Evidence map›Paper›PMID 39109157›Full record

ArticleFrontiers in public health2024

Understanding invisible pain in the older adult: a content analysis of social media's representation of herpes zoster vaccine.

Mingyi Yang, Yiran Yang, Shuyue Li

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. 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

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

3 citing papers in PubMed.

  1. Article
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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.

Mingyi YangSchool of Journalism and Communication, Guangxi University, Nanning, China.
Yiran YangSchool of Journalism and Communication, Guangxi University, Nanning, China.
Shuyue LiSchool of Journalism and Communication, Guangxi University, Nanning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Herpes zoster (HZ), a common disease in older adults, affects their quality of life. Therefore, this study aimed to examine the blog posts of HZ-related information on social media platforms to analyze the attitudes and behaviors of residents toward the dissemination of health information. This research used content analysis to focus on Weibo, a representative social media in China, to analyze the content of 1866 blog posts related to herpes zoster (HZ) and herpes zoster vaccine (HZV). According to the consistency test by Cohen's Kappa, four themes were identified: (a) sources, (b) tones, (c) epidemiological information, and (d) extended parallel process model elements. The findings showed that most information on Weibo came from non-professionals, with a neutral tone, and showed the invisible pain of HZ and the effectiveness of HZV through the two largest aspects of prevention and aged protection in epidemiological information. However, current blog posts treat the older adult as invisible individuals, failing to acknowledge them as recipients of the information. Additionally, the cost of the vaccine acts as an invisible economic barrier, contributing to the dissemination of incorrect information about folk remedies. This impacts the older adult's acceptance of health information related to HZV. Thus, the way to share health information with the older adult needs to be improved in the future, and attention should be paid to the transmission of incorrect information to improve their vaccination rates and awareness of health management.

Indexed as

Herpes ZosterHerpes Zoster VaccineSocial MediaAgedChinaFemaleHumansMaleMiddle AgedPainQuality of LifeHerpes Zoster Vaccinehealth informationHZVolder adultssocial mediathe extended parallel process model

Identifiers

PMID39109157
PMCPMC11300333

What OpenQuestion holds

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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.