcare communities.

Xiaochen Liu, Zhen Xu, Xintao Yu, Tetsuaki Oda

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychology, 2022. 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
0.9field-weighted citation impact, top 21% of its field
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, 4 citations in OpenAlex.

  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

4 authors at 3 institutions in 2 countries.

Xiaochen LiuGraduate School of Technology Management, Ritsumeikan University, Osaka, Japan.
Zhen XuSchool of Communication, East China University of Political Science and Law, Shanghai, China.
Xintao YuSchool of Economics and Management, Liaoning University of Technology, Jinzhou, China.
Tetsuaki OdaGraduate School of Technology Management, Ritsumeikan University, Osaka, Japan.
Ritsumeikan University · JPEast China University of Political Science and Law · CNLiaoning University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 epidemic put the traditional healthcare system and offline consultation method under strain. Patient consultations through online healthcare communities (OHCs) provide patients and physicians with a more convenient and secure route. Based on the social support theory, this study explores the impact of three dimensions of social support from physicians-information diagnosticity, source credibility, and emotional support-on patient consultations in OHCs and their moderating effect on patients' compliments. We utilized Python Spiders to retrieve data from Haodf.com and gathered 2,982 physician reports. The model uses OLS regression with fixed effect estimations. The results show that these three dimensions of social support are positively impacted by consultation. Furthermore, patients' compliments weaken the positive relationship between the three dimensions of physicians' social support and patient consultations. This study contributes to the literature on social support theory in OHCs by exploring the physicians' social support dimension and its impact on patient consultation. Moreover, this study makes practical contributions to physicians and platform administrators in OHCs.

Indexed as

emotional supportinformational supportonline healthcare communitiespatients’ complimentspatients’ consultationssocial support theory

Identifiers

PMID36204749
PMCPMC9530996
OpenAlexW4296396616

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.