Evidence map›Paper›PMID 35788308›Full record

ArticleHealth promotion international2022

Online information and support seeking during COVID-19 lockdown in Wuhan: implications for health promotion.

Xiaoman Zhao, Iccha Basnyat

Abstract read
In one paragraph

Article in Health promotion international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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  5. Review
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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

2 authors.

Xiaoman ZhaoResearch Center of Journalism and Social Development, School of Journalism and Communication, Renmin University of China, 59 Zhongguancun Street, Beijing 100872, China.ORCID 0000-0002-7667-3813
Iccha BasnyatGlobal Affairs Program & Department of Communication, Horizon Hall, George Mason University, Fairfax, VA 22030, USA.

Funding

pecial Funds for Central Universities to Build World-class Universities
6 · The paper itself

Abstract

The lockdown that was deployed in Wuhan, China to combat the COVID-19 pandemic curbed the infection but also created great information challenges for people in social isolation. This resulted in surge in online health information seeking (OHIS) behaviors of the patients and their families. While the Internet has been widely used by Chinese public to access and search health information, there is relatively little research in the context of pandemic outbreaks, especially at the onset of a strong lockdown while many people were panicking. From a total of 10 908 '#COVID-19 Patient Seeking Help' posts on Weibo in a period of 20 days when the lockdown policy was first initiated, we identified 1496 unique patients living in or with family in Wuhan, China. Using textual analysis, we explored OHIS behaviors at the onset of the pandemic. Many faced increased difficulties accessing offline healthcare services and such turned to social media for help and information. In particular, the findings highlight the following themes: 'OHIS for medical treatment', 'OHIS to manage self-quarantine', 'OHIS for tangible support' and 'OHIS to navigate information discrepancy'. Overall, our findings provide important insights into health information seeking behaviors and the role of social media during a pandemic. Our findings also highlight the importance of considering people's information need and challenges created due to the lockdown policies in the future pandemic communication and preparedness.

Indexed as

COVID-19Social MediaCommunicable Disease ControlHealth PromotionHumansPandemicsCOVID-19information seekinglockdownsocial mediaWuhan

Identifiers

PMID35788308
PMCPMC9278205

What OpenQuestion holds

Textmetadata
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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.