Chatbots for embarrassing and stigmatizing conditions:could chatbots encourage users to seek medical advice?

Background: Chatbots are increasingly being used across a wide range of contexts. Medical chatbots have the potential to improve healthcare capacity and provide timely patient access to health information. Chatbots may also be useful for encouraging individuals to seek an initial consultation for embarrassing or stigmatizing conditions.Method: This experimental study used a series of vignettes to test the impact of different scenarios (experiencing embarrassing vs. stigmatizing conditions, and sexual vs. non-sexual symptoms) on consultation preferences (chatbot vs. doctor), attitudes toward consultation methods, and expected speed of seeking medical advice.Results: The findings show that the majority of participants preferred doctors over chatbots for consultations across all conditions and symptom types. However, more participants preferred chatbots when addressing embarrassing sexual symptoms, compared with other symptom categories. Consulting with a doctor was believed to be more accurate, reassuring, trustworthy, useful and confidential than consulting with a medical chatbot, but also more embarrassing and stressful. Consulting with a medical chatbot was believed to be easier and more convenient, but also more frustrating. Interestingly, people with an overall preference for chatbots believed this method would encourage them to seek medical advice earlier than those who would prefer to consult with a doctor.Conclusions: The findings highlight the potential role of chatbots in addressing embarrassing sexual symptoms. Incorporating chatbots into healthcare systems could provide a faster, more accessible and convenient route to health information and early diagnosis, as individuals may use them to seek earlier consultations

Digitalization and Democracy: Thoughts to the discussion on E-Voting

The general mantra is that digitisation is necessary to make companies and public administration more efficient. In both cases, more efficient ideally means better, faster, and more convenient; what it also usually implies is cheaper. In this paper we focus on the public side of the issue, namely on the topic of democracy. We also consideraspects of the polling system. For instance, e-voting should help increase poll participation and promote inclusion, however, it also has its pitfalls

Promoting Cybersecurity Culture Change in Healthcare

Cybersecurity problems have traditionally been addressed through technological solutions and staff training. Whilst technology can reduce or remove some weaknesses some attacks specifically target human users. Whilst training can educate staff on how to behave more securely, this is often not sufficient to promote actual secure behaviours . Knowing what to do is necessary but not sufficient. It is also necessary to remove barriers to the required behaviour and to intervene in a way that affords behaviour change. This is particularly true in healthcare, where environmental factors including time pressure, and staff fatigue can create barriers for cybersecurity behaviour change. Technology and training are only a partial solution. Only by taking a more holistic approach which encompasses technology, people and processes and addressing the culture change needed to facilitate more secure behaviours will any progress be made in the workplace. We conducted a series of in-depth interviews and workshops with staff across 3 healthcare organisations in Italy, Crete and Ireland. This paper reflects on our main findings, including key requirements for future cybersecurity interventions. We used this reflection to develop a secure behaviour toolkit to help healthcare organisations identify problematic behaviours, co-create interventions to increase secure staff behaviour being mindful that sometimes culture change is necessary to enable the required security behaviours. The toolkit also provides a means to evaluate the interventions identified and the final implementation of the intervention

Health stigma on Twitter:investigating the prevalence and type of stigma communication in tweets about different conditions and disorders

Background: Health-related stigma can act as a barrier to seeking treatment and can negatively impact wellbeing. Comparing stigma communication across different conditions may generate insights previously lacking from condition-specific approaches and help to broaden our understanding of health stigma as a whole.Method: A sequential explanatory mixed-methods approach was used to investigate the prevalence and type of health-related stigma on Twitter by extracting 1.8 million tweets referring to five potentially stigmatized health conditions and disorders (PSHCDs): Human Immunodeficiency Virus (HIV)/Acquired Immunodeficiency Syndrome (AIDS), Diabetes, Eating Disorders, Alcoholism, and Substance Use Disorders (SUD). Firstly, 1,500 tweets were manually coded by stigma communication type, followed by a larger sentiment analysis (n = 250,000). Finally, the most prevalent category of tweets, “Anti-Stigma and Advice” (n = 273), was thematically analyzed to contextualize and explain its prevalence.Results: We found differences in stigma communication between PSHCDs. Tweets referring to substance use disorders were frequently accompanied by messages of societal peril. Whereas, HIV/AIDS related tweets were most associated with potential labels of stigma communication. We found consistencies between automatic tools for sentiment analysis and manual coding of stigma communication. Finally, the themes identified by our thematic analysis of anti-stigma and advice were Social Understanding, Need for Change, Encouragement and Support, and Information and Advice.Conclusions: Despite one third of health-related tweets being manually coded as potentially stigmatizing, the notable presence of anti-stigma suggests that efforts are being made by users to counter online health stigma. The negative sentiment and societal peril associated with substance use disorders reflects recent suggestions that, though attitudes have improved toward physical diseases in recent years, stigma around addiction has seen little decline. Finally, consistencies between our manual coding and automatic tools for identifying language features of harmful content, suggest that machine learning approaches may be a reasonable next step for identifying general health-related stigma online

Your hospital needs you: Eliciting positive cybersecurity behaviours from healthcare staff

Staff behaviour plays a key role in the cybersecurity position of an organisation. Despite this, behaviour-change interventions are not commonly applied within the field of cybersecurity. Behaviour change technique could be particularly beneficial given increasing concerns around healthcare cybersecurity risks; particularly following the 2017 WannaCry ransomware attack which had devastating results on healthcare services. Cyber-risk is particularly concerning within healthcare given the criticality of medical systems and the potential impacts of a cyberbreach or attack. In worst case scenarios, cybersecurity incidents could result in patient harm or even fatalities. Whilst there has been concerted investment in improving healthcare’s technological defences against cyberthreat, the same level of investment has not been made in healthcare staff. This has left staff behaviour as a vulnerability which can be exploited by attackers. This paper introduces a structured approach to help organisations work through four key steps that we refer to as the AIDE approach to Assess, Identify, Develop and Evaluate&nbsp;behaviour change techniques to facilitate more secure behaviour. We include a worked example of how we are applying this approach to the development of interventions to mitigate insecure cybersecurity behaviours in a healthcare context. </p

Developing and Validating a Behavioural Model of Cyberinsurance Adoption

Business disruption from cyberattacks is a growing concern, yet cyberinsurance uptake remains low. Using an online behavioural economics experiment with 4800 participants across four EU countries, this study tests a predictive model of cyberinsurance adoption, incorporating elements of Protection Motivation Theory (PMT) and the Theory of Planned Behaviour (TPB) as well as factors in relation to risk propensity and price. During the experiment, participants were given the opportunity to purchase different cybersecurity measures and cyberinsurance products before performing an online task. Participants likelihood of suffering a cyberattack was dependent upon their adoption of cybersecurity measures and their behaviour during the online task. The consequences of any attack were dependent upon the participants insurance decisions. Structural equation modelling was applied and the model was further developed to include elements of the wider security ecosystem. The final model shows that all TPB factors, and response efficacy from the PMT, positively predicted adoption of premium cyberinsurance. Interestingly, adoption of cybersecurity measures was associated with safer behaviour online, contrary to concerns of “moral hazard”. The findings highlight the need to consider the larger cybersecurity ecosystem when designing interventions to increase adoption of cyberinsurance and/or promote more secure online behaviour

Your hospital needs you: Eliciting positive cybersecurity behaviours from healthcare staff using the AIDE approach

Staff behaviour plays a key role in the cybersecurity position of an organisation. Despite this, behaviour-change interventions are not commonly applied within the field of cybersecurity. Behaviour change technique could be particularly beneficial given increasing concerns around healthcare cybersecurity risks; particularly following the 2017 WannaCry ransomware attack which had devastating results on healthcare services. Cyber-risk is particularly concerning within healthcare given the criticality of medical systems and the potential impacts of a cyberbreach or attack. In worst case scenarios, cybersecurity incidents could result in patient harm or even fatalities. Whilst there has been concerted investment in improving healthcare’s technological defences against cyberthreat, the same level of investment has not been made in healthcare staff. This has left staff behaviour as a vulnerability which can be exploited by attackers. This paper introduces a structured approach to help organisations work through four key steps that we refer to as the AIDE approach to Assess, Identify, Develop and Evaluate behaviour change techniques to facilitate more secure behaviour. We include a worked example of how we are applying this approach to the development of interventions to mitigate insecure cybersecurity behaviours in a healthcare context

Distinguishing suicide ideation from suicide attempts: Further test of the Integrated Motivational-Volitional Model of Suicidal Behaviour

Suicidal behaviour poses a significant public health concern. Research into the factors that distinguish between the emergence of suicide ideation and the enactment of a suicide attempt is crucial. This study tests central tenets of the Integrated Motivational-Volitional Model of suicidal behaviour (IMV, O’Connor and Kirtley, 2018) which posits that volitional phase factors govern the transition from thinking to attempting suicide. 299 adults completed a face-to-face interview and were allocated to groups based on their suicidal history: Suicide attempt group (N = 100), suicide ideation group (N = 105), and a control group (N = 94). Measures were taken at baseline, at 1-month and 6-months follow-up. As predicted, the attempt group differed from the ideation group on all volitional phase factors. Those who had attempted suicide reported higher capability for suicide, were more likely to have a family member or friend who had self-injured or attempted suicide, and were more impulsive. In keeping with the IMV model, the ideation and attempt groups had similar scores on the motivational factors. Defeat and entrapment were significant predictors of ideation at baseline, and mediation analyses indicated that defeat had an indirect effect on ideation through entrapment at baseline and at 1-month follow-up. The results support the IMV model and suggest that entrapment should be routinely included in suicide risk assessments. Further research to test predictors of the transition from suicide ideation to suicide attempts is crucial to inform future intervention development and health care delivery