Evidence map›Paper›PMID 36093363›Full record

ArticleSexuality & culture2023

Investigating Media Coverage and Public Perceptions of the HPV Vaccine in China - A Content Analysis of Weibo Posts.

Junyi Hu, Thomas William Whyke, Joaquin Lopez-Mugica

Abstract read
In one paragraph

Article in Sexuality & culture, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 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

3 authors.

Junyi HuThe School of International Communications, The University of Nottingham Ningbo China, Ningbo, Zhejiang China.
Thomas William WhykeThe School of International Communications, The University of Nottingham Ningbo China, Ningbo, Zhejiang China.ORCID 0000-0002-9771-3626
Joaquin Lopez-MugicaWenzhou-Kean University, Wenzhou, Zhejiang China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human papillomavirus (HPV) is one of the most common sexually transmitted infections worldwide. The HPV vaccination has been widely advocated around the world since the vaccine is beneficial in avoiding diseases, including some sexually transmitted diseases, brought on by HPV infections. For most Chinese, the HPV vaccine is still a relatively new concept, having only been made available to the general public in 2016. Despite the vaccine's increased prominence, there is still a lack of investigation about how the public is influencing the conversation about HPV vaccines and the public's perception of this vaccine. With the theoretical construct of the Health Belief Model, this study conducts both quantitative and qualitative content analysis to investigate the existing media narratives around HPV vaccines in China and the changes in public opinion by looking at users' contributions on Weibo, one of China's most popular social networking sites. It was found that different groups of Weibo users had contributed to diverse narratives surrounding HPV vaccination. Though the public awareness of HPV vaccination had been improved along with increasingly active communication practices and enhanced public health services, public knowledge about HPV remains inadequate. Therefore, to facilitate the popularisation of HPV related knowledge, more effort should be invested in tailoring and disseminating messages that communicate responsive and comprehensive HPV related information.

Indexed as

ChinaContent AnalysisHealth Belief ModelHuman Papillomavirus VaccinesSexual HealthSocial MediaVaccination Awareness

Identifiers

PMID36093363
PMCPMC9443650

What OpenQuestion holds

Textmetadata
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.