Evidence map›Paper›PMID 38098818›Full record

ArticleFrontiers in public health2023

Unfolding COVID-19 vaccine communication campaigns in China's neighborhoods: a qualitative study of stakeholders' narratives.

Ronghui Yang, Yanchao Han

Abstract read
In one paragraph

Article in Frontiers in public health, 2023. 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

2 authors.

Ronghui YangSchool of Humanities, Donghua University, Shanghai, China.
Yanchao HanSchool of Humanities, Donghua University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The Chinese state has recently implemented the COVID-19 Vaccine Communication Campaign (CVCC) to counter vaccine hesitancy. Nonetheless, the extant literature that examines COVID-19 vaccine acceptance has less represented COVID-19 vaccine communication efforts. Methods: To address this lacuna, we qualitatively explored how CVCCs were organized in Chinese communities by investigating 54 Chinese stakeholders. Results: This study indicates that the CVCC was sustained by top-down political pressure. CVCCs' components involve ideological education among politically affiliated health workers, expanding health worker networks, training health workers, implementing media promotion, communicating with residents using persuasive and explanatory techniques, encouraging multistakeholder partnerships, and using public opinion-steered and coercive approaches. While CVCCs significantly enhanced COVID-19 vaccine acceptance, lacking open communication, stigmatizing vaccine refusers, insufficient stakeholder collaboration, and low trust in the COVID-19 vaccination program (CVP) eroded the validity of CVCCs. Discussion: To promote the continuity of CVCCs in China, CVCC performers are expected to conduct open and inclusive communication with residents. Furthermore, CVP planers should create robust partnerships among health workers by ensuring their agreements on strategies for implementing CVCCs and optimize COVID-19 immunization service provision to depoliticize CVPs. Our study will not only deepen global audiences' understanding of CVCCs in authoritarian China but also offer potential neighborhood-level solutions for implementing local and global public health communication efforts.

Indexed as

COVID-19COVID-19 VaccinesChinaCommunicationHumansQualitative ResearchCOVID-19 Vaccinescommunication campaigncontinuityCOVID-19 vaccineneighborhoodpolitical pressure

Identifiers

PMID38098818
PMCPMC10720319

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.