Search

Purpose: The purpose of this review is to provide a conceptual framework of consumer engagement behavior in the value co-creation process of healthcare services, and further understand the current knowledge maps and advances. Design/methodology/approach: Specifically, the scoping review methodology is used to synthesize the extant findings. The authors first develop the inclusion/exclusion criteria to evaluate the source material for the review; then, the authors further conduct the literature refinement to select the final data sample. As such, the authors extract and analyze the information derived from these articles. Findings: The authors found most related studies focus on exploring patients' engagement behavior in the value co-creation process, especially those with chronic disease; the findings also reveal that consumers are most likely to engage in the value co-creation process of healthcare services by seeking or sharing health information; also, consumers engagement behavior is mainly driven by individual, interactive, and technological factors; moreover, consumer engagement in the value co-creation of healthcare services are more likely to achieve positive health and behavioral outcomes. Originality/value: The role of consumers has gradually shifted from that of passive recipients to that of active participants in the healthcare value co-creation process. Consumer engagement behavior is the key premise for the realization of healthcare value co-creation, and it has received increasing attention both academically and practically. By unearthing the conceptual framework of consumer engagement behavior in the value co-creation process of healthcare services, this study provides a systematic understanding and serves as a useful resource for future research and practice. [ABSTRACT FROM AUTHOR]

Purpose: Free knowledge sharing in the online health community has been widely documented. However, whether free knowledge sharing can help physicians accumulate popularity and further the accumulated popularity can help physicians attract patients remain unclear. To unveil these gaps, this study aims to examine how physicians' popularity are affected by their free knowledge sharing, how the relationship between free knowledge sharing and popularity is moderated by professional capital, and how the popularity finally impacts patients' attraction. Design/methodology/approach: The authors collect a panel dataset from Hepatitis B within an online health community platform with 10,888 observations from April 2020 to August 2020. The authors develop a model that integrates free knowledge sharing, popularity, professional capital, and patients' attraction. The hierarchical regression model is used to for examining the impact of free knowledge sharing on physicians' popularity and further investigating the impact of popularity on patients' attraction. Findings: The authors find that the quantity of articles acted as the heuristic cue and the quality of articles acted as the systematic cue have positive effect on physicians' popularity, and this effect is strengthened by physicians' professional capital. Furthermore, physicians' popularity positively influences their patients' attraction. Originality/value: This study reveals the aggregation of physicians' popularity and patients' attraction within online health communities and provides practical implications for managers in online health communities. [ABSTRACT FROM AUTHOR]

Online health communities (OHCs) play an important role in enabling patients to exchange information and obtain social support from each other. However, do OHC interactions always benefit patients? In this research, we investigate different mechanisms by which OHC content may affect patients’ emotions. Specifically, we notice users can read not only emotional support intended to help them but also emotional support targeting other persons or posts that are not intended to generate any emotional support (auxiliary content). Drawing from emotional contagion theories, we argue that even though emotional support may benefit targeted support seekers, it could have a negative impact on the emotions of other support seekers. Our empirical study on an OHC for depression patients supports these arguments. Our findings are new to the literature and have critical practical implications since they suggest that we should carefully manage OHC-based interventions for depression patients to avoid unintended consequences. We design a novel deep learning model to differentiate emotional support from auxiliary content. Such differentiation is critical for identifying the negative effect of emotional support on unintended recipients. We also discuss options to alter the intervention volume, length, and frequency to tackle the challenge of the negative effect. [ABSTRACT FROM AUTHOR]

Internet medical and health services are services that require high levels of trust. We identify factors that influence user trust based on trust source credibility model and trust transitivity model and explore differences in initial and continuous trust formation among users of online health communities from the perspective of trust dynamics. We found that the type of service provision, etc., which represents benevolence trust, whether doctors provide personal photographs, which represents integrity trust, and the overall recommendation popularity, number of electronic gifts, thank you letters, patient votes and positive service quality ratings, which represent trust transitivity, all significantly affect users' initial and continuous trust, but there are differences in the degree of influence on the two types of trust. The doctor's title in the ability trust only has a significant effect on users' initial trust and does not have a significant effect on continuous trust. The three dimensions of ability, benevolence and integrity in the trust source credibility model have a greater impact on users' initial trust than on their continuous trust, while trust transitivity has a greater impact on users' continuous trust than on their initial trust. Overall, in addition to traditional influences such as doctor's title, online information can also support users' decision making, indicating that online health communities can provide useful information to alleviate the current information asymmetry between doctors and patients. [ABSTRACT FROM AUTHOR]

Background: Amyotrophic Lateral Sclerosis (ALS) significantly impacts the lives of people with the diagnosis and their families. A supportive social environment is important for people with ALS to adopt effective coping strategies and health behaviours, and reduce depressive symptoms. Peer support can provide a supportive social environment and can happen inperson and online. Advantages of online peer support are that people can engage from their own home, at their own time and pace, and that it offers a variety of different platforms and modes of communication. Objectives: To (1) explore the benefits and challenges of online peer support for people with ALS, and (2) identify successful elements of online peer support for people with ALS. Methods: The method selected for this systematic review was a narrative synthesis. Six databases were systematically searched in April 2020 for articles published between 1989 and 2020. The search was updated in June 2022. The quality of the included studies was assessed with the Critical Appraisal Skills Programme qualitative research checklist. Results: 10,987 unique articles were identified through the systematic database search. Of those, 9 were included in this review. One of the main benefits of online peer support was that people could communicate using text rather than needing verbal communication, which can be challenging for some with ALS. Successful elements included using profile pages and graphics to identify others with similar or relevant experiences. Challenges included ALS symptoms which could make it difficult to use technological devices. Conclusions: Peer support can provide a non-judgmental and supportive environment for people with ALS, in which they can exchange experiences and emotional support, which can help people in developing adaptive coping strategies. However, ALS symptoms may make it more difficult for people to use technological devices and engage in online peer support. More research is needed to identify what kind of specific barriers people with ALS experience, and how these could be overcome. [ABSTRACT FROM AUTHOR]

Introduction: This study investigates the geographical distribution and fractal characteristics of the medical service network in China, using the "Good Doctor website" as a case study. Methods: Data for this study were extracted from the Good Doctor website Health Community. A two-tiered hierarchical network model was developed to analyze the geographical distribution and fractal characteristics of the medical service network in China. Results: Results unveil the hierarchical nature of hospital distribution and the interconnectivity among healthcare institutions. Shandong Province as a central node within the national hospital network, and networks of secondary hospitals show significant self-similarity and scale-free properties. Discussion: The small world and fractal characteristics shed light on the rapid dissemination of medical information and the robustness of the healthcare network. The results offer a novel perspective for understanding and optimizing the distribution of medical resources, and help improve the efficiency of healthcare services supply. [ABSTRACT FROM AUTHOR]

BackgroundAmyotrophic Lateral Sclerosis (ALS) significantly impacts the lives of people with the diagnosis and their families. A supportive social environment is important for people with ALS to adopt effective coping strategies and health behaviours, and reduce depressive symptoms. Peer support can provide a supportive social environment and can happen in-person and online. Advantages of online peer support are that people can engage from their own home, at their own time and pace, and that it offers a variety of different platforms and modes of communication.ObjectivesTo (1) explore the benefits and challenges of online peer support for people with ALS, and (2) identify successful elements of online peer support for people with ALS.MethodsThe method selected for this systematic review was a narrative synthesis. Six databases were systematically searched in April 2020 for articles published between 1989 and 2020. The search was updated in June 2022. The quality of the included studies was assessed with the Critical Appraisal Skills Programme qualitative research checklist.Results10,987 unique articles were identified through the systematic database search. Of those, 9 were included in this review. One of the main benefits of online peer support was that people could communicate using text rather than needing verbal communication, which can be challenging for some with ALS. Successful elements included using profile pages and graphics to identify others with similar or relevant experiences. Challenges included ALS symptoms which could make it difficult to use technological devices.ConclusionsPeer support can provide a non-judgmental and supportive environment for people with ALS, in which they can exchange experiences and emotional support, which can help people in developing adaptive coping strategies. However, ALS symptoms may make it more difficult for people to use technological devices and engage in online peer support. More research is needed to identify what kind of specific barriers people with ALS experience, and how these could be overcome.

Online health communities (OHC) consist of individuals with shared health-related interests who exchange health-related information among themselves and for the benefit of others. Unfortunately, a notable issue within these communities is the dissemination of a substantial volume of inaccurate health information by various online health groups. Nevertheless, a dearth of research examining the impact of information-seeking activities within OHCs exists. This study aimed to examine the influence of direct and indirect health information-seeking behaviors, specifically among users diagnosed with Type 2 diabetes who have reported complications in OHC, also called claims. Employing association rule mining (ARM) techniques, user data from PatientsLikeMe were extracted to capture information on users' reported complications subsequent to being diagnosed with Type 2 diabetes (N = 6371). Subsequently, we utilized zero-inflated negative binomial regression (ZINB) to evaluate the effect of direct and indirect information search activities on false notes, including their interaction of them. The outcomes of this investigation have the potential to offer patients valuable insights regarding the reliability and trustworthiness of information derived from OHCs. [ABSTRACT FROM AUTHOR]

This study examines the determinants that drive the behavior of sharing health information within online health communities. Leveraging the Theory of Planned Behavior, the Technology Acceptance Model, and the "Knowledge-Attitude-Practice" theory, a comprehensive model elucidating the key elements that sway the health information-sharing behavior among users of online health communities is designed. This model is validated through Structural Equation Modeling (SEM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA). Findings derived from the SEM suggest that perceived ease of use, perceived usefulness, perceived trust, and perceived behavioral control exert a significant positive impact on attitudes towards health information sharing, the intention to share health information, and the actual health information-sharing behavior. The fsQCA unfolds two unique configuration path models that lead to the emergence of health information-sharing behavior: one predicated on perceived trust and sharing intention, and the other on perceived usefulness, behavioral control, and sharing attitude. This research provides invaluable insights, fostering a deeper comprehension of the dynamics involved in health information sharing within online communities, thereby directing the design of more effective health platforms to augment user engagement and enable informed health decisions. [ABSTRACT FROM AUTHOR]

The rising prevalence of diabetes and the increasing awareness of self-health management have resulted in a surge in diabetes patients seeking health information and emotional support in online health communities. Consequently, there is a vast database of patient consultation information in these online health communities. However, due to the heterogeneity and incompleteness of the content, mining medical information and patient health data from these communities can be a challenge. To address this issue, we built the RoBERTa-BiLSTM-CRF (RBC) model for identifying entities in the online health community of diabetes. We selected 1889 question–answer texts from the most active online health community in China, Good Doctor Online, and used these public data to identify five types of entities. In addition, we conducted a comparative evaluation with three other commonly used models to validate the performance of our proposed model, including RoBERTa-CRF (RC), BilSTM-CRF (BC), and RoBERTa-Softmax (RS). The results showed that the RBC model achieved excellent performance on the test set, with an accuracy of 81.2% and an F1 score of 80.7%, outperforming the performance of traditional entity recognition models in named entity recognition in online medical communities for doctors and diabetes patients. The high performance of entity recognition in online health communities will provide a crucial knowledge source for constructing medical knowledge graphs. This integration would help alleviate the growing demand for medical consultations and the strain on healthcare resources, while assisting healthcare professionals in making informed decisions and providing personalized services to patients. [ABSTRACT FROM AUTHOR]

Drawing on observations of a Chinese online depression community, this article explored the members' sense making of depression by analyzing their narrative accounts of depression. Four types of sense making were predominant among the depression sufferers: complaining, regret, superiority, and discovery. The complaining narrative is the members' telling about the pain caused by family (parental control or neglect), school bullying, stress from study or work, and social norms. The regret narrative is the members' reflection on their habit of perfectionism and lack of self-disclosure. The superiority narrative is the members' attribution of depression to their intelligence and morality that surpass the average people. The discovery narrative is the members' novel understanding of the self, significant others, and key events. The findings suggest that the social and psychological explanation of the causes of depression, instead of the medical model, is popular among the Chinese patients. Their stories of depression are also stories of marginalization, visions for the future, and realizing the normalization of identity as depression patients. The findings have implications for public policy around support for mental health. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Library & Information Science is the property of Anhui University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Academic Library & Information Science is the property of Anhui University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Today's diverse health needs place greater demands on physicians. However, individual doctors have limited capabilities and may encounter many unsolvable medical problems. The physician online community provides a platform for physicians to communicate with each other and help each other. Physicians can post for help about problems they encounter at work. The number of responses to physicians' posts is critical to whether or not the problem is resolved. This study collected information on 13,226 posts from a well-known physician online community in China to analyze the factors that influence the number of post replies. In the analysis of the post content of the physician online community, this study innovatively introduces word usage features in the medical field. TextMind was used to extract the rate of several types of words in posts that frequently appear when describing medical information. Ultimately, we found that the rate of time words, visual words, auditory words, and physiological process words used in posts had a positive and significant effect on the number of post responses. A series of new post features has been found to have an impact on the number of post replies in physician online communities. This finding is beneficial for physicians to quickly obtain peer assistance through online platforms, increasing the likelihood of solving workplace challenges and improving physician care, as well as the success of physician online communities. [ABSTRACT FROM AUTHOR]

Background: Online health communities (OHCs) provide a new channel for users to obtain more health-related information and support, playing an important role in alleviating hospital congestion and uneven medical resource distribution, especially during the COVID-19 pandemic in China. An in-depth study of users' continuous usage is of great value for the long-term development of OHCs. Objective: The purpose of this study is to explore the factors that influence users' continuous usage in online health communities based on the theory of planned behavior (TPB) and social cognitive theory (SCT). Methods: Data from 480 users with experience in online health communities were collected through a questionnaire survey, and structural equations were applied to verify the model hypotheses empirically. Results: Self-efficacy and controllability have significant effects on users' continuous intention; attitude has a significant relationship with continuous intention; social norms have a positive effect on continuous intention. Moreover, the relationship between continuous intention and behavior is positive. Self-efficacy and outcome expectations have significant positive associations with continuous usage. Finally, system quality, information quality, and social interaction ties have significant and positive relationships to continuous usage. Conclusion: To improve the level of user's continuous usage, online health service providers can improve the quality of the community by organizing the website's page layout, navigation menus, and site elements to ensure users quickly search and find what they want meanwhile try to change people's cognition gradually, in addition, decision and policymakers should provide more favorable policies to stimulate and help provider in building and managing strategic plans for sustaining a thriving online community. A supportive climate in society through public service advertisements and others for the sake of OHCs is necessary. Limitations: (1) This study collected data through a cross-sectional survey. Thus, it lacked the process of capturing the changes in participants' attitudes toward all variables. (2) The environmental factors in SCT theory need to be more comprehensive, containing online factors without offline factors. (3) The dates were obtained from China, which neglects the different cultural content. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Emotional experience is a purely subjective feeling of the interaction between users and products. Exploring user participation behavior and influencing factors in online health communities under the influence of user emotional experience has obvious significance for retaining users. [Method/Process] A total of 25 users of the online health platform were selected as the research objects. The semi-structured interview method was used, and the basic information of the interviewees was combined with the questionnaire. With the help of grounded theory, the original interview data were coded, and the factors influencing user participation in online health communities were analyzed. Then, the emotional experience and feedback mechanism model of user participation in online health communities was constructed by combining the "emotion-cognitive recursive model". Finally, effective cases were screened from the questionnaire, and the configuration of the influencing factors of user participation behavior and affective feedback was explored based on the principle of qualitative comparative analysis and calculation of fuzzy sets. [Results/Conclusions] Through the grounded theory coding, six factors affecting user participation behavior are obtained: risk perception, platform cognition, demand motivation, platform experience, user emotion and environment. Then combined with the cognitive-affective model, the seven main categories derived from the rooted coding were divided into three dimensions: cognitive assessment, affective experience and participatory behavior. The feedback mechanism of emotional experience after participating behavior is further studied. It is found that the cognitive evaluation including risk perception, platform cognition and demand motivation affects the emotional experience of users, thus affecting their actual participation behavior. In the process of user participation in online health community, the factors of cognitive assessment, emotional experience and participation behavior interact with each other, and have a two-way effect on user participation behavior and user cognitive assessment. Further verification by fuzzy set qualitative comparative analysis (fsQCA) shows that among the six factors affecting user participation behavior, platform experience and demand motivation are the core conditions affecting user participation in online health community, and the three single factor variables of user emotion, environment and platform cognition do not necessarily affect the generation of user participation behavior. The extraction of influencing factors and the definition of user behavior are not comprehensive, and some real variables and key engagement behaviors may be overlooked. In addition, there is an imbalance in the number of samples on each platform. Follow-up related research can improve the pertinence of the platform and expand the sample capacity.

Plain English summary The COVID-19 pandemic has made it difficult to conduct research in person, but qualitative and quantitative data remain critical for developing research design. To gather and understand vaccine perceptions, opinions, and behaviors in a real time manner, The Virtual Engagement Research Community (VERC) was launched. As of October 2021, the VERC had 72 members who had the opportunity to participate in research events such as surveys, polls, discussion topics, and moderated chats in a private, invitation-only virtual community. The online format offered researchers the opportunity to adapt the types and frequency of engagements, fine-tune the research questions based on community responses and current events, and adjust the analysis and reporting methodology as needed to increase participation and maximize the value of the VERC. The success of the VERC and the lessons learned support the use of online communities to enhance mixed methods research as a valuable strategy for other health researchers.

[Purpose/Significance] The psychological contract between online communities and users in the communities determines users' EVLN behaviors, including four behaviors of exit, voice, loyalty and neglect. Related research has become a hot topic. However, most existing studies focus on questionnaire and other survey methods, and the survey results are highly subjective, lacking objective data analysis from the perspective of big data, so as to deeply analyze the mechanism of user behaviors. [Method/process] In this study, the Hudescendant collector software was used to crawl data from Sweet Home Diabetes Forum, and the psychological contract theory was used as the entry point. Through regression analysis of crawled objective data, this paper discussed the correlation between information support and emotional support of online health community users and transactional psychological contract and relational psychological contract, as well as the correlation between the two psychological contracts and the subsequent use behavior EVLN. [Result/Conclusion] The results show that :(1) transactional psychological contract has an impact on users' enthusiasm and initiative, but is not a direct factor that causes users to withdraw or ignore communities directly. (2) Information support and emotional support are important factors that affect users' psychological contract satisfaction; (3) Whether the relational psychological contract is satisfied or not will not cause users to directly quit the online health community. The managers of online health communities can promote users' two positive behaviors of voice behavior and loyalty and realize the sustainable development of online health communities.

Purpose: The authors empirically examined social capital factors affecting patients' social support acquisition with the aim of providing guidance to patients seeking social support online. Design/methodology/approach: The authors used social network analysis to extract data about social capital factors from online health communities and text mining to identify forms of informational support and emotional support grounded in online, text-based communication. Moreover, the authors employed a random coefficient model to understand the dynamic influence of social capital factors on both informational and emotional support. Findings: The results from the empirical analyses show that structural connections have a lasting impact on the acquisition of both types of support; that is, social connections developed in the past will have an effect on the future. For relational capital, strong ties were less important; the quantity of connections mattered more than the quality when acquiring informational support. The use of health-related language increased the amount of informational support acquired. Over time, patients gained increasing social support, which primarily came from the patients' historical threads, likely via searches from peers facilitated by accumulated social capital. Originality/value: The authors' research adds to the literature on social capital and social support in online health communities by exploring how the three dimensions of social capital affect social support acquisition. The authors' research also contributes to the online health care literature by examining social support from a dynamic perspective. Practically, the authors' findings provide guidance for patients on what decisions to make to acquire more social support. [ABSTRACT FROM AUTHOR]

Objective Online health communities (OHCs) have been identified as important outlets for social support and community connection for adolescents and young adults (AYAs) living with chronic illnesses. Despite evident benefits, there remains a gap in research on methods to maximize sustained patient engagement within OHCs. This study assessed per-patient daily commenting rates over time, as well as associations with program staff and volunteer-facilitated events and engagement. Materials and Methods We utilized data from 662 daily patient, volunteer, and staff comment totals within a Discord server hosted through the Streetlight at UF Health Streetlight Gaming and Online Team, between January 2019 and January 2022. Multilevel models were used to assess per-patient daily commenting rates and examine associations with OHC-level predictors of staff and volunteer-facilitated daily and seasonal events, as well as the number of daily active users. Results Per-patient comment rates showed an overall negative slope with time in most models. Unadjusted and adjusted growth curve models showed that daily events (β = .21), seasonal events (β = .18), and total daily active users (β = .09) were all significantly associated with increases in per-patient daily comment rates. Discussion Results suggest that social event facilitation strategies can be applied to increase AYA patient engagement in OHCs. Seasonal events and staff and volunteer engagement may be the effective means of maintaining engagement among long-term patients. Conclusion Our findings highlight the importance of staff and volunteer presence in OHCs in driving long-term patient engagement and in considering patient needs and perspectives in developing OHC features. [ABSTRACT FROM AUTHOR]

Online support groups (OSGs) provide caregivers of children with mental disorders, information, and advice, as well as the opportunity to exchange social support. This research explores the effects of social support on caregivers when they participate in OSGs. The research survey was conducted on OSGs for parents of children with mental disorders, including developmental delay, autism spectrum disorder, and attention-deficit/hyperactivity disorder. This study collected 204 questionnaires from caregivers for analysis. The results found that informational support and tangible support positively affect members' self-efficacy (SEF), while offering esteem support, emotional support, and companionship support, influencing positive affect (PA). Both SEF and PA improve with members' knowledge-sharing intentions and subjective well-being. In addition, PA impacts the release of stress and recovery from self-stigma. This study provides insights into members' behavior toward support groups. The findings also provide preliminary guidelines for health professionals in adopting strategies to support caregivers. [ABSTRACT FROM AUTHOR]

ObjectiveTo investigate the relationship among information processing, risk/benefit perception and the COVID-19 vaccination intention of OHCs users with the heuristic-systematic model (HSM).MethodsThis study conducted a cross-sectional questionnaire via an online survey among Chinese adults. A structural equation model (SEM) was used to examine the research hypotheses.ResultsSystematic information processing positively influenced benefit perception, and heuristic information processing positively influenced risk perception. Benefit perception had a significant positive effect on users' vaccination intention. Risk perception had a negative impact on vaccination intention. Findings revealed that differences in information processing methods affect users' perceptions of risk and benefit, which decide their vaccination intention.ConclusionOnline health communities can provide more systematic cues and users should process information systematically to increase their perceived benefits, consequently increase their willingness to receive COVID-19 vaccine.

Online health communities (OHCs) have become more important to people's daily lives on the foundation of the voluntary sharing of electronic health records (EHRs). However, no in-depth investigation has been conducted concerning the influence of the perceptions of privacy protection among patients on their willingness to share EHRs. To fill the knowledge gap, by combining and modifying the theory of planned behavior (TPB) and the health belief model in the context of the privacy protection models implemented by OHCs, an empirical research method using a questionnaire approach is conducted to validate the hypotheses. The results indicate that the more positive a patient's attitude toward medical information sharing behavior is, the higher that patient's level of perceived behavioral control; in addition, the greater the social rewards obtained from this process, the more willing the patient is to share his or her EHRs after privacy protection measures are implemented by OHCs. Meanwhile, the effects of past positive experiences and disease severity have also been tested. The findings of this study can be used to promote patients' full participation in OHCs from a privacy perspective and offer theoretical and practical suggestions to promote the development of OHCs. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet and the normalization of COVID- 19 epidemic prevention and control, Online health communities (OHCs) have gradually become one of the important ways for people to obtain health information, and users have to go through a series of information processing when facing the massive amount of data. Understanding the factors influencing user information processing is necessary to promote users’ health literacy, health knowledge popularization and health behavior shaping. Based on the Heuristic-Systematic Model (HSM), Information Ecology Theory, Privacy Trade-Off and Self-Efficacy Theory,we constructed a model of factors influencing user information processing in online health communities. We found that information quality and emotional support had indirect effects on heuristic and systematic information processing, and these effects were mediated by privacy concerns and self-efficacy. In our research model, systematic information processing was most positively influenced directly by self-efficacy. Privacy concerns had a direct negative correlation with both dual information processing pathways. Therefore, OHCs managers should develop relevant regulations to ensure the information quality in OHCs and improve privacy protection services to promote user information processing by improving users’ self-efficacy and reducing their privacy concerns. Providing a user-friendly and interactive environment for users is also recommended to create more emotional support, thus facilitating more systematic information processing. [ABSTRACT FROM AUTHOR]

Aim: To identify a serial multiple mediation effect of social support in online health communities and collective empowerment on the relationship between diabetes‐related burden (DRB) and self‐efficacy, among mothers using a continuous glucose monitoring device for children with type 1 diabetes (T1D). Design: A secondary analysis, cross‐sectional, descriptive study. Methods: Data were obtained from 198 mothers of children with T1D via a web survey, from August to September 2020. Measures used were the Collective Empowerment in the Online Health Community Scale, a modified version of the Multidimensional Scale of Perceived Social Support, Problem Areas in Diabetes Survey—Parent Revised version and a modified version of the Maternal Self‐efficacy for Diabetes Management Scale. Data were analysed using SPSS 25.0, and PROCESS MACRO for SPSS v3.5. Results: A serial multiple mediation model was used. The indirect effects of both social support in online health communities and collective empowerment were identified in the relationship between DRB and diabetes self‐efficacy (DSE). However, there was no indirect effect of social support in online health communities on these relationships. Conclusion: These findings suggest that online social support alone has a limited role in chronic disease management self‐efficacy. Collective empowerment should be a strategic component in intervention development using online health communities to strengthen DSE in mothers of children with T1D. Impact This study provides novel insights into the functional mechanism of online health communities for T1D. Peer mentor coaching by parents of children with T1D effectively helps other children's parents with its recent diagnosis. The findings recognize a need for strategies enhancing collective empowerment among parents of children with T1D. Along with peer coaching, these strategies should strengthen knowledge of resources and methods to impact social change as well as resource mobilization for collective actions. [ABSTRACT FROM AUTHOR]

Since its beginning in the 1980s, the mental health rhetoric research (MHRR) has explored the rhetorical construction of mental illness from a genre perspective. Despite a growing body of research on the rhetorical study of mental health in the West, the discourse of depression has been rarely explored in the Chinese context. This study investigates a corpus of 3217 posts in the Baidu depression community in China with an attempt to identify typical rhetorical features of the posts in the depression community as a genre. We intend, through an exploration of the exigences, social actions, and consequences of the posts, to facilitate a better understanding of the depressed population in China. We argue that the social exigences for the posts in online depression communities can be regarded as a failure of the rhetorical environment in real life and that expressing emotions, exchanging information, establishing interpersonal relationships, and communicating across spatiotemporal boundaries are the typified social actions performed by the posts in the Baidu depression community. Moreover, as a genre, the posts have successfully created an ambience of collective intimacy. Taken together, the findings shed light on the rhetorical study of depression in China. • Applying the rhetorical genre theory to explore the field of health and medicine. • Facilitating a better understanding of the depression communities in China. • Exploring the rhetoric of health and medicine (RHM) in a non-western culture. • Employing a corpus-based approach with a large data set from real-world sources. [ABSTRACT FROM AUTHOR]

Online health communities (OHCs) as an essential means of patient education can significantly improve patients' health literacy and treatment outcomes. However, sustaining these social benefits brought by OHCs establishes the prerequisite that physicians can continuously share their knowledge on OHCs. Although previous studies have explored physicians' knowledge-sharing in OHCs, scholarly knowledge related to the means of motivating physicians to continue sharing their knowledge remains limited. Therefore, this study developed a research model based on motivation theory to explore the influence of practical benefits, psychological rewards, and perceived connectedness with OHCs on physicians' continuous knowledge-sharing behaviors and the contingent role of physicians' online seniority status. The research model and relevant hypotheses were examined using objective data from one of the leading OHCs in China. The empirical results reveal that both practical benefits and psychological rewards positively affect physicians' continuous knowledge-sharing behaviors. However, an unexpected finding is that perceived connectedness is negatively associated with physicians' continuous knowledge-sharing behaviors. In addition, physicians' online seniority status strengthens the relationship between practical benefits and continuous knowledge-sharing behaviors but weaken the role of psychological rewards and perceived connectedness on continuous knowledge-sharing behaviors. This study contributes to the understanding of the motivational mechanisms underlying physicians' continuous knowledge-sharing behaviors in OHCs and provides significant practical implications for practitioners of OHCs.

Conducting mixed methods research is critical for healthcare researchers to understand attitudes, behaviors, and experiences on health-related topics, such as vaccine acceptance. As the COVID-19 pandemic has made it difficult to employ traditional, face-to-face qualitative methodologies, this paper describes the use of a virtual platform to conduct person-centered research. To overcome these challenges and better understand the attitudes and behaviors of vaccine-eligible individuals in the United States, an online health community called the Virtual Engagement Research Community (VERC) was designed and implemented. Using the Health Belief Model as a framework, the VERC employed a mixed methods approach to elicit insights, which included discussion topics, rapid polls, and surveys. Throughout the initial enrollment period of April–October 2021, continuous improvement efforts were made to bolster recruitment and member engagement. This agile research strategy was successful in utilizing mixed methods to capture community sentiments regarding vaccines. While this community focused on vaccination, the methodology holds promise for other areas of health research such as obesity, HIV, mental health disorders, and diabetes. Plain English summary: The COVID-19 pandemic has made it difficult to conduct research in person, but qualitative and quantitative data remain critical for developing research design. To gather and understand vaccine perceptions, opinions, and behaviors in a real time manner, The Virtual Engagement Research Community (VERC) was launched. As of October 2021, the VERC had 72 members who had the opportunity to participate in research events such as surveys, polls, discussion topics, and moderated chats in a private, invitation-only virtual community. The online format offered researchers the opportunity to adapt the types and frequency of engagements, fine-tune the research questions based on community responses and current events, and adjust the analysis and reporting methodology as needed to increase participation and maximize the value of the VERC. The success of the VERC and the lessons learned support the use of online communities to enhance mixed methods research as a valuable strategy for other health researchers. [ABSTRACT FROM AUTHOR]

BackgroundThe increased uptake of contralateral prophylactic mastectomy (CPM) among breast cancer patients remains poorly understood. We hypothesized that the increased rate of CPM is represented in conversations on an online breast cancer community and may contribute to patients choosing this operation.MethodsWe downloaded 328,763 posts and their dates of creation from an online breast cancer community from August 1, 2000, to May 22, 2016. We then performed a keyword search to identify posts which mentioned breast cancer surgeries: contralateral prophylactic mastectomy (n = 7095), mastectomy (n = 10,889), and lumpectomy (n = 9694). We graphed the percentage of CPM-related, lumpectomy-related, and mastectomy-related conversations over time. We also graphed the frequency of posts which mentioned multiple operations over time. Finally, we performed a qualitative study to identify factors influencing the observed trends.ResultsSurgically related posts (e.g., mentioning at least one operation) made up a small percentage (n = 27,678; 8.4%) of all posts on this community. The percentage of surgically related posts mentioning CPM was found to increase over time, whereas the percentage of surgically related posts mentioning mastectomy decreased over time. Among posts that mentioned more than one operation, mastectomy and lumpectomy were the procedures most commonly mentioned together, followed by mastectomy and CPM. There was no change over time in the frequency of posts that mentioned more than one operation. Our qualitative review found that most posts mentioning a single operation were unrelated to surgical decision-making; rather the operation was mentioned only in the context of the patient's cancer history. Conversely, the most posts mentioning multiple operations centered around the patients' surgical decision-making process.ConclusionsCPM-related conversation is increasing on this online breast cancer community, whereas mastectomy-related conversation is decreasing. These results appear to be primarily informed by patients reporting the types of operations they have undergone, and thus appear to correspond to the known increased uptake of CPM.

Background: Online health communities have given rise to a new e-service known as online medical consultations (OMCs), enabling remote interactions between physicians and patients. To address challenges, such as patient information overload and uneven distribution of physician visits, online health communities should develop OMC-oriented recommenders., Objective: We aimed to comprehensively investigate what paradigms lead to the success of OMC-oriented recommendations., Methods: A literature search was conducted through e-databases, including PubMed, ACM Digital Library, Springer, and ScienceDirect, from January 2011 to December 2023. This review included all papers directly and indirectly related to the topic of health care-related recommendations for online services., Results: The search identified 611 articles, of which 26 (4.3%) met the inclusion criteria. Despite the growing academic interest in OMC recommendations, there remains a lack of consensus among researchers on the definition of e-service-oriented recommenders. The discussion highlighted 3 key factors influencing recommender success: features, algorithms, and metrics. It advocated for moving beyond traditional e-commerce-oriented recommenders to establish an innovative theoretical framework for e-service-oriented recommenders and addresses critical technical issues regarding 2-sided personalized recommendations., Conclusions: This review underscores the essence of e-services, particularly in knowledge- and labor-intensive domains such as OMCs, where patients seek interpretable recommendations due to their lack of domain knowledge and physicians must balance their energy levels to avoid overworking. Our study's findings shed light on the importance of customizing e-service-oriented personalized recommendations to meet the distinct expectations of 2-sided users considering their cognitive abilities, decision-making perspectives, and preferences. To achieve this, a paradigm shift is essential to develop unique attributes and explore distinct content tailored for both parties involved., (©Hongxun Jiang, Ziyue Mi, Wei Xu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.07.2024.)

This study examines the determinants that drive the behavior of sharing health information within online health communities. Leveraging the Theory of Planned Behavior, the Technology Acceptance Model, and the “Knowledge-Attitude-Practice” theory, a comprehensive model elucidating the key elements that sway the health information-sharing behavior among users of online health communities is designed. This model is validated through Structural Equation Modeling (SEM) and Fuzzy Set Qualitative Comparative Analysis (fsQCA). Findings derived from the SEM suggest that perceived ease of use, perceived usefulness, perceived trust, and perceived behavioral control exert a significant positive impact on attitudes towards health information sharing, the intention to share health information, and the actual health information-sharing behavior. The fsQCA unfolds two unique configuration path models that lead to the emergence of health information-sharing behavior: one predicated on perceived trust and sharing intention, and the other on perceived usefulness, behavioral control, and sharing attitude. This research provides invaluable insights, fostering a deeper comprehension of the dynamics involved in health information sharing within online communities, thereby directing the design of more effective health platforms to augment user engagement and enable informed health decisions.

The rising prevalence of diabetes and the increasing awareness of self-health management have resulted in a surge in diabetes patients seeking health information and emotional support in online health communities. Consequently, there is a vast database of patient consultation information in these online health communities. However, due to the heterogeneity and incompleteness of the content, mining medical information and patient health data from these communities can be a challenge. To address this issue, we built the RoBERTa-BiLSTM-CRF (RBC) model for identifying entities in the online health community of diabetes. We selected 1889 question–answer texts from the most active online health community in China, Good Doctor Online, and used these public data to identify five types of entities. In addition, we conducted a comparative evaluation with three other commonly used models to validate the performance of our proposed model, including RoBERTa-CRF (RC), BilSTM-CRF (BC), and RoBERTa-Softmax (RS). The results showed that the RBC model achieved excellent performance on the test set, with an accuracy of 81.2% and an F1 score of 80.7%, outperforming the performance of traditional entity recognition models in named entity recognition in online medical communities for doctors and diabetes patients. The high performance of entity recognition in online health communities will provide a crucial knowledge source for constructing medical knowledge graphs. This integration would help alleviate the growing demand for medical consultations and the strain on healthcare resources, while assisting healthcare professionals in making informed decisions and providing personalized services to patients.