Patient-centered or population-centered? How epistemic discrepancies cause harm and sow mistrust

Medical distrust is often conceived of as a problem of misinformation or ignorance. In this paper, I depart from this framework, attributing distrust instead to epistemic divergence between lay people and experts. Using data from a contraceptive side effects Facebook group and in-depth physician interviews, I find that providers employ a "body-as-subject" lens informed by population-health goals, while group members employ a "body-as-agent" lens that privileges individuality and bodily autonomy. Provider epistemologies are privileged, creating epistemic injustice and harm for patients. Ultimately, this erodes trust in providers and the medical community more broadly.

The quality of informational social support in online health communities: A content analysis of cancer-related discussions

Objective

Informational social support is one of the main reasons for patients to visit online health communities (OHCs). Calls have been made to investigate the objective quality of such support in the light of a worrying number of inaccurate online health-related information. The main aim of this study is to conceptualize the Quality of Informational Social Support (QISS) and develop and test a measure of QISS for content analysis. A further aim is to investigate the level of QISS in cancer-related messages in the largest OHC in Slovenia and examine the differences among various types of discussion forums, namely, online consultation forums, online support group forums, and socializing forums.

Methods

A multidimensional measurement instrument was developed, which included 20 items in a coding scheme for a content analysis of cancer-related messages. On a set of almost three million posts published between 2015 and 2019, a machine-learning algorithm was used to detect cancer-related discussions in the OHC. We then identified the messages providing informational social support, and through quantitative content analysis, three experts coded a random sample of 403 cancer-related messages for the QISS.

Results

The results demonstrate a good level of interrater reliability and agreement for a QISS scale with six dimensions, each demonstrating good internal consistency. The results reveal large differences among the social support, socializing, and consultation forums, with the latter recording significantly higher quality in terms of accuracy (M = 4.48, P < .001), trustworthiness (M = 4.65, P < .001), relevance (M = 3.59, P < .001), and justification (M = 3.81, P = .05) in messages providing informational social support regarding cancer-related issues.

Conclusions

This study provides the research field with a valid tool to further investigate the factors and consequences of varying quality of information exchanged in supportive communication. From a practical perspective, OHCs should dedicate more resources and develop mechanisms for the professional moderation of health-related topics in socializing forums and thereby suppress the publication and dissemination of low-quality information among OHC users and visitors.

Development and implementation of an online community as a strategy for mixed methods research during a pandemic

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.

Patients need emotional support: Managing physician disclosure information to attract more patients

BACKGROUND

Information asymmetry causes barriers for the patient's decision-making in the online health community. Patients can rely on the physician's self-disclosed information to alleviate it. However, the impact of physician's self-disclosed information on the patient's decision has rarely been discussed.

OBJECTIVES

To investigate the impact of the physician's self-disclosed information on the patient's decision in the online health community and to examine the moderating effect of the physician's online reputation.

METHODS

Drawing on the limited-capacity model of attention, we develop a theoretical model to estimate the impact of physician's self-disclosure information on patient's decision and the contingent roles of physician's online reputation in online healthcare community by econometric methods. We designed a web crawler based on R language program to collect more than 20,000 physicians' data from their homepage in Haodf-a leading online healthcare community platform in China. The attributes of the physician's information disclosure are measured by the following variables: emotion orientation, the quantity of information and the semantic topics diversity.

RESULTS

The empirical analysis derives the following findings: (1) The emotion orientation in physician's self-disclosure information is positively associated with patient's decision; (2) Both excessive quantity of information and semantic topics diversity can raise barriers for patient's decision; (3) When the level of physician's online reputation is high, the negative effect of the quantity of information and semantic topics diversity are all strengthened while the positive effect of the emotion orientation is not strengthened.

CONCLUSIONS

This study has a profound importance for a deep understanding of the impact of physician's self-disclosure information and contributes to the literature on information disclosure, the limited capacity model of attention, patient's decision. Also, this study provides implications for practice.

Investigating relationships among cancer survivors' engagement in an online support community, social support perceptions, well-being, and moderating effects of existing (offline) social support

PURPOSE

Socially supportive relationships help cancer survivors cope with their diagnosis and may improve quality of life; however, many survivors report unmet support and information needs. Online communities of survivors may address these needs, but research on their benefits have been equivocal. This cross-sectional, self-report study investigated relationships among cancer survivors' level of engagement in an online survivor community (The American Cancer Society Cancer Survivors Network®; CSN), perceptions of emotional/informational support available from online communities ("online social support"), well-being, and moderating effects of "offline social support."

METHODS

Participants were 1255 registered users of the CSN who completed surveys between 2013 and 2014. Three types of engagement with the CSN-social/communal, interpersonal communication, and informational/search engagement-were identified through principal components analysis. Regression analyses examined hypotheses.

RESULTS

More frequent social/communal and interpersonal communication engagement were associated with increased online social support (p < .0001), and the relationship between interpersonal communication engagement and online social support was strongest for survivors reporting lower offline social support (interaction β = - .35, p < .001). Greater online social support was associated with increased well-being, but only among survivors reporting low offline social support (interaction β = - .35, p < .0001).

CONCLUSIONS

Engagement in online survivor communities may increase support perceptions that promote well-being, but benefits may accrue more to survivors reporting low offline social support.

IMPLICATIONS FOR CANCER SURVIVORS

Newly diagnosed cancer survivors, particularly those with unmet emotional/informational support needs, should be given the opportunity to communicate with other survivors through online survivor support networks.

Understanding the factors influencing health professionals' online voluntary behaviors: Evidence from YiXinLi, a Chinese online health community for mental health

BACKGROUND

Normal users' voluntary behaviors (e.g., knowledge sharing) in virtual communities (VCs) has been well investigated; however, research on health professionals' voluntary behaviors in online health communities (OHCs) is limited.

OBJECTIVE

This paper focuses on OHCs for mental health and aims to explore how intrinsic and extrinsic motivations influence mental health service providers' voluntary behaviors.

METHODS

Based on motivation theory and prior studies, we incorporated technical competence as intrinsic motivation and online reputation and economic rewards as extrinsic motivations, and proposed five hypotheses. We crawled objective data from YiXinLi, a Chinese OHC for mental health, and tested the hypotheses based on the Poisson regression model. All hypotheses are supported.

RESULTS

1) Technical competence, online reputation, and economic rewards positively influence mental health service providers' voluntary behaviors; 2) the interaction effect between technical competence and online reputation negatively influences mental health service providers' voluntary behaviors; 3) the interaction effect between technical competence and economic rewards negatively influences mental health service providers' voluntary behaviors.

CONCLUSIONS

Both intrinsic motivations and extrinsic motivations positively influence mental health service providers' voluntary behaviors, and their interaction effects negatively influence mental health service providers' voluntary behaviors. This study first contributes to the literature on health professionals' voluntary behaviors in OHCs by verifying the positive effect of economic rewards. It then contributes to motivation theory by incorporating a situation where intrinsic motivations and extrinsic motivations could negatively interact.

Increase in contralateral prophylactic mastectomy conversation online unrelated to decision-making

BACKGROUND

The 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.

METHODS

We 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.

RESULTS

Surgically 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.

CONCLUSIONS

CPM-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.

Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks

Identifying topics of discussions in online health communities (OHC) is critical to various information extraction applications, but can be difficult because topics of OHC content are usually heterogeneous and domain-dependent. In this paper, we provide a multi-class schema, an annotated dataset, and supervised classifiers based on convolutional neural network (CNN) and other models for the task of classifying discussion topics. We apply the CNN classifier to the most popular breast cancer online community, and carry out cross-sectional and longitudinal analyses to show topic distributions and topic dynamics throughout members' participation. Our experimental results suggest that CNN outperforms other classifiers in the task of topic classification and identify several patterns and trajectories. For example, although members discuss mainly disease-related topics, their interest may change through time and vary with their disease severities.

Exploring the benefits and challenges of health professionals' participation in online health communities: Emergence of (dis)empowerment processes and outcomes

BACKGROUND

Various online applications and service has led to the development of online health communities (OHCs), which in addition to the peer-to-peer communication offer patients and other users also interaction with health professionals. While the benefits and challenges of patients and other users' participation in OHCs have been extensively studied, a thorough examination of how health professionals as moderators (i.e., those who provide clinical expertise to patients and other users in OHCs) experience participation in OHCs is lacking.

OBJECTIVE

The aim of this study is to explore the main benefits and challenges of health professional moderators' participation in the OHCs.

METHODS

The study undertakes an exploratory qualitative study, with in-depth semi-structured interviews with health professional moderators (n=7) participating in the largest OHC in Slovenia, Med.Over.Net. The data was analysed using inductive thematic analysis approach and principles of grounded theory.

RESULTS

Four themes of health professional moderators' experiences were identified: (a) benefits of addressing OHC users' health-related needs, (b) challenges of addressing OHC users' health-related needs, (c) health professional moderators' benefits, and (d) health professional moderators' challenges.

CONCLUSIONS

This small study demonstrates that health professional participating in OHCs as moderators perceive themselves as facilitators of patients and other OHC's users empowering processes and outcomes, in which OHC's users improve their health literacy, develop skills, expand their social support, and gain other important resources necessary when dealing with health-related issues. Health professional moderator's role, however, also involves several duties, responsibilities and limitations that are often experienced as difficulties in providing patients and other users with adequate counselling and online medical service. OHCs also represent an important terrain for personal and professional empowerment of health professional moderators, although the presence of disempowering processes also needs to be noted.

Personas in online health communities

Many researchers and practitioners use online health communities (OHCs) to influence health behavior and provide patients with social support. One of the biggest challenges in this approach, however, is the rate of attrition. OHCs face similar problems as other social media platforms where user migration happens unless tailored content and appropriate socialization is supported. To provide tailored support for each OHC user, we developed personas in OHCs illustrating users' needs and requirements in OHC use. To develop OHC personas, we first interviewed 16 OHC users and administrators to qualitatively understand varying user needs in OHC. Based on their responses, we developed an online survey to systematically investigate OHC personas. We received 184 survey responses from OHC users, which informed their values and their OHC use patterns. We performed open coding analysis with the interview data and cluster analysis with the survey data and consolidated the analyses of the two datasets. Four personas emerged-Caretakers, Opportunists, Scientists, and Adventurers. The results inform users' interaction behavior and attitude patterns with OHCs. We discuss implications for how these personas inform OHCs in delivering personalized informational and emotional support.

Automatic topic identification of health-related messages in online health community using text classification

To facilitate patient involvement in online health community and obtain informative support and emotional support they need, a topic identification approach was proposed in this paper for identifying automatically topics of the health-related messages in online health community, thus assisting patients in reaching the most relevant messages for their queries efficiently. Feature-based classification framework was presented for automatic topic identification in our study. We first collected the messages related to some predefined topics in a online health community. Then we combined three different types of features, n-gram-based features, domain-specific features and sentiment features to build four feature sets for health-related text representation. Finally, three different text classification techniques, C4.5, Naïve Bayes and SVM were adopted to evaluate our topic classification model. By comparing different feature sets and different classification techniques, we found that n-gram-based features, domain-specific features and sentiment features were all considered to be effective in distinguishing different types of health-related topics. In addition, feature reduction technique based on information gain was also effective to improve the topic classification performance. In terms of classification techniques, SVM outperformed C4.5 and Naïve Bayes significantly. The experimental results demonstrated that the proposed approach could identify the topics of online health-related messages efficiently.