ArticleJournal of medical Internet research2021
Classification of the Use of Online Health Information Channels and Variation in Motivations for Channel Selection: Cross-sectional Survey.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Knowledge of Syphilis and Human Papillomavirus and Associated Factors in a Japanese University Setting: A Cross-Sectional Study.Health science reports · 2026Article
- Factors Shaping Trust and Satisfaction With AI Medical Chatbots: A Mixed Methods Vignette Survey of Caregivers Seeking Guidance on Pediatric Infectious Diseases.Journal of medical Internet research · 2026Article
- Medication information sources and associated factors among older adults in Nanjing, China: a CMIS-based cross-sectional study.BMC geriatrics · 2026Article
- Characterizing the Adoption and Experiences of Users of Artificial Intelligence-Generated Health Information in the United States: Cross-Sectional Questionnaire Study.Journal of medical Internet research · 2024Article
- Creating, publishing, and spreading processes of health-related contents in internet news sites: evaluation of the opinions of actors in health communication.Frontiers in public health · 2024Article
- Exploring how members of the public access and use health research and information: a scoping review.BMC public health · 2023Article
- Association Between Online Health Information-Seeking Behaviors by Caregivers and Delays in Pediatric Cancer: Mixed Methods Study in China.Journal of medical Internet research · 2023Article
- Searching for HIV and AIDS Health Information in South Africa, 2004-2019: Analysis of Google and Wikipedia Search Trends.JMIR formative research · 2022Article
- Article
- The relationship between eHealth literacy, social media self-efficacy and health communication intention among Chinese nursing undergraduates: A cross-sectional study.Frontiers in public health · 2022Article
- Article
- Navigating online health information: Insights into consumer influence and decision-making strategies-An overview of reviews.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundExisting health education and communication research routinely measures online channel use as a whole by, for example, evaluating how frequently people use the internet to search for health information. This approach fails to capture the complexity and diversity of online channel use in health information seeking. The measurement of generic online channel use may cause too much error, and it lends no support to media planning in public health promotion campaigns or scholarly research involving online channel use.
objectiveThis study intends to present a thorough picture of patterns of online health information channel use and classify the use of various types of online health information channels, including WeChat, microblogs, web portals, search engines, mobile apps, and online forums. Under the framework of the risk information seeking and processing model, this study also analyzes the differences in individuals' motivations for channel selection to offer further evidence to validate the classification scheme.
methodsThis study sampled 542 Chinese internet users in Beijing. The average age of the respondents was 33 years, female respondents accounted for 52.0% (282/542) of the sample, and the average monthly income ranged from US $900 to $1200. The study surveyed the use of 13 commonly used online health information channels and various sociopsychological factors associated with online health information seeking.
resultsThis study derived 3 categories of online health information channels: searching, browsing, and scanning channels. It was found that the use of online searching channels was affect driven (B=0.11; β=0.10; P=.02) and characterized by a stronger need for health knowledge (B=0.09; β=0.01; P<.001). The use of browsing channels was directly influenced by informational subjective norms (B=0.33; β=0.15; P=.004) and perceived current knowledge (B=0.007; β=0.09; P=.003). The use of scanning channels was mainly influenced by informational subjective norms (B=0.29; β=0.15; P=.007).
conclusionsThe results of this study suggest that health communication practitioners and scholars may consider measuring the use of internet, new media, or online media more precisely instead of simply asking the public about the frequency of online channel use or internet use in the acquisition of health information. Scholars and practitioners may consider measuring the use of online health information channels by using the 3-category scheme described in this study. Future research is encouraged to further explore how people process health information when using different online channels.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.