BACKGROUND:Effective trauma care depends on technical proficiency and teamwork under pressure. While simulation has become integral to trauma education, few reports detail the methodological processes and lessons learned from developing highly complex operative simulations designed to both assess and improve team performance. METHODS:We developed and implemented a high-fidelity trauma surgery simulation to evaluate multidisciplinary intraoperative team performance. Twenty-two surgical teams-each composed of an attending surgeon, resident, anesthesiologist, circulating nurse, and scrub technician-completed two trauma scenarios requiring surgical intervention for a patient presenting in hemorrhagic shock: Scenario 1-liver/iliac injuries; Scenario 2-kidney/spleen injuries. Scenario development incorporated multidisciplinary perspectives, high-fidelity simulators, and iterative pilot testing. A dual-layer performance framework was created to operationalize measurement of taskwork and teamwork, using time-stamped video analysis and validated postsimulation surveys assessing teamwork, psychological safety, and shared mental models. RESULTS:Through iterative refinement, rater calibration, and multidisciplinary collaboration, the team established a feasible framework for delivering complex, realistic, surgical trauma team simulations. Challenges and lessons learned are described. CONCLUSIONS:This methods paper outlines the development and execution of a high-fidelity trauma surgery simulation aimed at enhancing operative team readiness. Lessons learned underscore the importance of deliberate scenario design, planning, and multidisciplinary coordination to optimize outcomes. The framework described provides a reproducible model for institutions seeking to implement simulation-based trauma training and performance assessment programs. LEVEL OF EVIDENCE:Prognostic/Epidemiological; Level IV.

As artificial intelligence (AI) becomes increasingly embedded in critical care settings, there is a pressing need to understand how these technologies interact with human teams responsible for high-stakes decision-making. This paper introduces a tailored Input-Mediator-Output-Input (IMOI) model to conceptualize the complex, cyclical dynamics of human-AI teaming in environments such as intensive care units and emergency departments. Building on principles from team science and information processing theory, the model identifies key inputs (e.g., AI capabilities, team composition, interface design), mediators (e.g., trust, communication, coordination), and outputs (e.g., team performance, patient outcomes), while accounting for moderating factors like clinician experience, stress, and AI transparency. A critical feature of the model is its feedback loop, through which outcomes inform future team behaviors, training, and system redesign. The paper outlines practical applications for healthcare training, AI system design, and simulation-based evaluation, offering a comprehensive roadmap for integrating AI as an adaptive, trustworthy member of clinical teams. Importantly, this model is conceptual and has not yet been empirically validated; it is intended to serve as a foundation for future empirical research. This model supports ongoing quality improvement initiatives and promotes safer, more effective human-AI collaboration in time-sensitive, high-pressure care environments.

This meta-analysis examined how transactive memory systems (TMS) operationalization, task characteristics, and outcome measurements moderate TMS-team outcome relationships in laboratory studies. Based on 44 studies (103 effect sizes), TMS demonstrated a moderate positive relationship with team outcomes (r = .44, 95% CI [0.36, 0.50], p < .001). Team outcome measurement approach emerged as a significant moderator: self-report scales yielded larger effects (r = .77) than observer ratings (r = .38) or embedded metrics (r = .39). Findings highlight the importance of measurement decisions in TMS research.

Consumers are increasingly loyal to brands that are inclusive of them and their values, which promotes a sense of belonging. The goal of inclusive design is to design for the widest population possible; however, technology product inclusivity is a multifaceted concept that reflects consumer experience with a product, perceptions of trust, and ways to satisfy psychological needs. Technology products that foster a sense of belonging may promote perceptions of product quality, satisfaction, and usability. Although many measurement scales have been developed to assess consumer perceptions of technology products, no published scales exist to assess consumer perceptions of product inclusivity. The authors of this article adhered to best practices in scale development to generate an item pool based on a literature review, expert review, pilot studies, exploratory factor analysis (EFA; N = 785), and two confirmatory factor analyses (CFA; N = 677; CFA II; N = 588). We refined an initial pool of 194 items to a 25-item scale. Factor analyses indicate 5 factors contribute to the perceptions of product inclusivity: Personal Connection, Product Challenges, Confidence in Usage, Meets Expectations, and Company Empathy. The goal of developing this scale is to allow companies and product designers to measure how inclusive their technology product is, as well as to gain insight into areas of inclusivity that excel or could be improved.

Patient-centered communication (PCC) underpins high-quality medical care, yet its traditional dyadic, longitudinal models are ill-suited to the emergency department (ED). In the ED, visits are brief, unpredictable, and often involve clinicians and patients with no prior relationship. This mismatch creates a paradox: the fast-paced, high-uncertainty environment heightens the need for PCC while simultaneously impeding its enactment. Drawing on the science of teamwork, we propose conceptualizing PCC as a team process. Our approach reconceptualizes communication as a coordinated, interdisciplinary process. In this model, an ED PCC team-comprising physicians, nurses, ancillary staff, and the patient (and caregivers when appropriate)-functions as a "swift-starting action team," required to achieve high-stakes goals shortly after formation. By integrating teamwork constructs, including the input-process-outcome-input model and the consideration of team cognition, our proposed conceptualization expands the scope of PCC research and creates a pathway to design interventions that facilitate PCC in the fast-paced ED setting. This opens new avenues for research, education, and quality improvement across emergency settings.

The rapid deployment of artificial intelligence (AI) across domains has highlighted the critical challenges of ensuring safe, transparent, and effective human-AI integration. Despite the growing body of work examining individual aspects of human-AI interaction, such as trust, usability, and performance, these factors are often studied in isolation, limiting the collective understanding of how human and AI characteristics jointly shape outcomes over time in a team setting. The lack of a comprehensive framework for studying human-AI interactions makes it difficult to understand what factors are of most importance. In order to address this gap, the Human-AI Interaction Framework, grounded in the Inputs-Mediators-Outputs-Inputs (IMOI) process model, is outlined and explained. In this framework, the initial inputs would be organized into AI system characteristics (e.g., the sources and algorithms the AI utilizes, and the observable features related to its information output) and human characteristics (i.e., the individual differences the user brings into the interaction). These inputs are influenced by moderators (i.e., task and situation factors) and mediated by processes and emergent states to influence outputs of the framework, which are classified into human-AI performance and human perceptions factors. In this framework, these outputs feed into the subsequent performance episode’s inputs through feedback: the AI adaptations and human adaptations that commence. Overall, this framework seeks to guide researchers in studying human-AI interactions in a way that provides a structured approach to increase generalizability of findings and support the design of safer, more transparent, and effective human-AI teams.

This work explores the potential transfer of military and professional healthcare XR training techniques to civilian settings. This is accomplished by briefly discussing key insights gleaned from military and professional healthcare applications of XR training technologies. Based on these insights, recommendations surrounding civilian lay-provider applications for this technology are provided, as well as practical considerations regarding its adoption. The lack of transfer of these XR technologies into basic medical training of the civilian lay-providers showcases the inequality in adoption of the practice. With the boom of XR exploration in healthcare and military settings, it stands that there are areas outside of professional use that may benefit from the tool. The implications of which include the potential for a greater pursuit of life-saving knowledge and more accessible training opportunities for civilian lay-providers.

In this introduction to the special issue, the guest editors situate the selected articles within the ongoing conversation about Human Factors in Aviation.

Student evaluations of teaching significantly impact faculty careers but have been shown to exhibit biases, notably regarding instructor gender. Previous research consistently finds that women faculty receive lower student ratings compared to men - even when objective indicators such as student performance are equal. This study investigates how evaluation question framing influences bias expression. The research analyzes whether asking students to assess observable, concrete instructional behaviors rather than subjective instructor characteristics reduces gender bias in midterm feedback. Using multivariate analyses of evaluations from 967 students and 27 faculty from diverse institutions and disciplines, the study found no statistically significant gender differences when feedback was structured around specific instructional behaviors. Instead, significant variations emerged related to instructor age and teaching experience, highlighting the importance of contextualizing feedback within a developmental framework. These results suggest that behaviorally anchored evaluation instruments can produce fairer, more constructive student feedback. Institutions seeking equitable and meaningful teaching evaluations may thus benefit from adopting formative, behavior-focused tools that emphasize specific teaching practices rather than subjective traits.

Long-duration space exploration (LDSE) presents significant psychological and physical challenges for astronauts, including isolation, stress, and team coordination issues. This paper explores gamification, which refers to the use of game mechanics in non-game contexts, as a novel approach to enhance training, collaboration, social interaction, and overall well-being in LDSE missions. Drawing on insights from other high-stakes domains, like healthcare or the military, a cursory review of gamification literature identified effective game mechanics such as immediate feedback, progress tracking, adaptive challenges, and social features. These mechanics were evaluated across five core domains relevant to space missions: training, collaboration, social interaction, psychological well-being, and physical well-being.

Artificial intelligence (AI) is becoming increasingly integrated into aviation, transforming numerous operations, including those performed at the flight deck. This paper explores theoretical approaches to optimizing human-AI teamwork in the context of aviation, such as trust and role clarity, to enhance safety and efficiency. It also outlines strategies for effective task allocation in human-AI teams using a previously developed conceptual model. By applying teamwork and cognitive principles, such as situation awareness, it examines the complementary strengths of humans and AI, and addresses how AI can serve both as a tool and as a collaborative teammate in aviation contexts. This paper evaluates human strengths, such as adaptive decision-making and AI capabilities, including real-time data processing, alongside shared limitations like fatigue and inflexibility. It discusses the risks of over-reliance on AI, reduced situational awareness, and cybersecurity threats. Best practices for fostering trust, clear roles, and interdependence are presented, drawing from Crew Resource Management (CRM) principles. This work extends human factors research by applying a novel theoretical framework to human-AI collaboration in aviation. Unlike prior studies focused solely on technological advances, it provides actionable insights for task allocation, risk mitigation, and training, supporting balanced and effective human-AI teams in the flight deck.

This study evaluates the relationships between individual and team-level factors in influencing burnout among clinical healthcare providers. Focusing on psychological safety, perceived autonomy, perceived team effectiveness, and emotional intelligence, the research aims to understand how these elements contribute to the prevalence and severity of burnout symptoms. Using electronic questionnaires analyzed through Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM), the study sampled 180 healthcare providers from one large US medical center. The study results found that psychological safety significantly decreases levels of burnout, particularly emotional exhaustion. The results on team effectiveness suggest a complex relationship with burnout, with different dimensions having varied impacts. The study did not find support for the negative prediction of burnout by perceived autonomy and emotional intelligence, contrary to expectations based on prior research. These findings have practical implications for healthcare management, stressing the importance of psychological safety and effective team dynamics in reducing burnout. Overall, this study contributes significantly to understanding burnout in healthcare, emphasizing the critical role of team structures and individual emotional resilience in managing workplace well-being.

Better understanding of the implications of many aspects of human behavior, especially that of cybersecurity professionals, can help develop the cybersecurity workforce. Despite efforts aimed at documenting and understanding cybersecurity professionals’ knowledge, understanding of how cognition supports human performance in specific cybersecurity tasks remains limited. Specifically, understanding of the elements of situation awareness (SA), defined as goal-directed knowledge, is necessary to support human-centered evaluation, selection, training, and recruitment strategies. In this poster, we propose a framework for developing measures of situation awareness for cybersecurity professionals and a method of capturing initial effectiveness data that does not rely on access to proprietary information. While this will not be a complete solution to the problem of limited access, we aim to shorten the path between observation and measurement by demonstrating a process of creating SA measurement from a ransomware simulation.

Handoffs continue to be an integral and necessary component of patient care. Despite efforts to improve handoffs, there continue to be misconceptions about their implementation and function. As such, the present paper provides seven myths about handoffs that continue to persist and have the potential to lead to subpar processes and outcomes. The seven myths presented in this paper are: 1) Structured protocols are all that teams need to be successful, 2)Individuals with strong communication skills will always have successful handoffs, 3) Experts already know, so they don’t need handoff tools and training, 4) What constitutes “important” information is unanimous, 5) Handoffs are the frontline’s responsibility, 6) Handoff interventions and tools should be constant, and 7) Measurement should be considered last. Recognizing the impact of these myths, the present paper aims to debunk these misconceptions and provide actionable recommendations to address these inaccuracies.

The Perceptions of Technology Product Inclusivity (PTPI) Scale has been previously validated with a wide variety of technology products and users, as well as with special populations such as people with disabilities. This study explores the use of the PTPI with older adults to determine if the scale is appropriate to measure technology product inclusivity with this population. The PTPI was administered via Qualtrics to 520 older adults and 1125 younger adults. A confirmatory factor analysis (CFA) was conducted, and results indicated the PTPI has acceptable model fit. Further quantitative data analysis revealed that older adults had lower feelings of satisfaction, perceptions of inclusivity, and overall PTPI score with products they disliked than younger adults, and higher feelings of satisfaction and overall PTPI score with products they liked. Older adults were more likely to think the product was made for them because it was easy to use, while younger adults were more likely to state that it met their needs and fit with their lifestyle.

Hospital telemetry, initially developed for monitoring astronauts, has evolved significantly to become a staple in modern medical practice. This paper explores the cognitive demands placed on healthcare professionals by advanced telemetry systems and identifies potential risks associated with their use in hospital settings. Through an examination of cognitive processes such as attention, vigilance, multi-tasking, mental workload, memory, and situation awareness, this study highlights the challenges faced by healthcare professionals in managing the complex streams of data generated by telemetry devices. The paper portrays a table with these cognitive factors, their influences on the task of hospital telemetry monitoring, and proposed solutions. It emphasizes the need for aligning telemetry system design with human cognitive strengths and limitations to enhance patient monitoring and improve care outcomes. By integrating advanced decision support systems, ergonomic designs, and targeted training programs, this paper suggests strategies to mitigate cognitive overload and enhance the effectiveness of telemetry in hospital settings. The ultimate goal is to provide guidance for future research and the development of interventions that support telemetry staff, thereby improving the safety and efficacy of patient care.

There has been a recent rise in interest and funding in women's professional sports, specifically the WNBA. This has created an increased pool of talented players that coaches have to sort through to identify whom they want on their teams. Prior literature has researched the relationship between talent and the quantity of talented players on NBA teams, but this has not been evaluated within the WNBA. As such, the present works investigate this relationship through a regression analysis operationalizing talent as either plus-minus ratings (PM) or player impact estimates (PIE) and team performance as regular season wins. Results from this analysis indicate that teams with a higher quantity of top talent tend to win more games during the regular season when utilizing both PIE ( F (2, 117) = 120.37, p < .05) and PM as a measure of talent ( F (2, 117) = 120.37, p < .001). These findings create novel insights to assist coaches in their decision-making for player selections.

INTRODUCTION: This study aimed to update and reinforce previous research on helicopter emergency medical service accidents in the United States. By investigating predictors of fatalities after helicopter emergency medical service crashes through the application of machine learning techniques, we updated existing data sets and sought to uncover patterns that traditional analysis might not reveal. METHODS: Using the National Transportation Safety Board database, the authors analyzed a dataset of 267 helicopter emergency medical service accidents between 1991–2022. We first calculated fatalities odds ratios for each condition. We then plotted geospatial locations of all reported accidents. Finally, we used XGBoost regression to understand the most important features contributing to fatality after an accident. RESULTS: The findings reaffirm previous research and identify significant predictors of fatalities in helicopter emergency medical service accidents. Key factors such as adverse flight conditions (weather), the absence of a copilot, and postcrash fires are highlighted as critical to understanding and mitigating risks of fatality. DISCUSSION: These findings emphasize the utility of machine learning in extracting meaningful insights from accident data, suggesting that such techniques offer a more nuanced understanding of the conditions leading to fatalities. It points out the potential of these methods to not only enhance aviation safety but also to be applied across other sectors. We conclude by underlining the significant potential of techniques like XGBoost in advancing safety measures within helicopter emergency medical service and possibly other aviation sectors. Korentsides J, Keebler JR, Berezovski M, Chaparro A. Factors contributing to fatalities in helicopter emergency medical service accidents . Aerosp Med Hum Perform. 2025; 96(2):111–115.