Evidence map›Paper›PMID 36360695›Full record

ArticleInternational journal of environmental research and public health2022

What Drives the Influence of Health Science Communication Accounts on TikTok? A Fuzzy-Set Qualitative Comparative Analysis.

Ran Liu, Tianan Yang, Wenhao Deng, Xiaoyan Liu, Jianwei Deng

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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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

5 authors.

Ran LiuSchool of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China.
Tianan YangSchool of Management and Economics, Beijing Institute of Technology, Beijing 100081, China.ORCID 0000-0001-6994-354X
Wenhao DengSchool of Management and Economics, Beijing Institute of Technology, Beijing 100081, China.
Xiaoyan LiuSchool of Languages and Communication Studies, Beijing Jiaotong University, Beijing 100044, China.
Jianwei DengSchool of Management and Economics, Beijing Institute of Technology, Beijing 100081, China.ORCID 0000-0001-5846-9715

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical institutions face a variety of challenges as they seek to enhance their reputation and increase the influence of their social media accounts. Becoming a social media influencer in the health field in today's complex online environment requires integrated social and technical systems. However, rather than holistically investigating the mechanism of account influence, studies have focused on a narrow subset of social and technical conditions that drive online influence. We attribute this to the mismatch between complex causality problems and traditional symmetric regression methods. In this study, we adopted an asymmetric configurational perspective that allowed us to test a causally complex model of the conditions that create strong and not-strong account influence. We used fuzzy-set qualitative comparative analysis (fsQCA) to detect the effects of varying configurations of three social system characteristics (i.e., an oncology-related attribute, a public attribute, and comment interaction) and two technical system characteristics (i.e., telepresence and video collection) on the TikTok accounts of 63 elderly Chinese doctors (60 to 92 years old). Our results revealed two pathways associated with distinct sociotechnical configurations to strong account influence and three pathways associated with distinct sociotechnical configurations to not-strong account influence. Furthermore, the results confirmed that a single antecedent condition cannot, on its own, produce an outcome, i.e., account influence. Multiple inter-related conditions are required to produce an influential account. These results offer a more holistic picture of how health science communication accounts operate and reconcile the scattered results in the literature. We also demonstrate how configurational theory and methods can be used to analyze the complexities of social media platforms.

Indexed as

Health CommunicationSocial MediaAgedAged, 80 and overHumansMiddle Agedaccount influencefsQCAhealth science communicationmicrovideoTikTok

Identifiers

PMID36360695
PMCPMC9655241

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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.