Influencing Factors on Health Information to Improve Public Health Literacy in the Official WeChat Account of Guangzhou CDC
Bibliographic Data
| ID | 22089623 |
|---|---|
| Authors | Xiaowei Ma (0000-0003-1513-0855, Guangzhou Center for Disease Control and Prevention), Jianyun Lu (0000-0002-1721-1862, Guangzhou Center for Disease Control and Prevention), Weisi Liu (0000-0002-4730-5448, Guangzhou Center for Disease Control and Prevention, corresponding author) |
| Year | 2021 |
| Volume | 9 |
| Pages | 657082-657082 |
| Publication date | 2021-08-03 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2021.657082 |
| PMID | 34414152 |
| OpenAlex | W3193130057 |
| Language | EN |
| Citations received | 8 |
| References cited | 36 |
Background: Social media is used as a new channel for health information. In China, the official WeChat account is becoming the most popular platform for health information dissemination, which has created a good opportunity for the Centers for Disease Control and Prevention to facilitate health information online to improve emergency public health literacy. Methods: Data were collected from the Guangzhou CDC i-Health official WeChat account between April 1, 2018 and April 30, 2019. Descriptive analysis was performed for basic information about the followers and posts of the official WeChat account. Multiple logistic regression analysis was used to analyze the association among various factors of posts on engagement of followers of the official WeChat account. Results: Among 187,033 followers, the total numbers of post views, shares, likes, add to favorites, and comments for 213 posts were 1,147,308, 8,4671, and 5,535, respectively. Engagement of followers peaked on the dissemination date and gradually declined. The main post topics were health education posts and original posts. In the multiple logistic regression model, the number of post views was found to be significantly associated with infectious disease posts (AOR: 3.20, 95% CI: 1.16–8.81), original posts (AOR: 10.20, 95% CI: 1.17–89.28), and posts with title-reflected content (AOR: 2.93, 95% CI: 1.16–8.81). Conclusion: Our findings facilitate the government to formulate better strategies and improve the effectiveness of public information dissemination
China · Descriptive statistics · Dissemination · Environmental health · Health care · Health information · Health literacy · Information Dissemination · Literacy · Logistic regression · Political science · Public health · Social media · World Wide Web · Computer Science · Health Literacy and Information Accessibility · Medicine · Misinformation and Its Impacts · Nursing · Social Media in Health Education
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Digital Junk
| Unique citing works | 8 |
|---|---|
| Citations per year | 2 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 8 |