Advice reification, learning, and emergent collective intelligence in online health support communities

Buffering against exposure to mental health misinformation in online communities on Facebook: the interplay of depression literacy and expert moderation

BACKGROUND

The proliferation of health misinformation on social media is a growing public health concern. Online communities for mental health (OCMHs) are also considered an outlet for exposure to misinformation. This study explored the impact of the self-reported volume of exposure to mental health misinformation in misinformation agreement and the moderating effects of depression literacy and type of OCMHs participation (expert vs. peer-led).

METHODS

Participants (n = 403) were recruited in Italian-speaking OCMHs on Facebook. We conducted regression analyses using PROCESS macro (moderated moderation, Model 3). Measures included: the Depression Literacy Questionnaire (Griffiths et al., 2004), the self-reported misinformation exposure in the OCMHs (3 items), and misinformation agreement with the exposure items (3 items). Whether participants were members of expert or peer-led OCMHs was also investigated.

RESULTS

The final model explained the 12% variance in the agreement. There was a positive and significant relationship between misinformation exposure and misinformation agreement (β = 0.3221, p < .001), a significant two-way interaction between misinformation exposure and depression literacy (β = - 0.2179, p = .0014 ), and between self-reported misinformation exposure and type of OCMH (β = - 0.2322, p = .0254), such that at higher levels of depression literacy and in case of participation to expert-led OCMHs, the relationship misinformation exposure-misinformation agreement was weaker. Finally, a three-way interaction was found (β = 0.2497, p = .0144) that showed that depression literacy moderated the positive relationship between misinformation exposure and misinformation agreement such that the more misinformation participants were exposed to, the more they agreed with it unless they had higher levels of depression literacy; this, however, occurred only if they participated in peer-led groups.

CONCLUSIONS

Results provide evidence that the more members reported being exposed to mental health misinformation, the more they tended to agree with it, however this was only visible when participants had lower depression literacy and were participating in peer-led OCMHs. Results of this study suggest that both internal factors (i.e., high depression literacy) and external factors (the type of online community individuals were participating in) can buffer the negative effects of misinformation exposure. It also suggests that increasing depression literacy and expert community moderation could curb the negative consequences of misinformation exposure related to mental health. Results will guide interventions to mitigate the effects of misinformation in OCMHs, including encouraging health professionals in their administration and implementing health education programs.

Content Analysis of Social Support, and Disease Framing of Klinefelter Syndrome and Down Syndrome: Social Support, Framing and Decision Tendencies

The Internet plays a crucial role for people searching for health information. In the context of parents faced with a prenatal diagnosis of a chromosomal abnormality, Internet-based resources convey critical information and attitudes that bear on the process of healthcare decision-making. In this study, we collected and analyzed 268 websites for Klinefelter syndrome and Down syndrome, which are two of the most common genetic conditions marked by an abnormal number of chromosomes. While these two syndromes are quite different in terms of their associated symptoms and health consequences, they are commonly screened for, have similar incidence rates, and parents who are presented with prenatal diagnosis of the two conditions terminate pregnancies at similar rates. The study found substantial differences in framings of information, and social support available online for Klinefelter syndrome and Down syndrome. This study's key finding supports the argument that social support and framing are important mechanisms that mediate online information and perceptions of information utility, hope and decision tendency. The study extends theories and literature related to information framing in a mass media context, social support, and cognitive information processing and applies to investigating online health information, social support and perceptions.

Group Structure and Information Distribution on the Emergence of Collective Intelligence

More and more decision-making problems are being solved by groups. Collective intelligence is the ability of groups to perform well when solving complex problems. Thus, it is important to encourage collective intelligence to emerge from groups. In this study, we explore how two critical characteristics of groups, that is, group structure and individual knowledge in groups, influence the emergence of collective intelligence. To do this, we propose a measure for group structure using the collaboration network of a group and a measure for the distribution of individual knowledge in groups. Group structure is measured based on the intensities of links and whether the network is hierarchical or flat. The distribution of individual knowledge is measured from the perspective of whether group information is shared or unique. Social interactions among group members and individual changes in opinion are modeled based on a simulation technique. We find that unbalanced information distribution undermines group performance, whereas group structure can modify the effect of information distribution. We also find that groups with broadly distributed knowledge are good at solving complex problems.

Funding: This work was supported by the National Natural Science Foundation of China [Grants 72171158, 71771156 and 71971145].

Determining Factors Affecting the Users' Participation of Online Health Communities: An Integrated Framework of Social Capital and Social Support

As the national awareness of health keeps deepening, online health communities (OHCs) have achieved rapid development. Users' participation is critically important to the sustainable development of OHCs. Nevertheless, users usually lack the motive for participation. Based on the social capital theory, this research examines factors influencing users' participation in OHCs. The purpose of this research is to find out decisive factors that influence users' participation in OHCs, enrich the understanding of users' participation in OHCs, and help OHCs address the issue of sustainable development. The research model was empirically tested using 1277 responses from an online survey conducted in China. Data was analyzed using the structural equation modeling (SEM). We found informational support and emotional support to have significant direct effects over the structural capital, relational capital and cognitive capital of OHCs. Meanwhile, it is observed that relational capital and cognitive capital degree have a significant influence on knowledge acquisition and knowledge contribution of OHCs. For researchers this study provides a basis for further refinement of individual models of users' participation. For practitioners, understanding the social capital is crucial to users' knowledge acquisition and knowledge contribution that achieve high participation in OHCs.

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

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.

IoT Based Health—Related Topic Recognition from Emerging Online Health Community (Med Help) Using Machine Learning Technique

The unprompted patient’s and inimitable physician’s experience shared on online health communities (OHCs) contain a wealth of unexploited knowledge. Med Help and eHealth are some of the online health communities offering new insights and solutions to all health issues. Diabetes mellitus (DM), thyroid disorders and tuberculosis (TB) are chronic diseases increasing rapidly every year. As part of the project described in this article comments related to the diseases from Med Help were collected. The comments contain the patient and doctor discussions in an unstructured format. The sematic vision of the internet of things (IoT) plays a vital role in organizing the collected data. We pre-processed the data using standard natural language processing techniques and extracted the essential features of the words using the chi-squared test. After preprocessing the documents, we clustered them using the K-means++ algorithm, which is a popular centroid-based unsupervised iterative machine learning algorithm. A generative probabilistic model (LDA) was used to identify the essential topic in each cluster. This type of framework will empower the patients and doctors to identify the similarity and dissimilarity about the various diseases and important keywords among the diseases in the form of symptoms, medical tests and habits.

Perception of peer advice in online health communities: Access to lay expertise

When seeking advice online about health concerns, forums dedicated to medical themes are increasingly becoming an appreciated source of information for many individuals. In online health communities, patients can ask questions or otherwise seek advice that is particularly relevant to them. While they may find some of the advice useful, other advice may be perceived as less valuable. By studying the advice-seeking, advice-giving, and advice-evaluation behaviours in one of the largest online health communities in Europe, this paper looks at what determines which advice is perceived as helpful, and why. Drawing on network theory, we analysed the interaction data of 108,569 users over twelve consecutive years based on all publicly available information of an established Q&A online health community. Utilising zero-inflated negative binominal modelling, our results show that advice received from others, who have similar predominant interests, is valued more when reaching out for lay expertise. If this advice is given by peers, who can also draw on expertise from other health areas, allowing for a combination of diverse "lay" expertise, the advice is valued even more. Advice provided by those who are quick to obtain the latest knowledge available in the larger community further reinforces these effects. Our findings offer an original view to understand the influence of lay expertise exchanged via online health communities and hold implications for both policy-makers and medical practitioners regarding their approach to patient-initiated use of social media for health-related reasons.

The Role of Pharmacist in the Health Care System: Current Scenario in India

Pharmacists are society's specialists on drugs. The Pharmacist of today is a drug-maker, drug-dispenser, drug-custodian, patient-counselor, drug-researcher, and drug-educator and above all an honest and patriotic citizen. The techno-proficient foundation of the drug expert gives him/her the certainty of providing services with a moral way to deal with the satisfaction of patients. The consecrated qualities are required to be cherished and professed by the pharmacist. Pharmacists assume a significant job in giving health care services, benefits by means of community pharmacy services in rural areas where physicians are not accessible or where physician services are unreasonably expensive for meeting the health care necessities. The paper at that point recognizes how pharmacists give expanded services, identifies key challenges and barriers, and suggests rules and regulations that could help secure open doors for pharmacists to play out an extended job.

Modeling Impact of Word of Mouth and E-Government on Online Social Presence during COVID-19 Outbreak: A Multi-Mediation Approach

Although social presence plays an essential role under general conditions, its role becomes significant for societal protection during the quarantine period in epidemic outbreak. In this study, we attempted to identify the role of E-government and COVID-19 word of mouth in terms of their direct impact on online social presence during the outbreak as well as their impacts mediated by epidemic protection and attitudes toward epidemic outbreaks. For this purpose, a unique multi-mediation model is proposed to provide a new direction for research in the field of epidemic outbreaks and their control. Through random sampling, an online survey was conducted and data from 683participants were analyzed. Partial least squares structural equation modeling was used to test the relationships between the variables of interest. The study results revealed that the roles of E-government and COVID-19 word of mouth are positively related to online social presence during the outbreak. Epidemic protection and attitude toward epidemic outbreak were found to positively moderate the impact of the role of E-government and COVID-19 word of mouth on online social presence during the outbreak. The key findings of this study have both practical and academic implications.

Topic Analysis of UK Fitness to Practise Cases: What Lessons Can Be Learnt?

Background: Fitness to practise (FtP) impairment (failure of a healthcare professional to demonstrate skills, knowledge, character and/or health required for their job) can compromise patient safety, the profession's reputation, and an individual's career. In the United Kingdom (UK), various healthcare professionals' FtP cases (documents about the panel hearing(s) and outcome(s) relating to the alleged FtP impairment) are publicly available, yet reviewing these to learn lessons may be time-consuming given the number of cases across the professions and amount of text in each. We aimed to demonstrate how machine learning facilitated the examination of such cases (at uni- and multi-professional level), involving UK dental, medical, nursing and pharmacy professionals. Methods: Cases dating from August 2017 to June 2019 were downloaded (577 dental, 481 medical, 2199 nursing and 63 pharmacy) and converted to text files. A topic analysis method (non-negative matrix factorization; machine learning) was employed for data analysis. Results: Identified topics were criminal offences; dishonesty (fraud and theft); drug possession/supply; English language; indemnity insurance; patient care (including incompetence) and personal behavior (aggression, sexual conduct and substance misuse). The most frequently identified topic for dental, medical and nursing professions was patient care whereas for pharmacy, it was criminal offences. Conclusions: While commonalities exist, each has different priorities which professional and educational organizations should strive to address.