Attraction, selection, and attrition in online health communities: Initial conversations and their association with subsequent activity levels

Abedin, Babak; Milne, David; Erfani, Eila

doi:10.1016/j.ijmedinf.2020.104216

Cited by 6 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2) Learning and sharing: Patients accumulate more knowledge from online activities. For example, active participation in online healthcare community helps members enhance their health literacy [ 36 ], since they could read a lot of popular medical contents written by doctors or experiences shared by other patients [ 1 , 29 , 32 , 56 , 60 ], which offers a good opportunity to get familiar with and understand related medical terms [ 10 , 45 ]. (3) Asynchronous communication: E-health communication can span a long period of time.…”

Section: Hypothesis Developmentmentioning

confidence: 99%

“…The online context of E-health provides patients with more access to medical information and knowledge. Patients are able to learn from the experience of other patients [ 1 , 29 , 56 ] and free knowledge from doctors [ 32 , 60 ], which could improve the health literacy of some patients [ 45 ]. In addition, there exist numerous related documents on the Internet, in either a professional style or a popular style.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Should doctors use or avoid medical terms? The influence of medical terms on service quality of E-health

Zhang

Wang

Yan

2021

Electron Commer Res

View full text Add to dashboard Cite

With epidemics and pandemics like COVID-19, many offline healthcare services have been suspended and shifted to online, where patients and doctors typically communicate by typing texts. The limited communication poses a threat to the service quality of E-health, and also raises higher demand on the language skills of doctors, in which medical terms are a common concern. Traditional studies of offline healthcare mostly hold a negative attitude towards the use of medical terms by doctors. However, should we still advise doctors to avoid using medical terms in E-health? To answer this question, this paper conducts a study combining technical and empirical analyses based on real data. In this paper, a novel unsupervised text-mining method is proposed to automatically identify medical terms with crowd wisdom from large-scale doctor-patient communication texts. Then, a TREC-type experiment is carried out to validate the proposed method in terms of Precision, Recall, and -measure, demonstrating that it can identify accurate and comprehensive medical terms. Based on the identified medical terms, an empirical analysis is conducted to verify the influence of medical terms used by doctors on the service quality of E-health. The analysis results show that for patients with low health literacy, the use of medical terms by doctors would decrease their service quality. However, for patients with high health literacy, the use of medical terms by doctors can significantly increase their service quality, revealing that doctors could improve their service quality in E-health by adjusting their medical term usage according to the health literacy of patients.

show abstract

Section: Hypothesis Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Should doctors use or avoid medical terms? The influence of medical terms on service quality of E-health

Zhang

Wang

Yan

2021

Electron Commer Res

View full text Add to dashboard Cite

show abstract

“…Characterized by user-generated content (UCG), OHCs requires deep participation of community members to accumulate mass data of health and medical care ( Abedin et al, 2020 ; Lu and Zhang, 2021 ; Zhou, 2021 ). However, a majority of users remain an inactive status, who mainly acquire health knowledge and information from the community, lacking the motive and intention to share ( Introne and Goggins, 2019 ; Liu S. et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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

Tian

Wu²

2022

Front. Psychol.

View full text Add to dashboard Cite

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.

show abstract

“…Artificial intelligence (AI) offers enormously rewarding opportunities, along with new challenges that need to be identified and handled successfully to utilize AI's advantages and minimize its downsides (Rai, 2020; Abedin et al , 2020; Dwivedi et al , 2019; Beydoun et al , 2019). Humans usually lack understanding about how AI systems produce online behavior analytics or display particular behaviors.…”

Section: Introductionmentioning

confidence: 99%

“…Historically, explanations of AI emerged first in the context of rule-based expert systems and were viewed as elements of designing a system capable of producing drill-down outputs (Biran and Cotton, 2017; Gregor and Benbasat, 1999). The rise of data analytics and machine learning in various fields (Beydoun et al , 2019; Abedin et al , 2020) has increased the need for universal methods and practices for examining and verifying the structure and intent of AI systems. In the absence of cohesive theories or principles for AI explainability or interpretability (Arrieta et al , 2020), research to date has predominantly blended approaches drawn from various disciplines.…”

Section: Introductionmentioning

confidence: 99%

Managing the tension between opposing effects of explainability of artificial intelligence: a contingency theory perspective

Abedin

2021

INTR

Self Cite

View full text Add to dashboard Cite

PurposeResearch into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote the benefits of explainability or criticize it due to its counterproductive effects. This study addresses this polarized space and aims to identify opposing effects of the explainability of AI and the tensions between them and propose how to manage this tension to optimize AI system performance and trustworthiness.Design/methodology/approachThe author systematically reviews the literature and synthesizes it using a contingency theory lens to develop a framework for managing the opposing effects of AI explainability.FindingsThe author finds five opposing effects of explainability: comprehensibility, conduct, confidentiality, completeness and confidence in AI (5Cs). The author also proposes six perspectives on managing the tensions between the 5Cs: pragmatism in explanation, contextualization of the explanation, cohabitation of human agency and AI agency, metrics and standardization, regulatory and ethical principles, and other emerging solutions (i.e. AI enveloping, blockchain and AI fuzzy systems).Research limitations/implicationsAs in other systematic literature review studies, the results are limited by the content of the selected papers.Practical implicationsThe findings show how AI owners and developers can manage tensions between profitability, prediction accuracy and system performance via visibility, accountability and maintaining the “social goodness” of AI. The results guide practitioners in developing metrics and standards for AI explainability, with the context of AI operation as the focus.Originality/valueThis study addresses polarized beliefs amongst scholars and practitioners about the benefits of AI explainability versus its counterproductive effects. It poses that there is no single best way to maximize AI explainability. Instead, the co-existence of enabling and constraining effects must be managed.

show abstract

Attraction, selection, and attrition in online health communities: Initial conversations and their association with subsequent activity levels

Cited by 6 publications

References 29 publications

Should doctors use or avoid medical terms? The influence of medical terms on service quality of E-health

Should doctors use or avoid medical terms? The influence of medical terms on service quality of E-health

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

Managing the tension between opposing effects of explainability of artificial intelligence: a contingency theory perspective

Contact Info

Product

Resources

About