The impact of Chatbot-Assisted Self Assessment (CASA) on intentions for sexual health screening in people from minoritised ethnic groups at risk of sexually transmitted infections.

Background Sexually transmitted infections (STIs) present a significant global public health issue, with disparities in STI rates often observed across ethnic groups. The study investigates the impact of Chatbot-Assisted Self Assessment (CASA) on the intentions for sexual health screening within minoritised ethnic groups (MEGs) at risk of STIs as well as the subsequent use of a chatbot for booking STI screening. Methods A simulation within-subject design was utilised to evaluate the effect of CASA on intentions for STI/HIV screening, concern about STIs, and attitudes towards STI screening. Screening intentions served as the dependent variable, while demographic and behavioural factors related to STI/HIV risk were the independent variables. ANCOVA tests were conducted to measure the impact of CASA on these perceptions. Results Involving 548 participants (54% women, 66% black, average age=30years), the study found that CASA positively influenced screening intentions t (547)=-10.3, P t (544)=-4.96, P t (543)=-4.36, P Conclusion CASA increased motivations for STI screening intentions among ethnically diverse communities. The intervention's non-judgemental nature and the chatbot's ability to emulate sexual history-taking were critical in fostering an environment conducive to behavioural intention change. The study's high acceptability indicates the potential for broader application in digital health interventions. However, the limitation of not tracking actual post-intervention behaviour warrants further investigation into CASA's real-world efficacy.

Individual differences in sharing false political information on social media: Deliberate and accidental sharing, motivations and positive schizotypy.

False political information-misinformation or disinformation-is widely spread on social media. Individual social media users play a large part in this. However, only a minority actively share false material. It is important to establish what sets these individuals apart from those who do not, and why they do it. Motivations for sharing may vary and are likely to differ between people who share false material unknowingly and on purpose. In this paper we consider the extent to which individual differences in personality and other variables, and motivations for sharing, are associated with the likelihood of people sharing false political information both accidentally and deliberately. In a series of four studies (Ns = 614, 563, 627, 113) we examined predictors of sharing false political information using different methodological approaches. Across the four studies, a key finding was that positive schizotypy is associated with measures of sharing false information both accidentally and deliberately. Motivations for sharing political information online were also relevant, with sharing for reasons of 'raising awareness' appearing particularly important. Implications for research and practice are discussed.

Achieving health equity through conversational AI: A roadmap for design and implementation of inclusive chatbots in healthcare.

BACKGROUND: The rapid evolution of conversational and generative artificial intelligence (AI) has led to the increased deployment of AI tools in healthcare settings. While these conversational AI tools promise efficiency and expanded access to healthcare services, there are growing concerns ethically, practically and in terms of inclusivity. This study aimed to identify activities which reduce bias in conversational AI and make their designs and implementation more equitable. METHODS: A qualitative research approach was employed to develop an analytical framework based on the content analysis of 17 guidelines about AI use in clinical settings. A stakeholder consultation was subsequently conducted with a total of 33 ethnically diverse community members, AI designers, industry experts and relevant health professionals to further develop a roadmap for equitable design and implementation of conversational AI in healthcare. Framework analysis was conducted on the interview data. RESULTS: A 10-stage roadmap was developed to outline activities relevant to equitable conversational AI design and implementation phases: 1) Conception and planning, 2) Diversity and collaboration, 3) Preliminary research, 4) Co-production, 5) Safety measures, 6) Preliminary testing, 7) Healthcare integration, 8) Service evaluation and auditing, 9) Maintenance, and 10) Termination. DISCUSSION: We have made specific recommendations to increase conversational AI's equity as part of healthcare services. These emphasise the importance of a collaborative approach and the involvement of patient groups in navigating the rapid evolution of conversational AI technologies. Further research must assess the impact of recommended activities on chatbots' fairness and their ability to reduce health inequalities.