Cynthia M. Baseman, Myeonghan Ryu, Nathaniel Swinger
et al.
Psychotherapy delivery relies on a negotiation between patient self-reports and clinical intuition. Growing evidence for technological support of psychotherapy suggests opportunities to aid the mediation of this tension. To explore this prospect, we designed a prototype of a clinical decision support system (CDSS) for treating veterans with post-traumatic stress disorder in a Prolonged Exposure (PE) therapy intensive outpatient program. We conducted a two-phase interview study to collect perspectives from practicing PE clinicians and former PE patients who are United States veterans. Our analysis distills opportunities for a CDSS (e.g., offering homework review at a glance, aiding patient conceptualization) and larger challenges related to context and deployment (e.g., navigating Veterans Affairs). By reframing our findings through three human-centered perspectives (distributed cognition, situated learning, infrastructural inversion), we highlight the complexities of designing a CDSS for psychotherapists in this context and offer theory-aligned design considerations.
The Supplemental Nutrition Assistance Program (SNAP), the largest U.S. nutrition assistance program, provides financial support to Americans with low income to purchase food. However, SNAP benefits cannot be used to purchase prepared foods, including foods at restaurants. The Restaurant Meals Program (RMP), a program under SNAP offices, offers an important opportunity, yet an underutilized strategy, to improve food access and food security for some of the most vulnerable individuals, including older adults, people experiencing homelessness, and those with disabilities, by allowing them to use SNAP benefits to purchase food at participating restaurants. Though introduced as an option for states in 1977, uptake of RMP has been low, with only nine states participating as of 2025. The factors driving or hindering RMP adoption and effective implementation are poorly understood, leaving a critical gap in policy and practice. To fill these knowledge gaps, this study utilized a rapid literature review, followed by key informant interviews with state administrators of RMP and owners of independent restaurants participating in RMP. Key drivers for adoption and implementation of RMP included motivations to champion food access and food security; to connect local restaurants, communities, and cultures; and to stimulate local economies. Conversely, major constraints included onerous administrative processes for both states and restaurants; fast-food chain domination undermining the driver of connecting local restaurants, communities, and cultures; overcoming misconceptions and negative public opinions about the program; and addressing gaps in program evaluation efforts. These findings highlight the multi-level nature of factors, ranging from intrapersonal motivations to broader policy and administrative domains, that require attention for the successful and equitable expansion of RMP, and highlight RMP as an opportunity to promote agency, dignity, and equity in food assistance, particularly for vulnerable groups least able to prepare meals at home. Recommendations include streamlining enrollment, prioritizing independent restaurant participation, improving federal guidance, and investing in program evaluation.
Anaya Hall, Laura Edwards-Orr, Andrew Carberry
et al.
JAFSCD is delighted to share this inaugual column on the topic of value chain coordination. We define VCC as the development of relational infrastructure—networks, information channels, and partnerships—that support thriving and sustainable regional food economies. JAFSCD also announces a new series of program, policy, and practice briefs focused on value chain coordination This series aims to synthesize the current knowledge on coordinating values-based food supply chains into concise, high-impact, practitioner-focused briefs. The series and associated articles are being curated by a JAFSCD Value Chain Coordination Editorial Circle made up of the scholars and practitioners below, who will also jointly produce this quarterly column.
• Kathryn Barr, Associate, SupplyChange
• Patrick Baur, Associate Professor, University of Rhode Island
• Analena Bruce, Assistant Professor, University of New Hampshire
• Andrew Carberry, Project Manager, Wallace Center at Winrock International
• Eric DeLuca, Consultant, Food Finance Institute
• Laura Edwards-Orr, Senior Agricultural Marketing Specialist, USDA Agricultural Marketing Service, Local and Regional Food Division
• Anaya Hall, Postdoctoral Scholar, The Food Connection at the University of Kentucky
• Heather (“H”) Nieto-Friga, CEO, SupplyChange
• Ashton Potter, Executive Director, The Food Connection at the University of Kentucky
• Elliott Smith, Consultant, Kitchen Sync Strategies
• Jodee Smith, Executive Director, FARMWISE Indiana
• Ye Su, Assistant Professor, Lincoln University of Missouri
• Dawn Thilmany, Professor, Colorado State University
• Kamran Zendehdel, Research Branch Chief, USDA Agricultural Marketing Service, Local and Regional Food Division
* * *
At a moment when food systems stakeholders are navigating supply chain disruption, market consolidation, and increasing climate-driven risk, questions of how best to retain or expand benefits for small and midsize farms and food businesses are increasingly urgent. Across various geographies and markets, value chain coordination (VCC) has emerged as a critical lever for addressing these challenges. In this inaugural column, we introduce VCC—a concept that bridges many concerns of JAFSCD readers—and suggest a few topics the series may address. We also provide some context on the first brief, which focuses on how VCC offers a pathway to address challenges associated with institutional purchasing and harness this strategic opportunity to support regional and sustainable food producers.
Chitralekha Gupta, Hanjun Wu, Praveen Sasikumar
et al.
Wearable devices are transforming human capabilities by seamlessly augmenting cognitive functions. In this position paper, we propose a voice-based, interactive learning companion designed to amplify and extend cognitive abilities through informal learning. Our vision is threefold: (1) to enable users to discover new knowledge on-the-go through contextual interactive quizzes, fostering critical thinking and mindfulness, (2) to proactively detect misinformation, empowering users to critically assess information in real time, and (3) to provide spoken language correction and prompting hints for second language learning and effective communication. As an initial step toward this vision, we present Factually - a proactive, wearable fact-checking system integrated into devices like smartwatches or rings. Factually discreetly alerts users to potential falsehoods via vibrotactile feedback, helping them assess information critically. We demonstrate its utility through three illustrative scenarios, highlighting its potential to extend cognitive abilities for real-time misinformation detection. Early qualitative feedback suggests that Factually can enhance users' fact-checking capabilities, offering both practical and experiential benefits.
Retrieval-Augmented Generation (RAG) systems offer a powerful approach to enhancing large language model (LLM) outputs by incorporating fact-checked, contextually relevant information. However, fairness and reliability concerns persist, as hallucinations can emerge at both the retrieval and generation stages, affecting users' reasoning and decision-making. Our research explores how tailored warning messages -- whose content depends on the specific context of hallucination -- shape user reasoning and actions in an educational quiz setting. Preliminary findings suggest that while warnings improve accuracy and awareness of high-level hallucinations, they may also introduce cognitive friction, leading to confusion and diminished trust in the system. By examining these interactions, this work contributes to the broader goal of AI-augmented reasoning: developing systems that actively support human reflection, critical thinking, and informed decision-making rather than passive information consumption.
Effective communication between AI and humans is essential for successful human-AI co-creation. However, many current co-creative AI systems lack effective communication, which limits their potential for collaboration. This paper presents the initial design of the Framework for AI Communication (FAICO) for co-creative AI, developed through a systematic review of 107 full-length papers. FAICO presents key aspects of AI communication and their impact on user experience, offering preliminary guidelines for designing human-centered AI communication. To improve the framework, we conducted a preliminary study with two focus groups involving skilled individuals in AI, HCI, and design. These sessions sought to understand participants' preferences for AI communication, gather their perceptions of the framework, collect feedback for refinement, and explore its use in co-creative domains like collaborative writing and design. Our findings reveal a preference for a human-AI feedback loop over linear communication and emphasize the importance of context in fostering mutual understanding. Based on these insights, we propose actionable strategies for applying FAICO in practice and future directions, marking the first step toward developing comprehensive guidelines for designing effective human-centered AI communication in co-creation.
Recent pose-transfer methods aim to generate temporally consistent and fully controllable videos of human action where the motion from a reference video is reenacted by a new identity. We evaluate three state-of-the-art pose-transfer methods -- AnimateAnyone, MagicAnimate, and ExAvatar -- by generating videos with actions and identities outside the training distribution and conducting a participant study about the quality of these videos. In a controlled environment of 20 distinct human actions, we find that participants, presented with the pose-transferred videos, correctly identify the desired action only 42.92% of the time. Moreover, the participants find the actions in the generated videos consistent with the reference (source) videos only 36.46% of the time. These results vary by method: participants find the splatting-based ExAvatar more consistent and photorealistic than the diffusion-based AnimateAnyone and MagicAnimate.
This study examines the impact of financial inclusion on poverty in Sub-Saharan Africa (SSA) using data from 45 countries between 2001 and 2020. We find clear differences in how financial inclusion affects poverty across groups of countries: In the first group, financial inclusion reduces poverty, but in the other two groups, it increases poverty. Countries with higher incomes, lower inflation, stronger human capital, and greater trade openness are more likely to experience reduced poverty through financial inclusion. In countries lacking these conditions, financial inclusion can exacerbate poverty. These findings demonstrate that reforms aimed at increasing incomes, enhancing human capital, maintaining low inflation, and promoting trade are essential for facilitating financial inclusion and reducing poverty. Such reforms need to be tailored to each country's specific conditions and be part of broader strategies that encompass education, agriculture, and fair access to financial services.
Economic growth, development, planning, Human settlements. Communities
Liqun Zhao, Keyan Miao, Konstantinos Gatsis
et al.
Reinforcement learning (RL) excels in applications such as video games, but ensuring safety as well as the ability to achieve the specified goals remains challenging when using RL for real-world problems, such as human-aligned tasks where human safety is paramount. This paper provides safety and stability definitions for such human-aligned tasks, and then proposes an algorithm that leverages neural ordinary differential equations (NODEs) to predict human and robot movements and integrates the control barrier function (CBF) and control Lyapunov function (CLF) with the actor-critic method to help to maintain the safety and stability for human-aligned tasks. Simulation results show that the algorithm helps the controlled robot to reach the desired goal state with fewer safety violations and better sample efficiency compared to other methods in a human-aligned task.
Informal caregivers (e.g.,family members or friends) of people living with Alzheimers Disease and Related Dementias (ADRD) face substantial challenges and often seek informational or emotional support through online communities. Understanding the factors that drive engagement within these platforms is crucial, as it can enhance their long-term value for caregivers by ensuring that these communities effectively meet their needs. This study investigated the user interaction dynamics within two large, popular ADRD communities, TalkingPoint and ALZConnected, focusing on topic initiator engagement, initial post content, and the linguistic patterns of comments at the thread level. Using analytical methods such as propensity score matching, topic modeling, and predictive modeling, we found that active topic initiator engagement drives higher comment volumes, and reciprocal replies from topic initiators encourage further commentor engagement at the community level. Practical caregiving topics prompt more re-engagement of topic initiators, while emotional support topics attract more comments from other commentors. Additionally, the linguistic complexity and emotional tone of a comment influence its likelihood of receiving replies from topic initiators. These findings highlight the importance of fostering active and reciprocal engagement and providing effective strategies to enhance sustainability in ADRD caregiving and broader health-related online communities.
Luca Castri, Gloria Beraldo, Sariah Mghames
et al.
Deploying robots in human-shared spaces requires understanding interactions among nearby agents and objects. Modelling cause-and-effect relations through causal inference aids in predicting human behaviours and anticipating robot interventions. However, a critical challenge arises as existing causal discovery methods currently lack an implementation inside the ROS ecosystem, the standard de facto in robotics, hindering effective utilisation in robotics. To address this gap, this paper introduces ROS-Causal, a ROS-based framework for onboard data collection and causal discovery in human-robot spatial interactions. An ad-hoc simulator, integrated with ROS, illustrates the approach's effectiveness, showcasing the robot onboard generation of causal models during data collection. ROS-Causal is available on GitHub: https://github.com/lcastri/roscausal.git.
Scientific opinion is now unanimous that global temperatures are likely to continue to rise with concomitant extreme weather patterns and events. There is a protean body of scientific literature available on global warming and climate change, which is affecting urban living in every respect from ‘heat islands’, continuous light and sea level changes as well as severe droughts and floods paralysing urban areas. Urban planning implications are reflected in buildings, street and community design for more environmentally sustainable cities. The urban science related to climate change and its implications for human settlement is in its early stages. Nonetheless, climate change is already becoming a concern of insurance and actuarial industries as they begin to assess risk to human settlement, construction and other risks associated with atmospheric conditions. These cannot be anticipated and need to be examined with a new paradigm for urban problem solving which is outlined in this paper.
Amy E. Thompson, John P. Walden, Adrian S Z Chase
et al.
Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method—combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups—to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization.
Large language models (LLMs) are increasingly capable and prevalent, and can be used to produce creative content. The quality of content is influenced by the prompt used, with more specific prompts that incorporate examples generally producing better results. On from this, it could be seen that using instructions written for crowdsourcing tasks (that are specific and include examples to guide workers) could prove effective LLM prompts. To explore this, we used a previous crowdsourcing pipeline that gave examples to people to help them generate a collectively diverse corpus of motivational messages. We then used this same pipeline to generate messages using GPT-4, and compared the collective diversity of messages from: (1) crowd-writers, (2) GPT-4 using the pipeline, and (3 & 4) two baseline GPT-4 prompts. We found that the LLM prompts using the crowdsourcing pipeline caused GPT-4 to produce more diverse messages than the two baseline prompts. We also discuss implications from messages generated by both human writers and LLMs.
As high-speed, agile robots become more commonplace, these robots will have the potential to better aid and collaborate with humans. However, due to the increased agility and functionality of these robots, close collaboration with humans can create safety concerns that alter team dynamics and degrade task performance. In this work, we aim to enable the deployment of safe and trustworthy agile robots that operate in proximity with humans. We do so by 1) Proposing a novel human-robot doubles table tennis scenario to serve as a testbed for studying agile, proximate human-robot collaboration and 2) Conducting a user-study to understand how attributes of the robot (e.g., robot competency or capacity to communicate) impact team dynamics, perceived safety, and perceived trust, and how these latent factors affect human-robot collaboration (HRC) performance. We find that robot competency significantly increases perceived trust ($p<.001$), extending skill-to-trust assessments in prior studies to agile, proximate HRC. Furthermore, interestingly, we find that when the robot vocalizes its intention to perform a task, it results in a significant decrease in team performance ($p=.037$) and perceived safety of the system ($p=.009$).