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1. Investigating Cyber Attacker Team Cognition

   https://doi.org/10.1177/1071181322661132  

   Johnson, Craig J.; Ferguson-Walter, Kimberly J.; Gutzwiller, Robert S.; Scott, Dakota D.; Cooke, Nancy J. (September 2022, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Cyber attackers commonly operate in teams, which may process information collectively and thus, may be best understood when the team is treated as the unit of analysis. Future research in Oppositional Human Factors (OHF) should consider the impact of team-influencing and team-level biases and the impact that defensive interventions have on team cognition in general. Existing measurement approaches using team interactions may be well suited for studying red teams, and how OHF interventions impact cyber attackers. 

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2. An empirical study of using radiology reports and images to improve intensive care unit mortality prediction

   https://doi.org/10.1093/jamiaopen/ooae137  

   Lin, Mingquan; Wang, Song; Ding, Ying; Zhao, Lihui; Wang, Fei; Peng, Yifan (February 2025, JAMIA Open)

   Abstract ObjectivesThe predictive intensive care unit (ICU) scoring system is crucial for predicting patient outcomes, particularly mortality. Traditional scoring systems rely mainly on structured clinical data from electronic health records, which can overlook important clinical information in narratives and images. Materials and MethodsIn this work, we build a deep learning-based survival prediction model that utilizes multimodality data for ICU mortality prediction. Four sets of features are investigated: (1) physiological measurements of Simplified Acute Physiology Score (SAPS) II, (2) common thorax diseases predefined by radiologists, (3) bidirectional encoder representations from transformers-based text representations, and (4) chest X-ray image features. The model was evaluated using the Medical Information Mart for Intensive Care IV dataset. ResultsOur model achieves an average C-index of 0.7829 (95% CI, 0.7620-0.8038), surpassing the baseline using only SAPS-II features, which had a C-index of 0.7470 (95% CI: 0.7263-0.7676). Ablation studies further demonstrate the contributions of incorporating predefined labels (2.00% improvement), text features (2.44% improvement), and image features (2.82% improvement). Discussion and ConclusionThe deep learning model demonstrated superior performance to traditional machine learning methods under the same feature fusion setting for ICU mortality prediction. This study highlights the potential of integrating multimodal data into deep learning models to enhance the accuracy of ICU mortality prediction. 

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3. Exploring the Social Contexts of Exoskeleton Design and Implementation in Long-Term Care: A Study of Nurses and Nurse Managers with Musculoskeletal Disorders

   https://doi.org/10.1177/10711813241275507  

   Scheer, Eleanore_Rae; Atweh, Jad_A; Arora, Jessica; Thompson, Elizabeth; Murray, Jaiden; Sellers, Elvie; Srinivasan, Divya; Valdez, Rupa_S (August 2024, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Nurses face significant physical demands during patient care, leading to high rates of musculoskeletal disorders (MSDs) among nurses in long-term care. Exoskeletons demonstrate promise in supporting nurses and nurse managers with MSDs; however, social contextual factors are crucial to their design and implementation. Through thematic analysis of 17 semi-structured interviews, this paper reveals social contextual factors important to exoskeleton use among nurses and nurse managers in long-term care. Participants expressed concerns about workplace discrimination, co-worker perceptions of their capabilities, and patient confidence. Our findings highlight the need for supportive organizational cultures and open communication channels. Recommendations include in-depth systems analysis to assess exoskeleton feasibility and efficacy, involving input from frontline nurses/managers, management, and patients. These findings can aid human factors and ergonomics (HF/E) experts in balancing social contextual factors and other work system elements to design work system contexts and exoskeletons that promote optimal outcomes in long-term care settings. 

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4. A "Distance Matters" Paradox: Facilitating Intra-Team Collaboration Can Harm Inter-Team Collaboration

   https://doi.org/10.1145/3512895  

   Hu, Xinlan Emily; Hinds, Rebecca; Valentine, Melissa; Bernstein, Michael S (March 2022, Proceedings of the ACM on Human-Computer Interaction)

   By identifying the socio-technical conditions required for teams to work effectively remotely, the Distance Matters framework has been influential in CSCW since its introduction in 2000. Advances in collaboration technology and practices have since brought teams increasingly closer to achieving these conditions. This paper presents a ten-month ethnography in a remote organization, where we observed that despite exhibiting excellent remote collaboration, teams paradoxically struggled to collaborate across team boundaries. We extend the Distance Matters framework to account for inter-team collaboration, arguing that challenges analogous to those in the original intra-team framework --- common ground, collaboration readiness, collaboration technology readiness, and coupling of work --- persist but are actualized differently at the inter-team scale. Finally, we identify a fundamental tension between the intra- and inter-team layers: the collaboration technology and practices that help individual teams thrive (e.g., adopting customized collaboration software) can also prompt collaboration challenges in the inter-team layer, and conversely the technology and practices that facilitate inter-team collaboration (e.g., strong centralized IT organizations) can harm practices at the intra-team layer. The addition of the inter-team layer to the Distance Matters framework opens new opportunities for CSCW, where balancing the tension between team and organizational collaboration needs will be a critical technological, operational, and organizational challenge for remote work in the coming decades. 

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5. From Teams to Teamness: Future Directions in the Science of Team Cognition

   https://doi.org/10.1177/00187208231162449  

   Cooke, Nancy J.; Cohen, Myke C.; Fazio, Walter C.; Inderberg, Laura H.; Johnson, Craig J.; Lematta, Glenn J.; Peel, Matthew; Teo, Aaron (March 2023, Human Factors: The Journal of the Human Factors and Ergonomics Society)

   Objective We review the current state-of-the-art in team cognition research, but more importantly describe the limitations of existing theories, laboratory paradigms, and measures considering the increasing complexities of modern teams and the study of team cognition. Background Research on, and applications of, team cognition has led to theories, data, and measures over the last several decades. Method This article is based on research questions generated in a spring 2022 seminar on team cognition at Arizona State University led by the first author. Results Future research directions are proposed for extending the conceptualization of teams and team cognition by examining dimensions of teamness; extending laboratory paradigms to attain more realistic teaming, including nonhuman teammates; and advancing measures of team cognition in a direction such that data can be collected unobtrusively, in real time, and automatically. Conclusion The future of team cognition is one of the new discoveries, new research paradigms, and new measures. Application Extending the concepts of teams and team cognition can also extend the potential applications of these concepts. 

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