Promoting users’ intention to share online health articles on social media: The role of confirmation bias

Zhao, Haiping; Fu, Shaoxiong; Chen, Xiaoyu

doi:10.1016/j.ipm.2020.102354

Cited by 58 publications

(44 citation statements)

References 52 publications

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“…Notably, the level of information literacy was found to affect users' engagement in health information seeking (Chang et al, 2015;Quinn et al, 2017). A study by Zhao et al (2020) confirmed these results. The study showed that users with high information literacy share positive health information, while users with a low level of information literacy tend to share information regardless of its authenticity.…”

Section: Health Information Needs In the Age Of Covid-19mentioning

confidence: 55%

Health Information behaviour during COVID-19 outbreak among Egyptian library and information science undergraduate students

Shehata

2020

Information Development

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Health information shared on the Internet has the potential to improve people’s quality of life. However, the level of health information literacy and informational skills determines the individuals’ ability to obtain credible health information useful for them. This study investigates the Egyptian LIS undergraduates’ health information behaviour during the COVID-19 pandemic. The study has adopted a quantitative approach using a questionnaire to explore library and information science undergraduates’ health information behaviour. A sample of 161 students answered a questionnaire. The data were analysed using SPSS software. The study results show that official health websites, social media, and family and friends are the primary sources for health information for the participants. The findings also revealed that the sample has adequate health information skills, though many of the participants still need to raise their information literacy skills. Information credibility and personal beliefs were found to affect the participants’ decision to share health information on the Internet. One of the limitations of the study is that the sample selected in this study was LIS students who are trained to find reliable information as part of their education. Findings may be different for the “average” student. Furthermore, the questionnaire was distributed in one university, which means that the results cannot be generalised in other LIS departments using different courses. Therefore, more studies need to be conducted in other LIS departments with different course plans as they may produce different results. This study explores the health information behaviour of LIS undergraduates during the COVID-19 pandemic. The current study results may help develop the course plan in many LIS departments in Egypt and the Middle East, which would help improve the students’ health information literacy.

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Section: Health Information Needs In the Age Of Covid-19mentioning

confidence: 55%

Health Information behaviour during COVID-19 outbreak among Egyptian library and information science undergraduate students

Shehata

2020

Information Development

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“…A contributing factor that might enhance the availability bias is the tendency of posting health articles on social media. Levels of confirmation bias in health professionals have been shown to influence which articles are shared on social media (Zhao, Fu & Chen, 2020).…”

Section: Empirical Data On the Prevalence Of Decision-making Biases In Medicinementioning

confidence: 99%

How Human Decision-making Biases Influence Health Outcomes in Patient Care

Sibbel

Huber

2021

EJMI

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Purpose: Medical treatments and medical decision making are mostly human based and therefore in risk of being influenced by cognitive biases. The potential impact could lead to bad medical outcome, unnecessary harm or even death. The aim of this comprehensive literature study is to analyse the evidence whether healthcare professionals are biased, which biases are most relevant in medicine and how these biases may be reduced. Approach/Findings: The results of the comprehensive literature based meta-analysis confirm on the one hand that several biases are relevant in the medical decision and treatment process. On the other hand, the study shows that the empirical evidence on the impact of cognitive biases on clinical outcome is scarce for most biases and that further research is necessary in this field. Value/Practical Implications: Nevertheless, it is important to determine the extent to which biases in healthcare professionals translate into negative clinical outcomes such as misdiagnosis, delayed diagnosis, or mistreatment. Only this way, the importance of incorporating debiasing strategies into the clinical setting, and which biases to focus on, can be properly assessed. Research Limitations/Future Research: Though recent literature puts great emphasis on cognitive debiasing strategies, there are still very few approaches that have proven to be efficient. Due to the increasing degree of specialization in medicine, the relevance of the different biases varies. Paper type: Theoretical.

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“…However, the low-health literacy assumption of patients should be re-considered in the online situation, driven by three unique characteristics of E-health: (1) Active online engagement: Patients with high health literacy tend to actively engage in online health-related activities, such as health information seeking and use [ 62 ]. (2) Learning and sharing: Patients accumulate more knowledge from online activities.…”

Section: Hypothesis Developmentmentioning

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

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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.

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Promoting users’ intention to share online health articles on social media: The role of confirmation bias

Cited by 58 publications

References 52 publications

Health Information behaviour during COVID-19 outbreak among Egyptian library and information science undergraduate students

Health Information behaviour during COVID-19 outbreak among Egyptian library and information science undergraduate students

How Human Decision-making Biases Influence Health Outcomes in Patient Care

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

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