Predictors of Health Care Practitioners' Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology.

BACKGROUND: Artificial intelligence-enabled clinical decision support systems (AI-CDSSs) offer potential for improving health care outcomes, but their adoption among health care practitioners remains limited. OBJECTIVE: This meta-analysis identified predictors influencing health care practitioners' intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Additional predictors were examined based on existing empirical evidence. METHODS: The literature search using electronic databases, forward searches, conference programs, and personal correspondence yielded 7731 results, of which 17 (0.22%) studies met the inclusion criteria. Random-effects meta-analysis, relative weight analyses, and meta-analytic moderation and mediation analyses were used to examine the relationships between relevant predictor variables and the intention to use AI-CDSSs. RESULTS: The meta-analysis results supported the application of the UTAUT to the context of the intention to use AI-CDSSs. The results showed that performance expectancy (r=0.66), effort expectancy (r=0.55), social influence (r=0.66), and facilitating conditions (r=0.66) were positively associated with the intention to use AI-CDSSs, in line with the predictions of the UTAUT. The meta-analysis further identified positive attitude (r=0.63), trust (r=0.73), anxiety (r=-0.41), perceived risk (r=-0.21), and innovativeness (r=0.54) as additional relevant predictors. Trust emerged as the most influential predictor overall. The results of the moderation analyses show that the relationship between social influence and use intention becomes weaker with increasing age. In addition, the relationship between effort expectancy and use intention was stronger for diagnostic AI-CDSSs than for devices that combined diagnostic and treatment recommendations. Finally, the relationship between facilitating conditions and use intention was mediated through performance and effort expectancy. CONCLUSIONS: This meta-analysis contributes to the understanding of the predictors of intention to use AI-CDSSs based on an extended UTAUT model. More research is needed to substantiate the identified relationships and explain the observed variations in effect sizes by identifying relevant moderating factors. The research findings bear important implications for the design and implementation of training programs for health care practitioners to ease the adoption of AI-CDSSs into their practice.

The psychological burden of COVID-19 on the desire for parenthood in minoritized sexual identities: a study on depressive symptoms and family planning in Germany.

BACKGROUND: The COVID-19 pandemic continues to spread across the globe and is associated with significant clinical and humanitarian burden. The desire for parenthood has been described to be positively correlated with psychological well-being: An unfulfilled wish for parenthood is associated with impaired mental health, and the wish for parenthood is a predictor for the development of depressive symptoms. While higher rates of anxiety and depression have been reported in individuals with minoritized sexual identities (compared to heterosexual individuals) during the COVID-19 pandemic, the specific impact of the pandemic and its social restriction measures on this population is poorly understood. METHODS: From April to July 2020, we conducted an anonymous cross-sectional survey online among N = 2463 adults living in Germany. We screened for depressive symptoms (Patient Health Questionnaire-4; PHQ-4) and assessed individuals' desire for parenthood during the pandemic, and motives for or against the desire for parenthood (Leipzig questionnaire on motives for having a child, Version 20; LKM-20), with the aim of identifying differences between individuals with minoritized sexual identities and heterosexual individuals. RESULTS: Compared to heterosexual individuals (n = 1304), individuals with minoritized sexual identities (n = 831) indicated higher levels of depressive symptoms. In our study sample the majority of all participants (81.9%) reported no change in the desire for parenthood since the COVID-19 pandemic. CONCLUSION: The findings underline the unmet need for social, psychological and medical support in regard to family-planning and the desire for parenthood during a pandemic. Furthermore, future research should explore COVID-19-related psychological consequences on individuals' desire for parenthood and building a family.

The Impact of the COVID-19 Pandemic on Sexual Health in Cis Women Living in Germany.

BACKGROUND: Preliminary research shows a substantial impact of the COVID-19 pandemic on women's sexual health, whereby empirical work on sexual well-being of minoritized sexual identities is still rare. AIM: The objective of this study was to explore sexual health in heterosexual, lesbian and bisexual cis women during the first wave of COVID-19 pandemic in Germany. METHODS: An anonymous nationwide online survey was conducted among cis women during the first nationwide lockdown in Germany from April 20th to July 20th, 2020. The questionnaire was distributed via e-mail, online chats and social-media platforms. OUTCOMES: Demographic variables and self-report measures from the Sexual Behavior Questionnaire (SBQ-G) "before the pandemic" and "since the pandemic" were collected. RESULTS: A total of 1,368 cis women participants were included: heterosexual women (n = 844), lesbian women (n = 293), bisexual women (n = 231). Results indicate overall decrease in frequency of sexual contacts and masturbation during the COVID-19 pandemic. Regarding differences before and during the pandemic lesbian women showed significant changes in sexual arousal whereas heterosexual women showed significant changes in all dimensions except capability to enjoy sexual intercourse. The data of bisexual women showed significant changes in almost all dimensions except for frequency of sexual intercourse and sexual arousal. Results of the multiple regression analysis revealed that being younger than 36 years-old, and being in a relationship as well as being heterosexual (compared with being lesbian) is positively associated with general satisfaction with sexual life during the pandemic. CLINICAL IMPLICATIONS: The findings suggest that during a pandemic sexual and mental health care for (cis) women should be provided and address the specific needs of sexual minority groups. STRENGTHS & LIMITATIONS: This is the first study to describe sexual behavior in heterosexual, lesbian and bisexual women during the COVID-19 pandemic in Germany. Limitations, however, include the fact that the data described were obtained at only one time point so there is a possibility of recall bias, and that the results cannot be generalized because of the underrepresentation of women over age 46. CONCLUSION: This study examined the impact of the COVID-19 pandemic and resulting social constraints on the sexual health of particular groups of lesbian and bisexual women, which may improve preparedness for future public health and policy crises. Batz F, Lermer E, Hatzler L, et al. The Impact of the COVID-19 Pandemic on Sexual Health in Cis Women Living in Germany. J Sex Med 2022;19:907-922.

Thinking Concretely Increases the Perceived Likelihood of Risks: The Effect of Construal Level on Risk Estimation.

Recent findings on construal level theory (CLT) suggest that abstract thinking leads to a lower estimated probability of an event occurring compared to concrete thinking. We applied this idea to the risk context and explored the influence of construal level (CL) on the overestimation of small and underestimation of large probabilities for risk estimates concerning a vague target person (Study 1 and Study 3) and personal risk estimates (Study 2). We were specifically interested in whether the often-found overestimation of small probabilities could be reduced with abstract thinking, and the often-found underestimation of large probabilities was reduced with concrete thinking. The results showed that CL influenced risk estimates. In particular, a concrete mindset led to higher risk estimates compared to an abstract mindset for several adverse events, including events with small and large probabilities. This suggests that CL manipulation can indeed be used for improving the accuracy of lay people's estimates of small and large probabilities. Moreover, the results suggest that professional risk managers' risk estimates of common events (thus with a relatively high probability) could be improved by adopting a concrete mindset. However, the abstract manipulation did not lead managers to estimate extremely unlikely events more accurately. Potential reasons for different CL manipulation effects on risk estimates' accuracy between lay people and risk managers are discussed.

Construal Level and Social Exclusion: Concrete Thinking Impedes Recovery From Social Exclusion.

Social exclusion is a painful experience. Recent research has shown, however, that coping with exclusion can be facilitated by favorable conditions. In the current research, we investigated whether construal level affects recovery from social exclusion. We hypothesized that an abstract vs. concrete mindset would moderate coping with exclusion. Indeed, lower compared to higher concrete thinking (Study 1) and abstract compared to concrete thinking (Study 2) bolstered the basic need of belonging when excluded. Priming of abstract thinking, moreover, increased participants' sense of belonging both in response to exclusion and inclusion relative to no priming (Study 3). Our results are the first to establish a relationship between construal level and social exclusion, thereby suggesting an alleviating "abstraction discount" effect for the consequences of social exclusion.

Explainability does not mitigate the negative impact of incorrect AI advice in a personnel selection task.

Despite the rise of decision support systems enabled by artificial intelligence (AI) in personnel selection, their impact on decision-making processes is largely unknown. Consequently, we conducted five experiments (N = 1403 students and Human Resource Management (HRM) employees) investigating how people interact with AI-generated advice in a personnel selection task. In all pre-registered experiments, we presented correct and incorrect advice. In Experiments 1a and 1b, we manipulated the source of the advice (human vs. AI). In Experiments 2a, 2b, and 2c, we further manipulated the type of explainability of AI advice (2a and 2b: heatmaps and 2c: charts). We hypothesized that accurate and explainable advice improves decision-making. The independent variables were regressed on task performance, perceived advice quality and confidence ratings. The results consistently showed that incorrect advice negatively impacted performance, as people failed to dismiss it (i.e., overreliance). Additionally, we found that the effects of source and explainability of advice on the dependent variables were limited. The lack of reduction in participants' overreliance on inaccurate advice when the systems' predictions were made more explainable highlights the complexity of human-AI interaction and the need for regulation and quality standards in HRM.

Comparing preferences for skin cancer screening: AI-enabled app vs dermatologist.

BACKGROUND AND AIM: Skin cancer is a major public health issue. While self-examinations and professional screenings are recommended, they are rarely performed. Mobile health (mHealth) apps utilising artificial intelligence (AI) for skin cancer screening offer a potential solution to aid self-examinations; however, their uptake is low. Therefore, the aim of this research was to examine provider and user characteristics influencing people's decisions to seek skin cancer screening performed by a mHealth app or a dermatologist. METHODS: Two forced-choice conjoint experiments with Nmain = 1591 and Nreplication = 308 participants from the United States were conducted online to investigate preferences for screening providers. In addition to the provider type (mHealth app vs dermatologist), the following provider attributes were manipulated: costs, expertise, privacy policy, and result details. Subsequently, a questionnaire assessed various user characteristics, including demographics, attitudes toward AI technology and medical mistrust. RESULTS: Outcomes were consistent across the two studies. The provider type was the most influential factor, with the dermatologist being selected more often than the mHealth app. Cost, expertise, and privacy policy also significantly impacted decisions. Demographic subgroup analyses showed rather consistent preference trends across various age, gender, and ethnicity groups. Individuals with greater medical mistrust were more inclined to choose the mHealth app. Trust, accuracy, and quality ratings were higher for the dermatologist, whether selected or not. CONCLUSION: Our results offer valuable insights for technology developers, healthcare providers, and policymakers, contributing to unlocking the potential of skin cancer screening apps in bridging healthcare gaps in underserved communities.

Troll story: The dark tetrad and online trolling revisited with a glance at humor.

Internet trolling is considered a negative form of online interaction that can have detrimental effects on people's well-being. This pre-registered, experimental study had three aims: first, to replicate the association between internet users' online trolling behavior and the Dark Tetrad of personality (Machiavellianism, narcissism, psychopathy, and sadism) established in prior research; second, to investigate the effect of experiencing social exclusion on people's motivation to engage in trolling behavior; and third, to explore the link between humor styles and trolling behavior. In this online study, participants were initially assessed on their personality, humor styles, and global trolling behavior. Next, respondents were randomly assigned to a social inclusion or exclusion condition. Thereafter, we measured participants' immediate trolling motivation. Results drawn from 1,026 German-speaking participants indicate a clear correlation between global trolling and all facets of the Dark Tetrad as well as with aggressive and self-defeating humor styles. However, no significant relationship between experiencing exclusion/inclusion and trolling motivation emerged. Our quantile regression findings suggest that psychopathy and sadism scores have a significant positive effect on immediate trolling motivation after the experimental manipulation, whereas Machiavellianism and narcissism did not explain variation in trolling motivation. Moreover, being socially excluded had generally no effect on immediate trolling motivation, apart from participants with higher immediate trolling motivation, for whom the experience of social exclusion actually reduced trolling motivation. We show that not all facets of the Dark Tetrad are of equal importance for predicting immediate trolling motivation and that research should perhaps focus more on psychopathy and sadism. Moreover, our results emphasize the relevance of quantile regression in personality research and suggest that even psychopathy and sadism may not be suitable predictors for low levels of trolling behavior.

Attitudes Toward the Adoption of 2 Artificial Intelligence-Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study.

BACKGROUND: Despite growing efforts to develop user-friendly artificial intelligence (AI) applications for clinical care, their adoption remains limited because of the barriers at individual, organizational, and system levels. There is limited research on the intention to use AI systems in mental health care. OBJECTIVE: This study aimed to address this gap by examining the predictors of psychology students' and early practitioners' intention to use 2 specific AI-enabled mental health tools based on the Unified Theory of Acceptance and Use of Technology. METHODS: This cross-sectional study included 206 psychology students and psychotherapists in training to examine the predictors of their intention to use 2 AI-enabled mental health care tools. The first tool provides feedback to the psychotherapist on their adherence to motivational interviewing techniques. The second tool uses patient voice samples to derive mood scores that the therapists may use for treatment decisions. Participants were presented with graphic depictions of the tools' functioning mechanisms before measuring the variables of the extended Unified Theory of Acceptance and Use of Technology. In total, 2 structural equation models (1 for each tool) were specified, which included direct and mediated paths for predicting tool use intentions. RESULTS: Perceived usefulness and social influence had a positive effect on the intention to use the feedback tool (P<.001) and the treatment recommendation tool (perceived usefulness, P=.01 and social influence, P<.001). However, trust was unrelated to use intentions for both the tools. Moreover, perceived ease of use was unrelated (feedback tool) and even negatively related (treatment recommendation tool) to use intentions when considering all predictors (P=.004). In addition, a positive relationship between cognitive technology readiness (P=.02) and the intention to use the feedback tool and a negative relationship between AI anxiety and the intention to use the feedback tool (P=.001) and the treatment recommendation tool (P<.001) were observed. CONCLUSIONS: The results shed light on the general and tool-dependent drivers of AI technology adoption in mental health care. Future research may explore the technological and user group characteristics that influence the adoption of AI-enabled tools in mental health care.

Surfing in the streets: How problematic smartphone use, fear of missing out, and antisocial personality traits are linked to driving behavior.

Smartphone use while driving (SUWD) is a major cause of accidents and fatal crashes. This serious problem is still too little understood to be solved. Therefore, the current research aimed to contribute to a better understanding of SUWD by examining factors that have received little or no attention in this context: problematic smartphone use (PSU), fear of missing out (FOMO), and Dark Triad. In the first step, we conducted a systematic literature review to map the current state of research on these factors. In the second step, we conducted a cross-sectional study and collected data from 989 German car drivers. A clear majority (61%) admitted to using the smartphone while driving at least occasionally. Further, the results showed that FOMO is positively linked to PSU and that both are positively associated with SUWD. Additionally, we found that Dark Triad traits are relevant predictors of SUWD and other problematic driving behaviors--in particular, psychopathy is associated with committed traffic offenses. Thus, results indicate that PSU, FOMO, and Dark Triad are relevant factors to explain SUWD. We hope to contribute to a more comprehensive understanding of this dangerous phenomenon with these findings.

Where a psychopathic personality matters at work: a cross-industry study of the relation of dark triad and psychological capital.

BACKGROUND: The concepts of Dark Triad and Psychological Capital (PsyCap) have been extensively researched separately, but until one recent study, their interrelation has not been investigated. Purpose of this study was to uncover differences of the relationship of both concepts across work related industries. METHODS: In total, 2,109 German employees across 11 industries completed a questionnaire on Dark Triad (narcissism, psychopathy and Machiavellianism) and PsyCap. Multiple regression analyses were used to test the association of both concepts across industries. RESULTS: Values of narcissism, psychopathy and PsyCap generally differed between industries. No significant differences were found for Machiavellianism. While narcissism relates positively to PsyCap in all industry sectors, psychopathy only showed a negative relation to PsyCap in some sectors. For industries architecture, automotive and consulting, psychopathy did not significantly predict PsyCap. CONCLUSIONS: We argue that different expectations of employees per industry make it easier or harder for different personalities to assimilate (homogeneity hypothesis) to the work context (measured by PsyCap). Future studies should investigate this further with other variables such as person-organization-fit. This study was, however, the first to simultaneously investigate Dark Triad and PsyCap among employees and their respective industry. It extends previous findings by revealing differences of both concepts across and within industry sectors. The study can help to reconsider in which industries Dark Triad personality affects PsyCap as antecedent of workplace outcomes such as work satisfaction or job performance.

Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays.

Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians' decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, rate the advice's quality, and judge their own confidence. We manipulated whether the advice came with or without a visual annotation on the X-rays, and whether it was labeled as coming from an AI or a human radiologist. Overall, receiving annotated advice from an AI resulted in the highest diagnostic accuracy. Physicians rated the quality of AI advice higher than human advice. We did not find a strong effect of either manipulation on participants' confidence. The magnitude of the effects varied between task experts and non-task experts, with the latter benefiting considerably from correct explainable AI advice. These findings raise important considerations for the deployment of diagnostic advice in healthcare.

Insights on the Current State and Future Outlook of AI in Health Care: Expert Interview Study.

BACKGROUND: Artificial intelligence (AI) is often promoted as a potential solution for many challenges health care systems face worldwide. However, its implementation in clinical practice lags behind its technological development. OBJECTIVE: This study aims to gain insights into the current state and prospects of AI technology from the stakeholders most directly involved in its adoption in the health care sector whose perspectives have received limited attention in research to date. METHODS: For this purpose, the perspectives of AI researchers and health care IT professionals in North America and Western Europe were collected and compared for profession-specific and regional differences. In this preregistered, mixed methods, cross-sectional study, 23 experts were interviewed using a semistructured guide. Data from the interviews were analyzed using deductive and inductive qualitative methods for the thematic analysis along with topic modeling to identify latent topics. RESULTS: Through our thematic analysis, four major categories emerged: (1) the current state of AI systems in health care, (2) the criteria and requirements for implementing AI systems in health care, (3) the challenges in implementing AI systems in health care, and (4) the prospects of the technology. Experts discussed the capabilities and limitations of current AI systems in health care in addition to their prevalence and regional differences. Several criteria and requirements deemed necessary for the successful implementation of AI systems were identified, including the technology's performance and security, smooth system integration and human-AI interaction, costs, stakeholder involvement, and employee training. However, regulatory, logistical, and technical issues were identified as the most critical barriers to an effective technology implementation process. In the future, our experts predicted both various threats and many opportunities related to AI technology in the health care sector. CONCLUSIONS: Our work provides new insights into the current state, criteria, challenges, and outlook for implementing AI technology in health care from the perspective of AI researchers and IT professionals in North America and Western Europe. For the full potential of AI-enabled technologies to be exploited and for them to contribute to solving current health care challenges, critical implementation criteria must be met, and all groups involved in the process must work together.

Hand(y) hygiene insights: Applying three theoretical models to investigate hospital patients' and visitors' hand hygiene behavior.

BACKGROUND: Improving hand hygiene in hospitals is the most efficient method to prevent healthcare-associated infections. The hand hygiene behavior of hospital patients and visitors is not well-researched, although they pose a risk for the transmission of pathogens. Therefore, the present study had three aims: (1) Finding a suitable theoretical model to explain patients' and visitors' hand hygiene practice; (2) Identifying important predictors for their hand hygiene behavior; and (3) Comparing the essential determinants of hand hygiene behavior between healthcare professionals from the literature to our non-professional sample. METHODS: In total N = 1,605 patients and visitors were surveyed on their hand hygiene practice in hospitals. The employed questionnaires were based on three theoretical models: a) the Theory of Planned Behavior (TPB); b) the Health Action Process Approach (HAPA); and c) the Theoretical Domains Framework (TDF). Structural equation modeling was used to analyze the data. To compare our results to the determinants of healthcare workers' hand hygiene behavior, we searched for studies that used one of the three theoretical models. RESULTS: Among patients, 52% of the variance in the hand hygiene behavior was accounted for by the TDF domains, 44% by a modified HAPA model, and 40% by the TPB factors. Among visitors, these figures were 59%, 37%, and 55%, respectively. Two clusters of variables surfaced as being essential determinants of behavior: self-regulatory processes and social influence processes. The critical determinants for healthcare professionals' hand hygiene reported in the literature were similar to the findings from our non-professional sample. CONCLUSIONS: The TDF was identified as the most suitable model to explain patients' and visitors' hand hygiene practices. Patients and visitors should be included in existing behavior change intervention strategies. Newly planned interventions should focus on targeting self-regulatory and social influence processes to improve effectiveness.

Utilizing behavioral theories to explain hospital visitors' observed hand hygiene behavior.

BACKGROUND: Hand hygiene is essential for infection prevention. This study aimed to find a suitable theoretical model and identify critical facilitators and barriers to explain hospital visitors' hand hygiene practice. METHODS: Visitors in 4 hospitals were observed and asked to give explanations for using or not using the hand rub dispenser. The written explanations of N = 838 participants were coded according to three theoretical models: Theory of Planned Behavior, Health Action Process Approach (HAPA), and Theoretical Domains Framework (TDF). RESULTS: Self-reported hand hygiene behavior differed from observed behavior, with 15.75% wrongly claiming to have cleaned their hands. Critical facilitators for hand hygiene were attitude toward the behavior,subjective norm, outcome expectancies, risk perception, planning, action control, knowledge and skills, motivation and goals, and social influences. Key barriers included perceived behavioral control; barriers and resources; memory, attention, and decision processes; and environmental context and resources. CONCLUSIONS: Visitors' self-reported hand hygiene behavior is over-reported. Both HAPA and TDF were identified as suitable theoretical models for explaining visitor's hand hygiene practice. Future behavior change interventions should focus on (1) visibility and accessibility of cleaning products; (2) informing laypeople about their role regarding infection prevention; and (3) leveraging social influence processes.

Early and Later Perceptions and Reactions to the COVID-19 Pandemic in Germany: On Predictors of Behavioral Responses and Guideline Adherence During the Restrictions.

In March 2020, the German government enacted measures on movement restrictions and social distancing due to the COVID-19 pandemic. As this situation was previously unknown, it raised numerous questions about people's perceptions of and behavioral responses to these new policies. In this context, we were specifically interested in people's trust in official information, predictors for self-prepping behavior and health behavior to protect oneself and others, and determinants for adherence to social distancing guidelines. To explore these questions, we conducted three studies in which a total of 1,368 participants were surveyed (Study 1 N=377, March 2020; Study 2 N=461, April 2020; Study 3 N=530, April 2021) across Germany between March 2020 and April 2021. Results showed striking differences in the level of trust in official statistics (depending on the source). Furthermore, all three studies showed congruent findings regarding the influence of different factors on the respective behavioral responses. Trust in official statistics predicted behavioral responses in all three studies. However, it did not influence adherence to social distancing guidelines in 2020, but in 2021. Furthermore, adherence to social distancing guidelines was associated with higher acceptance rates of the measures and being older. Being female and less right-wing orientated were positively associated with guidelines adherence only in the studies from 2020. This year, political orientation moderated the association between acceptance of the measures and guideline adherence. This investigation is one of the first to examine perceptions and reactions during the COVID-19 pandemic in Germany across 1year and provides insights into important dimensions that need to be considered when communicating with the public.

We Have a Lot to Do: Lack of Sexual Protection and Information-Results of the German-Language Online Survey "Let's Talk About Chemsex".

Background: The prevalence of chemsex and sexualized substance use is increasing in several European countries, particularly among men who have sex with men. In this subgroup, illegal substance use is associated with increased sexual risk behavior, which can result in severe physical and psychological impairments. The present study examined the incidence and prevalence of chemsex in German-speaking countries. Methods: To further describe the high-risk group of Chemsex users, participants (N = 429) were asked about their psychotropic substance use, sexual and health-related behavior, health status, and socio-demographic information by using an online questionnaire. Whether Chemsex has negative effects on well-being was measured with the WHO well-being index. Of additional interest was how informed Chemsex users are about the topic and what needs are placed on the practitioners. The online questionnaire consisted of 105 items, and data was collected from March to May 2019. Thousand forty seven datasets were saved with a dropout rate of 59%, 123 completed questionnaires fulfilled the criteria for chemsex users (n =123). Results: There were no significant differences in well-being between chemsex users and non-users. All participants protected themselves against sexually transmitted diseases irregularly or not at all. The majority of chemsex users reported intermittently using illegal substances (ketamine, methamphetamine, mephedrone, γ-butyrolactone/γ-hydroxy butyric acid). They viewed their sexual and substance use behavior as problematic, but few showed motivation for behavior change. Chemsex users clearly expressed a need for more information and advice centers. Conclusion: The results provide information on chemsex users that can be used for the future development of a therapy manual and thus contribute to improving health care for this group. The prevalence of chemsex is increasing and urgently needs more research to protect clients from health impairments and stigmatization.

Well-being during COVID-19 pandemic: A comparison of individuals with minoritized sexual and gender identities and cis-heterosexual individuals.

BACKGROUND: Preliminary empirical data indicates a substantial impact of the COVID-19 pandemic on well-being and mental health. Individuals with minoritized sexual and gender identities are at a higher risk of experiencing such negative changes in their well-being. The objective of this study was to compare levels of well-being among cis-heterosexual individuals and individuals with minoritized sexual and gender identities during the COVID-19 pandemic. METHODS: Using data obtained in a cross-sectional online survey between April 20 to July 20, 2020 (N = 2332), we compared levels of well-being (WHO-5) across subgroups (cis-individuals with minoritized sexual identities, individuals with minoritized gender identities and cis-heterosexual individuals) applying univariate (two-sample t-test) and multivariate analysis (multivariate linear regression). RESULTS: Results indicate overall lower levels of well-being as well as lower levels of well-being in minoritized sexual or gender identities compared to cis-heterosexual individuals. Further, multivariate analyses revealed that living in urban communities as well as being in a relationship were positively associated with higher levels of well-being. Furthermore, a moderation analysis showed that being in a relationship reduces the difference between groups in terms of well-being. CONCLUSION: Access to mental healthcare for individuals with minoritized sexual and gender identities as well as access to gender-affirming resources should be strengthened during COVID-19 pandemic. Healthcare services with low barriers of access such as telehealth and online peer support groups should be made available, especially for vulnerable groups.

Do as AI say: susceptibility in deployment of clinical decision-aids.

Artificial intelligence (AI) models for decision support have been developed for clinical settings such as radiology, but little work evaluates the potential impact of such systems. In this study, physicians received chest X-rays and diagnostic advice, some of which was inaccurate, and were asked to evaluate advice quality and make diagnoses. All advice was generated by human experts, but some was labeled as coming from an AI system. As a group, radiologists rated advice as lower quality when it appeared to come from an AI system; physicians with less task-expertise did not. Diagnostic accuracy was significantly worse when participants received inaccurate advice, regardless of the purported source. This work raises important considerations for how advice, AI and non-AI, should be deployed in clinical environments.