Evidence map›Paper›PMID 36505000›Full record

ArticleFrontiers in public health2022

How public health agencies communicate with the public on TikTok under the normalization of COVID-19: A case of 2022 Shanghai's outbreak.

ShaoPeng Che, Shunan Zhang, Jang Hyun Kim

Abstract readCase Reports
In one paragraph

Article in Frontiers in public health, 2022. 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

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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.

ShaoPeng CheDepartment of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul, South Korea.
Shunan ZhangDepartment of Interaction Science, Sungkyunkwan University, Seoul, South Korea.
Jang Hyun KimDepartment of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: As life with COVID-19 became a norm in 2022, the public's demand for and perception of COVID-19-related information has changed. This study analyzed the performance and responses of Healthy China and the public at various stages of COVID-19 normalization using the crisis and emergency risk communication (CERC) theory. Methods: This study was based on the 2022 Shanghai COVID-19 outbreak and data from "Healthy China," the official TikTok account of the National Health Commission of the People's Republic of China (NHCC). First, we divided the Shanghai lockdown into five stages in accordance with the CERC. Second, the videos released by Healthy China were open-coded. Third, to understand the distribution of strategies across the stages, we used counts and percentages to summarize the categorical variables. Fourth, we investigated the distribution of public participation indicators using descriptive statistical analysis. Finally, the relationship between stage and communication strategy was examined using the chi-square test and negative binomial regression. Results: (1) Healthy China adopted a more flexible approach to communication strategies; (2) new cases per day was the commonly used substrategy for uncertainty reduction; (3) there was a significant difference in the strategies used by Healthy China at different stages; (4) public participation was highest in the pre-crisis period; and (5) the stage had a significant positive impact on the number of views, favorites, likes, and shares. Conclusions: This research provides insight into effective communication strategies for the government or public health agencies to employ during COVID-19 normalization.

Indexed as

COVID-19ChinaCommunicable Disease ControlDisease OutbreaksHumansPublic Healthcommunication strategyCOVID-19crisis and emergency risk communicationnegative binomial regressionpublic health agencypublic health emergencysocial mediaTikTok

Identifiers

PMID36505000
PMCPMC9731275

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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.