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Clinician Ratings of Acceptability, Appropriateness, Feasibility and Usability of a Suicide Risk Flag Implemented in Behavioral Health Clinics
Get PDFRisk models that leverage data collected in electronic health records and insurance claims to estimate risk of suicide attempt are an innovation in suicide prevention. Published studies of their effectiveness and implementation are rare and few report clinicians’ perceptions of this technology. This study measured clinician ratings of acceptance, appropriateness, feasibility, usefulness, usability, and organizational readiness—both prior to intervention start and 6 months following use—among 298 respondents practicing in behavioral health clinics in three large health systems. Clinician ratings were neutral at pre-implementation with mean scores of 3.5 for acceptability, 3.6 for appropriateness, 3.7 for feasibility (scale 1-5, higher scores more favorable), 4.1 for usefulness (scale 1-7), and 57.0 for usability (scale 0-100). Readiness for change was also moderate (40.7, scale 0-60). At post-implementation, usability increased slightly but perceived usefulness declined; there were no significant changes in other measures. While a suicide risk model was not considered unacceptable, inappropriate, or infeasible, it was also not considered especially useful. If an ongoing effectiveness trial does not show superiority of the model over usual care in terms of suicide risk reduction, clinicians may be unlikely to endorse its use
Optimal Device-Type Inference in Cyber-Physical Systems Using Packet Data and Expert Rules
Get PDFAs cyber threats to cyber-physical systems continue to escalate, gaining a comprehensive understanding of the system architecture becomes essential for implementing effective defense strategies. However, it is often the case that even system owners lack a complete understanding of their systems. This underscores the need for developing approaches that facilitate the creation of detailed network maps to improve situational awareness and bolster cybersecurity measures. In this paper, we propose a novel approach that integrates mathematical optimization techniques with expert rules to infer device types based on communication patterns observed in passive packet capture (PCAP) data. This methodology serves as a crucial first step in developing detailed network maps, ultimately strengthening the security posture of cyber-physical systems
Channel Expansion Theory: A Comprehensive Meta-analysis, Literature Review, and Examination of Boundary Conditions
Get PDFChannel Expansion Theory (CET) has served as a foundational framework for understanding communication media selection and use since its introduction in the mid-1990s. While CET posits that individuals’ experiences with channel, partner, topic, and organizational context can expand perceptions of media richness, even for nominally “lean” media, empirical results have often been mixed across settings and constructs. This study presents a comprehensive literature review and meta-analysis of 31 quantitative studies applying CET across organizational and broader social contexts. The findings reveal when and how different forms of knowledge-building experience influence perceived media richness, media attitudes, and use behaviors, and discover important moderating effects of media synchronicity, communication setting, and power distance. This study advances our understanding of CET, resolves prior empirical inconsistencies, and provides directions for future research on technology-mediated communication and practical implications for media selection and media platform design
Enhancing Fairness in Image Classification: Modified Loss Functions for Mitigating Race and Sex Bias
Get PDFArtificial intelligence and machine learning have become ubiquitous in everyday life. With ubiquity, there is a need to ensure minimal algorithmic bias from these systems. Training data distributions can cause these systems to be unfair, negatively impacting users. One solution to this is to ensure fair, unbiased training sets. Unfortunately, unbiased training data is not enough to fully mitigate the issue. In this work, we utilize modified loss functions to mitigate race and sex bias that can appear when training machine learning models on biased data. We used face images from the FairFace dataset for binary and categorical classification tasks. Although FairFace has better diversity than other available face image datasets, it remains biased due to uneven distributions of race-sex groups. We find that the modified loss functions work moderately well at mitigating data bias. In some cases, combining multiple loss functions yields improved results compared to using one alone
An Eye for an Eye and A Smile for a Smile: Examining Aggressive and Supportive Behavioral Alignment Among Taiwanese Adolescence Who Play Video Games
Get PDFThe social dynamics of video games, especially those played online, are one of the primary motivators for gaming. Unfortunately, while there are numerous benefits to online gaming, there is also potentially harmful toxic and aggressive behavior. Prior research has investigated toxicity, but most studies rely on convenience samples, typically from WEIRD populations. The current study extends this scholarship with a nationally representative sample of Taiwanese adolescents who are frequent gamers, (a) quantifying their experiences with aggressive and supportive behaviors in games and (b) investigating personological-level variables that might explain reciprocating (referred to as “behavioral alignment”). Key findings were (1) aggressive behaviors were more common than supportive ones, but supportive behavioral alignment was more common (calculated using Jaccard coefficients) (2) trait conscientiousness reduced aggressive behavioral alignment, and (3) overparenting and social conformity increased both types of alignment
Entry Barriers and Opportunities for Fintechs in Open Banking Platforms
Get PDFThe revised Payment Services Directive (PSD2) is reshaping traditional banking by requiring banks to grant licensed third-party providers (TPPs) access to customer data. This paper explores PSD2’s impact on Fintech market entry, assessing both opportunities and ongoing structural barriers. Through a qualitative investigation, it identifies key barriers such as API standardization gaps, data access dependencies, and incumbent banks' strategic resistance. Simultaneously, it underscores emerging opportunities for Fintechs to gain market share and foster innovation. The findings suggest early signs of broader transformation, with both Fintechs and incumbents moving towards platform-based business models. The paper advances our understanding of digital platform regulation and governance and the emergence of digital ecosystems
Introduction to the Minitrack on Digitization of the Individual – Personal Decision Analytics
No full textIntroduction to the Minitrack on Advancing Software Resilience in the Age of LLM - Testing, Quality Assurance, and Metrics for Robust Engineering
No full textAcceptance of Artificial Intelligence Algorithms in Local Governance: Citizens’ Perspective
Get PDFBased on data from three representative surveys conducted in Singapore, Tallinn, and Warsaw, this study examines individual-level factors influencing the acceptance of AI in local governance. Drawing on existing literature, we identified four key variables: a) citizen efficacy, b) confidence in local governance, c) experience with technology, d) technology-related anxiety. We conducted separate Structural Equation Models for each city to uncover context-specific dynamics. Our results reveal a consistent positive relation between confidence in governance and AI acceptance. In Singapore, citizens demonstrate higher individual agency, with both civic efficacy and institutional confidence driving AI acceptance, while prior technology experience plays a lesser role. In Warsaw, confidence in governance and technology-related anxiety emerge as strong predictors. In Tallinn, technology-related anxiety is the only significant factor, suggesting a more emotionally driven public approach toward AI. These findings highlight the importance of sociopolitical context in shaping public attitudes toward algorithmic decision-making in local governance