Search Results for « Proactive Intervention »

Overcrowding in the emergency department is a frequent topic in the news. This results in long waiting time and dissatisfaction among patients, but also involves medical risks for the individual patient. When a high workload occurs in the emergency apartment, staff members have to work harder, which leads to reactive behavior. To improve both patient safety and the working environment, staff members and management need to be flexible, patient-focused, and proactive. Being proactive means taking control and making things happen, anticipating and preventing problems, as well as seizing opportunities. Emergency nurses perform advanced nursing interventions, a prerequisite of which is proactive behavior based on practical nursing wisdom. Being proactive requires something beyond normal work performance. However, not all staff members have sufficient knowledge and experience. Identifying proactive interventions that everyone can perform would be valuable.

Reddit has found its communities playing a prominent role in originating and propagating problematic sociopolitical discourse. Reddit administrators have generally struggled to prevent or contain such discourse for several reasons including: (1) the inability for a handful of human administrators to track and react to millions of posts and comments per day and (2) fear of backlash as a consequence of administrative decisions to ban or quarantine hateful communities. Consequently, administrative actions (community bans and quarantines) are often taken only when problematic discourse within a community spills over into the real world with serious consequences. In this paper, we investigate the feasibility of deploying tools to proactively identify problematic communities on Reddit. Proactive identification strategies show promise for three reasons: (1) they have potential to reduce the manual efforts required to track communities for problematic content, (2) they give administrators a scientific rationale to back their decisions and interventions, and (3) they facilitate early and more nuanced interventions (than banning or quarantining) to mitigate problematic discourse.

A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world. A critical challenge to such autonomy is the presence of irreversible states which require external assistance to recover from, such as when a robot arm has pushed an object off of a table. While standard agents require constant monitoring to decide when to intervene, we aim to design proactive agents that can request human intervention only when needed. To this end, we propose an algorithm that efficiently learns to detect and avoid states that are irreversible, and proactively asks for help in case the agent does enter them. On a suite of continuous control environments with unknown irreversible states, we find that our algorithm exhibits better sample- and intervention-efficiency compared to existing methods. Our code is publicly available at https://sites.google.com/view/proactive-interventions

BACKGROUND/OBJECTIVES: This study aimed to explore the feasibility of a Flemish adaptation of the New York University Caregiver Intervention (i.e., PROACTIVE intervention) modifying the recruitment and intervention content for informal caregivers of people with early cognitive decline, and across different subgroups. A feasibility study is necessary in order to reduce research waste for intervention adaptation and evaluation. METHODS: Researchers constructed, tested, and sent out a survey consisting of 43 questions on the following topics: awareness of symptoms of early cognitive decline, levels of cognitive performance using the updated Cognitive Performance Scale (CPS2), acceptability, and potential helpfulness of the intervention, and sociodemographic characteristics. Quantitative data were analyzed using descriptive statistics and logistic regression with SAS 9.4©. Qualitative data were analyzed using an inductive content analysis. RESULTS: A total of 463 informal caregivers completed the survey (mean age 58.8 ± 11.8, 83.6% female). Among them, 230 respondents who cared for people with cognitive decline indicated they would probably or certainly participate in the study. Identified factors influencing the recruitment were cognition, co-habitation, education, and employment status. Over half of the target caregivers indicated almost all services from the intervention could satisfy their needs. A majority perceived the PROACTIVE intervention would be helpful (69.4%), especially the CPS2 = 3 (76.1%) and CPS2 = 4 (74.1%) subgroups. CONCLUSION: The recruitment of target participants for a subsequent RCT evaluation study is feasible, and identified associated factors should be considered during the recruitment process. The PROACTIVE intervention and core components except 'peer-group participation' were perceived as helpful by most caregivers. The CPS2 = 3-4 subgroups were most accepting of the intervention and were most likely to benefit from the intervention.

The swift increasing number of mental health challenges worldwide demands prompt development of contemporary and extensiveproactive solutions for managing mental health. This paper researches how AmI combines with AI capabilities to alter mental healthtracking as well as intervention approaches. The paper provides an in-depth examination that explains the basic concepts of AmItogether with its applications for mental health surveillance and proactive intervention delivery. The study analyzes the processingof mental health diagnostic information from physiological and behavioral elements with contextual factors yet examines the needfor explaining AI models while ensuring ethical use and fairness of AI diagnostics.

So called "toxic behaviors" (e.g., hate speech, harassment, doxxing) are pervasive in online gaming communities, with research consistently documenting significant negative consequences for player wellbeing, community health, and industry revenue. While gaming studios and platforms have developed a range of reactive and proactive moderation strategies to address these harms, most existing approaches remain reactive, focusing on punishment after harm has already occurred. Community codes of conduct represent a foundational yet underutilized proactive intervention that has received comparatively little systematic attention. This literature review synthesizes existing research to address two core questions: (1) What is known about the design, implementation, and accessibility of community codes of conduct in gaming environments? and (2) How do codes of conduct influence player behavior? Drawing on a final corpus of 84 articles identified through systematic search of academic databases and supplemented by transparency reports from major gaming studios, the review maps the current landscape of both reactive and proactive moderation approaches before examining the specific role of community governance documents. Findings reveal significant gaps between current industry practice and evidence-based best practice. More than half of popular multiplayer games either lack accessible codes of conduct or bury them within legal documentation, limiting meaningful player engagement. Where codes do reach players, however, the evidence is promising: well-designed codes are associated with measurable reductions in toxic behavior, increased community engagement, and stronger community norms. Design features that enhance effectiveness include prominent placement and readability, values-centered and game-specific content, community-centered participatory development, transparent enforcement mechanisms, and integration of peer and community social influence. Despite these positive indicators, the evidence base remains limited, with most studies examining discrete platforms and short-term outcomes. This review concludes with evidence-based recommendations for industry practice, synthesized into the EFFECT Framework - a practical tool distilling six core evidence-based design principles for developing community codes of conduct that actively shape community culture. Together, these contributions call for greater research investment in how codes of conduct can be optimized as living community tools (rather than static legal documents) to meaningfully reduce toxicity and build resilient gaming communities.

Technological advancements have undoubtedly revolutionized various aspects of human life, altering the ways we perceive the world, engage with others, build relationships, and conduct our daily work routines. Among the recent advancements, the proliferation of virtual and mixed reality technologies stands out as a significant leap forward, promising to elevate our experiences and interactions to unprecedented levels. However, alongside the benefits, these emerging technologies also introduce novel avenues for harm and misuse, particularly in virtual and embodied spaces such as Zoom and virtual reality (VR) environments. The immersive nature of virtual reality environments raises unique challenges regarding psychological and emotional well-being. While VR can offer captivating and immersive experiences, prolonged exposure to virtual environments may lead to phenomena like cybersickness, disorientation, and even psychological distress in susceptible individuals. Additionally, the blurring of boundaries between virtual and real-world interactions in VR raises ethical concerns regarding consent, harassment, and the potential for virtual experiences to influence real-life behavior. Additionally, the increasing integration of artificial intelligence (AI) and machine learning algorithms in virtual spaces introduces risks related to algorithmic bias, discrimination, and manipulation. In VR environments, AI-driven systems may inadvertently perpetuate stereotypes, amplify inequalities, or manipulate user behavior through personalized content recommendations and targeted advertising, posing ethical dilemmas and societal risks.

Multimodal AI Agents are AI models that have the capability of interactively and cooperatively assisting human users to solve day-to-day tasks. Augmented Reality (AR) head worn devices can uniquely improve the user experience of solving procedural day-to-day tasks by providing egocentric multimodal (audio and video) observational capabilities to AI Agents. Such AR capabilities can help AI Agents see and listen to actions that users take which can relate to multimodal capabilities of human users. Existing AI Agents, either Large Language Models (LLMs) or Multimodal Vision-Language Models (VLMs) are reactive in nature, which means that models cannot take an action without reading or listening to the human user's prompts. Proactivity of AI Agents on the other hand can help the human user detect and correct any mistakes in agent observed tasks, encourage users when they do tasks correctly or simply engage in conversation with the user - akin to a human teaching or assisting a user. Our proposed YET to Intervene (YETI) multimodal agent focuses on the research question of identifying circumstances that may require the agent to intervene proactively. This allows the agent to understand when it can intervene in a conversation with human users that can help the user correct mistakes on tasks, like cooking, using AR. Our YETI Agent learns scene understanding signals based on interpretable notions of Structural Similarity (SSIM) on consecutive video frames. We also define the alignment signal which the AI Agent can learn to identify if the video frames corresponding to the user's actions on the task are consistent with expected actions. These signals are used by our AI Agent to determine when it should proactively intervene. We compare our results on the instances of proactive intervention in the HoloAssist multimodal benchmark for an expert agent guiding a user to complete procedural tasks.