A meta-analysis was conducted to quantify the overall effect of health misinformation exposure on shaping misbelief. Aggregation of results from 28 individual randomized controlled trial studies (n = 8752) reveals a positive but small average effect, d = 0.28. Moderation analyses suggest that adults who are younger and female tend to develop higher misbelief if exposed to health misinformation. Furthermore, media platform, message falsity, and misbelief measurements also contribute to the exposure effect. These findings offer nuanced but crucial insights into existing misinformation literature, and development of more effective strategies to mitigate the adverse impacts of health misinformation.

BACKGROUND: The escalating dissemination of health misinformation on social media platforms poses a significant threat to users\' well-being. It is imperative to identify the types of health misinformation that are more susceptible to widespread dissemination and to explore strategies to curb its spread.
METHODS: This study designed a 2 (emotional appeal type: positive vs. negative) × 2 (fabricated source type: pseudo-common vs. pseudo-authoritative) × 2 (accuracy-nudge label: No vs. Yes) online between-subjects experiment controlling for factors such as e-health literacy, prior sharing experience, and personal involvement. A snowball sampling approach was used to recruit 1952 participants through social media, resulting in a final sample of 1393 valid responses.
RESULTS: Compared to positive emotional appeal and pseudo-common sources, negative emotional appeal and pseudo-authoritative sources resulted in higher levels of sharing intention. Under the condition of negative emotional appeal, the promotion effect of pseudo-authoritative sources on sharing intention was intensified. The accuracy-nudge intervention could significantly mitigate this tendency. The underlying mechanisms revealed more details: both negative emotional appeals and pseudo-authoritative sources increased the perceived credibility of health misinformation, thereby increasing users\' sharing intention. However, in contrast to pseudo-authoritative sources, excessive negative emotional appeal induced vigilant verification behavior among users, which reduced sharing to some extent. Adding an accuracy-nudge label to health misinformation reduced users\' misguided trust in health misinformation features and stimulated information verification, ultimately reducing health misinformation sharing intention.
CONCLUSIONS: Negative emotional appeal and pseudo-authoritative sources can enhance the perceived credibility of health misinformation, thereby strengthening the sharing intention of social media users. Therefore, health misinformation with negative emotional appeal and pseudo-authoritative sources is more likely to be widely shared. The accuracy nudge intervention can trigger users\' information verification behavior, suppress the persuasive effects of the misinformation features mentioned above, and help prevent the spread of health misinformation on social media.

UNASSIGNED: Health misinformation is common and can lead to harmful behaviors such as medication non-adherence. We assessed the impact of a novel patient educational tool focused on overcoming misconceptions among patients with coronary artery disease (CAD).
UNASSIGNED: We developed the CAD Roadmap, an educational tool aimed at explaining the disease trajectory and overcoming common disease misconceptions (such as that statin medications are not beneficial). We designed a pilot survey to assess patients\' 1) CAD-related knowledge, 2) medication-taking behavior, and 3) acceptability of the Roadmap. Survey participants were recruited online. CAD knowledge scores were compared with repeated measures t-tests.
UNASSIGNED: Among 114 patients with CAD (mean age 67 years, 63% male), average CAD-related knowledge was 79.0% pre-test and 89.7% after review of the CAD Roadmap (p < .001). After review of the Roadmap, 24% indicated they planned to take their medications more regularly, 93% agreed it was helpful in understanding medication benefits, and 77% felt more empowered to participate in medical decisions.
UNASSIGNED: The CAD Roadmap was evaluated positively, improved disease-related knowledge, and has the potential to improve adherence to treatments.
UNASSIGNED: Unlike many other interventions, the CAD Roadmap is specifically designed to overcome common misconceptions to improve health behaviors.

BACKGROUND: Machine learning techniques have been shown to be efficient in identifying health misinformation, but the results may not be trusted unless they can be justified in a way that is understandable.
OBJECTIVE: This study aimed to provide a new criteria-based system to assess and justify health news quality. Using a subset of an existing set of criteria, this study compared the feasibility of 2 alternative methods for adding interpretability. Both methods used classification and highlighting to visualize sentence-level evidence.
METHODS: A total of 3 out of 10 well-established criteria were chosen for experimentation, namely whether the health news discussed the costs of the intervention (the cost criterion), explained or quantified the harms of the intervention (the harm criterion), and identified the conflicts of interest (the conflict criterion). The first step of the experiment was to automate the evaluation of the 3 criteria by developing a sentence-level classifier. We tested Logistic Regression, Naive Bayes, Support Vector Machine, and Random Forest algorithms. Next, we compared the 2 visualization approaches. For the first approach, we calculated word feature weights, which explained how classification models distill keywords that contribute to the prediction; then, using the local interpretable model-agnostic explanation framework, we selected keywords associated with the classified criterion at the document level; and finally, the system selected and highlighted sentences with keywords. For the second approach, we extracted sentences that provided evidence to support the evaluation result from 100 health news articles; based on these results, we trained a typology classification model at the sentence level; and then, the system highlighted a positive sentence instance for the result justification. The number of sentences to highlight was determined by a preset threshold empirically determined using the average accuracy.
RESULTS: The automatic evaluation of health news on the cost, harm, and conflict criteria achieved average area under the curve scores of 0.88, 0.76, and 0.73, respectively, after 50 repetitions of 10-fold cross-validation. We found that both approaches could successfully visualize the interpretation of the system but that the performance of the 2 approaches varied by criterion and highlighting the accuracy decreased as the number of highlighted sentences increased. When the threshold accuracy was ≥75%, this resulted in a visualization with a variable length ranging from 1 to 6 sentences.
CONCLUSIONS: We provided 2 approaches to interpret criteria-based health news evaluation models tested on 3 criteria. This method incorporated rule-based and statistical machine learning approaches. The results suggested that one might visually interpret an automatic criterion-based health news quality evaluation successfully using either approach; however, larger differences may arise when multiple quality-related criteria are considered. This study can increase public trust in computerized health information evaluation.

BACKGROUND: Widespread misinformation in web resources can lead to serious implications for individuals seeking health advice. Despite that, information retrieval models are often focused only on the query-document relevance dimension to rank results.
OBJECTIVE: We investigate a multidimensional information quality retrieval model based on deep learning to enhance the effectiveness of online health care information search results.
METHODS: In this study, we simulated online health information search scenarios with a topic set of 32 different health-related inquiries and a corpus containing 1 billion web documents from the April 2019 snapshot of Common Crawl. Using state-of-the-art pretrained language models, we assessed the quality of the retrieved documents according to their usefulness, supportiveness, and credibility dimensions for a given search query on 6030 human-annotated, query-document pairs. We evaluated this approach using transfer learning and more specific domain adaptation techniques.
RESULTS: In the transfer learning setting, the usefulness model provided the largest distinction between help- and harm-compatible documents, with a difference of +5.6%, leading to a majority of helpful documents in the top 10 retrieved. The supportiveness model achieved the best harm compatibility (+2.4%), while the combination of usefulness, supportiveness, and credibility models achieved the largest distinction between help- and harm-compatibility on helpful topics (+16.9%). In the domain adaptation setting, the linear combination of different models showed robust performance, with help-harm compatibility above +4.4% for all dimensions and going as high as +6.8%.
CONCLUSIONS: These results suggest that integrating automatic ranking models created for specific information quality dimensions can increase the effectiveness of health-related information retrieval. Thus, our approach could be used to enhance searches made by individuals seeking online health information.

TikTok is one of the most-used and fastest-growing social media platforms in the world, and recent reports indicate that it has become an increasingly popular source of news and information in the United States. These trends have important implications for public health because an abundance of health information exists on the platform. Women are among the largest group of TikTok users in the United States and may be especially affected by the dissemination of health information on TikTok. Prior research has shown that women are not only more likely to look for information on the internet but are also more likely to have their health-related behaviors and perceptions affected by their involvement with social media.
We conducted a survey of young women in the United States to better understand their use of TikTok for health information as well as their perceptions of TikTok\'s health information and health communication sources.
A web-based survey of US women aged 18 to 29 years (N=1172) was conducted in April-May 2023. The sample was recruited from a Qualtrics research panel and 2 public universities in the United States.
The results indicate that the majority of young women in the United States who have used TikTok have obtained health information from the platform either intentionally (672/1026, 65.5%) or unintentionally (948/1026, 92.4%). Age (959/1026, 93.47%; r=0.30; P<.001), education (959/1026, 93.47%; ρ=0.10; P=.001), and TikTok intensity (ie, participants\' emotional connectedness to TikTok and TikTok\'s integration into their daily lives; 959/1026, 93.47%; r=0.32; P<.001) were positively correlated with overall credibility perceptions of the health information. Nearly the entire sample reported that they think that misinformation is prevalent on TikTok to at least some extent (1007/1026, 98.15%), but a third-person effect was found because the young women reported that they believe that other people are more susceptible to health misinformation on TikTok than they personally are (t1025=21.16; P<.001). Both health professionals and general users were common sources of health information on TikTok: 93.08% (955/1026) of the participants indicated that they had obtained health information from a health professional, and 93.86% (963/1026) indicated that they had obtained health information from a general user. The respondents showed greater preference for health information from health professionals (vs general users; t1025=23.75; P<.001); the respondents also reported obtaining health information from health professionals more often than from general users (t1025=8.13; P<.001), and they were more likely to act on health information from health professionals (vs general users; t1025=12.74; P<.001).
The findings suggest that health professionals and health communication scholars need to proactively consider using TikTok as a platform for disseminating health information to young women because young women are obtaining health information from TikTok and prefer information from health professionals.

BACKGROUND: Health misinformation (HM) has emerged as a prominent social issue in recent years, driven by declining public trust, popularisation of digital media platforms and escalating public health crisis. Since the Covid-19 pandemic, HM has raised critical concerns due to its significant impacts on both individuals and society as a whole. A comprehensive understanding of HM and HM-related studies would be instrumental in identifying possible solutions to address HM and the associated challenges.
METHODS: Following the PRISMA procedure, 11,739 papers published from January 2013 to December 2022 were retrieved from five electronic databases, and 813 papers matching the inclusion criteria were retained for further analysis. This article critically reviewed HM-related studies, detailing the factors facilitating HM creation and dissemination, negative impacts of HM, solutions to HM, and research methods employed in those studies.
RESULTS: A growing number of studies have focused on HM since 2013. Results of this study highlight that trust plays a significant while latent role in the circuits of HM, facilitating the creation and dissemination of HM, exacerbating the negative impacts of HM and amplifying the difficulty in addressing HM.
CONCLUSIONS: For health authorities and governmental institutions, it is essential to systematically build public trust in order to reduce the probability of individuals acceptation of HM and to improve the effectiveness of misinformation correction. Future studies should pay more attention to the role of trust in how to address HM.

Correction is an important tool to reduce the negative impact of health misinformation on social media. In the era of \"I share, therefore I am\" social media, users actively share corrective information to achieve the \"anti-convincing\" effect of health misinformation. Focusing on the local Chinese context, this study constructs a structural equation model using social capital as a mediating variable to explore whether usage of Chinese users\' social media can promote corrective information sharing by influencing the structural, cognitive, and relational dimensions of social capital and the role of health literacy in corrective information sharing. It was found that social media use did not significantly affect corrective information share willingness but significantly influenced share willingness through social interaction connections, trust, and shared experiences, and share willingness significantly influenced sharing behavior. The moderating effect showed that health literacy played a significant moderating effect in the influence of corrective information share willingness on sharing behavior. This study introduces the three dimensions of social capital at the theoretical level and finds that users will share corrective information for the purpose of social capital accumulation. It also provides empirical evidence for specific practices, including improving users\' health literacy and actively mobilizing them to participate in the blocking and management of health misinformation in social media.

BACKGROUND: Health misinformation, which was particularly prevalent during the COVID-19 pandemic, hampers public health initiatives. Spanish-speaking communities in the San Francisco Bay Area may be especially affected due to low digital health literacy and skepticism towards science and healthcare experts. Our study aims to develop a checklist to counter misinformation, grounded in community insights.
METHODS: We adopted a multistage approach to understanding barriers to COVID-19 vaccine uptake in Spanish-speaking populations in Alameda and San Francisco counties. Initial work included key informant and community interviews. Partnering with a community-based organization (CBO), we organized co-design workshops in July 2022 to develop a practical tool for identifying misinformation. Template analysis identified key themes for actionable steps, such as source evaluation and content assessment. From this, we developed a Spanish-language checklist.
RESULTS: During formative interviews, misinformation was identified as a major obstacle to vaccine uptake. Three co-design workshops with 15 Spanish-speaking women resulted in a 10-step checklist for tackling health misinformation. Participants highlighted the need for scrutinizing sources and assessing messenger credibility, and cues in visual content that could instill fear. The checklist offers a pragmatic approach to source verification and information assessment, supplemented by resources from local CBOs.
CONCLUSIONS: We have co-created a targeted checklist for Spanish-speaking communities to identify and counter health misinformation. Such specialized tools are essential for populations that are more susceptible to misinformation, enabling them to differentiate between credible and non-credible information.

UNASSIGNED: ChatGPT is a generative artificial intelligence chatbot that uses natural language processing to understand and execute prompts in a human-like manner. While the chatbot has become popular as a source of information among the public, experts have expressed concerns about the number of false and misleading statements made by ChatGPT. Many people search online for information about self-managed medication abortion, which has become even more common following the overturning of Roe v. Wade. It is likely that ChatGPT is also being used as a source of this information; however, little is known about its accuracy.
UNASSIGNED: To assess the accuracy of ChatGPT responses to common questions regarding self-managed abortion safety and the process of using abortion pills.
UNASSIGNED: We prompted ChatGPT with 65 questions about self-managed medication abortion, which produced approximately 11,000 words of text. We qualitatively coded all data in MAXQDA and performed thematic analysis.
UNASSIGNED: ChatGPT responses correctly described clinician-managed medication abortion as both safe and effective. In contrast, self-managed medication abortion was inaccurately described as dangerous and associated with an increase in the risk of complications, which was attributed to the lack of clinician supervision.
UNASSIGNED: ChatGPT repeatedly provided responses that overstated the risk of complications associated with self-managed medication abortion in ways that directly contradict the expansive body of evidence demonstrating that self-managed medication abortion is both safe and effective. The chatbot\'s tendency to perpetuate health misinformation and associated stigma regarding self-managed medication abortions poses a threat to public health and reproductive autonomy.