Deriving personalized HRM practices using employee-AI collaboration: A shared mental model approach.

In many regions of Europe and beyond, health training institutions have distinct governance structures for care, research, and education. Developing a shared culture therefore represents a real challenge. Some initiatives across the Atlantic promote this culture by offering innovative approaches to training and collaboration. An interinstitutional partnership between Lausanne and Quebec enhances coordinated practices among institutions, mobilizing thinking skills as a pedagogical tool to teach interprofessionalism and to develop a shared language and culture. By integrating collaborative clinical reasoning, students and health professionals develop a shared mental model that can be transferred to their daily practice in order to improve collaboration.

ABSTRACT Objective Primary care is the largest platform for continuous, person-centred, relationship-based care in the United States of America. Integrating mental and behavioural health into primary care may help meet demand for clients needing this care and relies on interdisciplinary primary care team members functioning well. Method This qualitative evaluation of an integrated behavioural health program in seven family medicine clinics gathered in-depth, semi-structured interviews from 46 practice members – physician medical directors, practice administrators, psychologists, and psychiatrists. The ‘Big Five’ of teams framework from Salas, et al. provided an analytical framework to deeply examine components of effective teamwork: team leadership, mutual performance monitoring, backup behaviour, adaptability, and team orientation. A team-based analysis approach used grounded theory techniques for coding with a priori segmenting codes and deductive coding. Results Practice teams benefitted from visible and supportive team leadership, capable mutual performance monitoring, skilful backup behaviours, adaptability, and strong and cooperative team orientation. Teams further benefited from supporting mechanisms: a shared overall mental model of integrated behavioural health, mutual trust among team members, and effective closed-loop communications. Discussion The ‘Big Five’ in teams framework illuminated areas of team functioning useful for this evaluation and may be useful when examining other interprofessional healthcare teams.

Checklist- and algorithm-based simulation training produce comparable improvements in teamwork-related shared mental models: a cluster-randomized study in trauma resuscitation teams.

OCCUPATIONAL APPLICATIONS This study explores the relationship between interpersonal physiological synchrony and shared mental models (SMMs) in collaborative mixed reality (MR) manufacturing environments. By integrating heart rate (HR) and heart rate variability (HRV) data with team-level SMM assessments, we show that real-time physiological alignment serves as a meaningful correlate of team cognition during virtual collaboration. Physiological synchrony measures also distinguished high- and low-performing teams, indicating potential value for monitoring team effectiveness. These findings have direct implications for occupational training and performance monitoring in high-stakes manufacturing contexts. Physiological synchrony offers practitioners a non-intrusive, real-time indicator of interaction quality complementing conventional approaches. Our findings further suggest that triadic teams exhibited stronger physiological alignment and more robust SMMs than dyads, informing decisions about team structuring and collaborative task design. Incorporating physiological monitoring into MR training platforms can identify breakdowns in shared understanding and provide adaptive feedback, enabling targeted interventions to improve coordination, safety, and productivity.

The Liaison Committee on Medical Education (LCME) accreditation process presents significant challenges to medical schools in the current educational landscape. Medical schools invest substantial resources in accreditation preparation, often funding consultants and mock site visits, often diverting resources from institutional priorities. Current self-evaluation resources, while meeting Department of Education minimum requirements, inadequately prepare both medical school staff and site surveyors for the complex evaluation process. The accreditation system lacks transparent evaluation methodologies that would ensure consistent application of evaluations based on institutional contexts. Site surveyors and medical school staff require specialized competencies that are not adequately addressed through existing preparation methods. This has led to the perception that application of standards is inconsistent and results in increased financial burden on medical schools. This commentary proposes implementing rubrics and frame-of-reference training to improve the LCME accreditation process. Drawing from established assessment and program evaluation methodologies that employ transparent standard-setting processes, frame-of-reference training can establish shared mental models for how standards and elements are rated, thereby improving consistency in their application. This approach would provide greater guidance to both medical schools and site surveyors throughout the evaluation process. This adoption could redirect medical school funding from accreditation consultants toward institutional priorities and educational improvements. This systematic approach has the potential to enhance the consistency of accreditation decisions while reducing the financial burden on institutions. Future research should examine outcomes of this proposed framework to evaluate its effectiveness in improving the accreditation process and institutional resource allocation.

Introduction: Intubation in the pediatric intensive care unit (PICU) is a high-risk procedure requiring rapid coordination, clear communication, and reliable access to equipment. Our project aimed to promote utilization of an intubation checklist to reduce adverse events associated with intubations over a 2-month period in summer 2025. We predicted that the process would increase intubation safety and success without adding time to intubation. Methods: A multidisciplinary team of PICU fellows, attendings, respiratory therapists (RTs), and nurses (RNs) developed a checklist encompassing personnel roles, safety considerations, room setup, and required equipment. During implementation, team champions were present in the unit to ensure fidelity to the process and evaluate barriers. Checklists were placed in each patient room and on airway carts. Procedure and resuscitation records were reviewed to confirm checklist use as a process measure, adverse events, and duration from time out to intubation. Adverse events (AEs) were categorized as hypoxemia (oxygen desaturation < 80%), non-severe (mainstem intubation, emesis without aspiration), severe (emesis with aspiration), and multiple attempts. A post-intervention survey was disseminated to all PICU team members to assess utility of the checklist, as well as perceived impacts on patient care. Results: The checklist was utilized in 85% of intubations during the study period; the most common barrier was the procedure being too emergent. The rate of intubations complicated by AEs decreased in number and severity following checklist implementation without prolonging time to intubation. Survey respondents reported that the checklist improved safety, supported a shared mental model, decreased cognitive burden, and helped identify equipment gaps. No participants felt it detracted from autonomy or was unnecessary. Ninety percent of respondents supported continued checklist use. Conclusions: Implementation of a standardized intubation checklist was feasible and well-received in the PICU. It reduced adverse events. Users reported improved safety, team coordination, and reduced cognitive load. These findings support a standardized intubation process involving checklist use to build teamwork and promote a shared mental model during a complex event.

Trauma care demands rapid decision-making under uncertainty and time pressure, where maintaining situational awareness becomes challenging. Artificial intelligence offers potential to augment clinical judgment by processing complex physiological signals, predicting clinical trajectory, and enhancing shared mental models across care phases. This review examines recent developments in artificial intelligence-enabled triage and decision support across prehospital, emergency department, and mass-casualty settings. Machine learning models now predict lifesaving intervention needs from early prehospital data with performance comparable to expert judgment. Deep learning systems detect intracranial hemorrhage in real time, while dynamic risk scores continuously update mortality predictions during resuscitation. Artificial intelligence-enabled worklist triage accelerates critical imaging interpretation and procedural team activation. In mass-casualty scenarios, wearable sensors with automated triage algorithms maintain live patient classification, while drone-based computer vision enables contactless vital sign assessment. However, prospective evaluations reveal variable translation to measurable clinical benefit, requiring careful attention to usability, workflow integration, and ongoing calibration monitoring. Artificial intelligence has the potential to strengthen trauma triage by supporting cognitive work under pressure, but realizing this benefit requires rigorous evaluation extending beyond discrimination metrics to include calibration, clinical utility analysis, and human factors assessment. The most meaningful role for artificial intelligence is augmentation rather than automation, protecting clinicians' cognitive capacity for sound judgment.

ABSTRACT Effective intra-team communication is fundamental for coordination and success in team sports. This paper critically reviews the existing conceptualizations of intra-team communication, identifying gaps in representational approaches within sport psychology. Representational theories rely on shared mental models to achieve coordinated actions, which limited practical impact due to their static nature and the assumption that all players share identical cognitive representations. This, in turn, constrains the ability to flexibly adapt to unpredictable and dynamic in-game situations. As an alternative, we propose an ecological (non-representational) perspective that conceptualizes intra-team communication as serving the collective attention to information specifying the shared affordances that support ongoing team actions. This perspective emphasizes the dynamic nature of communication, utilizing methods from the dynamical systems approach to explore these interactions. We highlight the essential role of communication in coordinating team actions and propose a research agenda for further exploration in this field, particularly in football.

With the rapid advancement of artificial intelligence (AI), especially the widespread adoption of large language models (e.g., ChatGPT), the role of AI in team collaboration is undergoing profound transformation. Prior research suggests that improvements in team effectiveness largely depend on the knowledge complementarity among team members and the development of shared mental models. However, the underlying mechanisms through which these factors operate in human–AI teams remain insufficiently understood. Grounded in shared mental model theory and transactive memory system theory and informed by a cognitive complementarity framework within human–AI teams, this study develops a cognitive mechanism model for team collaboration. A 2 (team type: human–human vs. human–AI) × 2 (knowledge complementarity: high vs. low) × 2 (team climate: positive vs. negative) between-subjects experimental design was used to examine how knowledge complementarity affects team effectiveness and member satisfaction via shared mental models, with a focus on moderating effects. A total of 128 participants were recruited to complete a collaborative promotional writing task with the theme “modern communication of classical Western artistic ideals.” The participants co-created a promotional text (within 300 words) with either a human teammate or an AI system powered by the DeepSeek architecture. The results indicate that in human–AI teams, high levels of knowledge complementarity significantly enhance both team effectiveness and member satisfaction. Shared mental models serve as a mediating mechanism, and a positive team climate further amplifies these effects. These findings contribute to a deeper understanding of cognitive structures in human–AI interactions and offer theoretical and practical guidance for the deployment of large language models in collaborative tasks.

Perinatal transition is uniquely complex in newborns with critical congenital heart disease (CCHD), whose cardiopulmonary physiology often diverges from the standard neonatal resuscitation framework. We developed lesion-specific clinical practice guidelines (CPGs) for delivery room (DR) management of six high-risk CCHD diagnoses - hypoplastic left heart syndrome, dextro- transposition of the great arteries, Ebstein anomaly, congenital complete heart block, total anomalous pulmonary venous return, and tetralogy of Fallot with absent pulmonary valve. Developed collaboratively between neonatologists, cardiologists, and cardiac intensivists, these guidelines standardize DR stabilization for high-risk, low frequency events, promote a shared mental model among multidisciplinary teams, and provide structured escalation thresholds for oxygen, respiratory support, intubation, and medication use. Beyond clinical standardization, these CPGs enhance provider education, team preparedness, and family counseling. They offer a scalable framework adaptable to centers with or without on-site cardiac care, bridging physiologic principles with practical implementation.

Objective: The present study aimed to model and predict family decision-making effectiveness using machine learning techniques by examining the roles of shared mental models, power dynamics, and negotiation styles. Methods and Materials: This descriptive–correlational study with a predictive modeling approach was conducted on 412 married adults in Thailand selected through multistage cluster sampling. Data were collected using standardized instruments assessing shared mental models, family power dynamics, and negotiation styles, all of which demonstrated acceptable validity and reliability. Data analysis was performed using a hybrid approach combining statistical analysis in IBM SPSS Statistics (version 27) and machine learning modeling in Python with scikit-learn and TensorFlow. Predictive models including Random Forest, Support Vector Machine, Gradient Boosting, and Multilayer Perceptron were trained and evaluated using cross-validation, with performance assessed through accuracy, precision, recall, F1-score, and AUC-ROC metrics. Findings: The results indicated that shared mental models had a significant positive effect on family decision-making effectiveness, while power dynamics showed a significant negative effect. Integrative negotiation style significantly and positively predicted decision-making effectiveness, whereas dominating style had a significant negative association. Machine learning analysis revealed that the Gradient Boosting model achieved the highest predictive performance (accuracy = 0.89, AUC = 0.92), outperforming other models. Feature importance analysis demonstrated that shared mental models were the strongest predictor, followed by integrative negotiation style and power dynamics, confirming the relative contribution of cognitive, behavioral, and structural variables in predicting decision outcomes. Conclusion: The findings highlight the central role of cognitive alignment and collaborative negotiation in enhancing family decision-making effectiveness, while unequal power structures undermine optimal outcomes. The integration of machine learning approaches provides a robust and nuanced framework for modeling complex family processes, offering both theoretical advancement and practical implications for improving relational functioning.

The purpose of this paper is to study the influence of the shared mental model (SMM) on team innovation performance (TIP) by exploring the moderating effect of emotional infection (EI).The Credamo platform was used to conduct a questionnaire survey on Chinese teams participating in collaborative innovation projects, and 198 valid samples were collected for the research. The authors apply confirmatory factor analysis to test the reliability and validity of the constructs, structural ordinary least squares hierarchical regression model to verify the hypothesis. The results show that both the collaborative shared mind model and the task-based shared mind model have significant positive effects on team innovation performance. Positive emotion infection can regulate the relationship between the collaborative shared mind model and team innovation performance, but has no significant moderating effect on the relationship between the task-based shared mind model and team innovation performance. Negative emotional contagion not only negatively moderates the relationship between the collaborative shared mental model and team innovation performance, but also negatively moderates the relationship between the task-based shared mental model and team innovation performance.

Human-robot teaming can benefit many domains. Teams with sufficient team situation awareness may better accomplish their goals, but team situation awareness can be challenging to develop and maintain. We interpret team situation awareness as the team’s collective understanding of the whole situation at a given time. In order to determine how team situation awareness can be developed and maintained in a human-robot team, we conducted a between-subjects experiment to investigate how shared mental models and communication impact team situation awareness, and how team situation awareness relates to performance. Results from 48 subjects showed the impact of shared mental models is relative to communication. A high shared mental model improved team situation awareness and performance efficiency when there was little communication, while the level of shared mental model was inconsequential when high communication was provided. In addition, team situation awareness was positively related to performance efficiency. The findings indicate that team situation awareness can be achieved through either high communication or a high shared mental model under limited communication, which consequently allows for improved performance.

Background The relationship between nursing teamwork and patient falls is a crucial area of study in healthcare, as effective teamwork has been consistently linked to improved patient outcomes and safety. Objective To examine the relationship between nursing teamwork and patient falls in Jordanian hospitals. Methods A descriptive correlational research design was conducted, involving 375 registered nurses from four hospitals representing three health sectors in Jordan. The participants completed the “Nursing Teamwork Survey,” which contains 33 questions divided into five subscales, measured on a five‐point Likert scale. Additionally, there is one question regarding the nurses’ experiences with patient falls in their units, which is measured on a seven‐point Likert scale. The data collection process took place from September to October 2024. Results The total mean nursing teamwork score was 116.36 (SD = 35.2), while the frequency of patient falls mean reported by nurses was 0.54 (SD = 0.67). The shared mental model subscale received the highest score (M = 3.58, SD = 1.0) among all nursing teamwork subscales, while the team orientation subscale received the lowest (M = 3.45, SD = 1.0). A weak negative significant relationship was found between nursing teamwork and patient falls ( r s = −0.248, p &lt; 0.001). Although the correlation is weak, it suggests that improving nursing teamwork may positively impact patient safety by potentially reducing the incidence of patient falls. Also, weak negative significant correlation was showed between patient falls and the following teamwork subscales: Shared mental model ( r s = −0.260, p &lt; 0.001), Backup ( r s = −0.242, p &lt; 0.001), Team leadership ( r s = −0.267, p &lt; 0.001), Mutual trust ( r s = −0.223, p &lt; 0.001), and Team orientation ( r s = −0.223, p &lt; 0.001). Conclusion Enhancing nursing teamwork is vital for improving patient outcomes and ensuring safety within healthcare settings. The moderate level of teamwork identified highlights the need for targeted interventions, such as training programs focused on communication and collaboration, to foster a more cohesive nursing staff. The observed correlation between teamwork and patient falls suggests that a stronger emphasis on teamwork can directly impact patient safety, emphasizing the importance of creating an environment that encourages collaboration. Additionally, the development and implementation of policies that address specific areas of teamwork, along with ongoing assessments of teamwork dynamics, are essential for sustained improvement. By prioritizing these efforts, healthcare organizations can create a culture of safety and teamwork that ultimately benefits both patients and healthcare providers.

Background: High-acuity patient crises, particularly those involving complex surgical pathologies like bariatric or endocrine emergencies, demand seamless coordination across pre-hospital, emergency, and surgical teams. Traditional siloed training often fails to prepare these disparate groups for the intense collaboration required, leading to breakdowns in communication, role confusion, and delayed decision-making. Aim: This narrative review aims to synthesize current evidence on multidisciplinary simulation (MDS) as a pedagogical tool to build shared mental models, enhance interprofessional communication, and clarify role responsibilities among EMS, Emergency Medicine, Nursing, and General Surgery teams during time-sensitive events. Methods: A structured literature search was conducted across PubMed, CINAHL, Scopus, and Web of Science (2010-2024) using keywords related to simulation, interprofessional education, teamwork, and the specified clinical domains. Included literature focused on simulation involving at least three of the target disciplines in high-acuity settings. Results: The analysis reveals that MDS effectively improves non-technical skills, including situational awareness, closed-loop communication, and leadership. Scenario design principles emphasizing realism, cognitive fidelity, and structured debriefing are critical. Successful implementations, such as "Field-to-OR" or "Clinic-to-ICU" pathways for surgical complications, demonstrate improved clinical outcomes, including reduced time-to-intervention and enhanced team psychological safety. Conclusion: Multidisciplinary simulation is a powerful, evidence-based strategy for constructing the shared cognitive frames necessary for managing complex patient crises. To bridge persistent gaps in care continuity, healthcare institutions must prioritize and institutionalize immersive, cross-disciplinary simulation training that mirrors the high-stakes, interdependent nature of real-world emergency care.

Call for Proposals: Special Issue on “Humanomics, Systems Thinking, and AI Shared Mental Models” (Updated December 6, 2025)

As virtual teams become increasingly prevalent in today’s globalized and technology-driven environments, shared mental models (SMMs) are crucial for enhancing collaboration and performance. We conducted a systematic review of existing literature (2004–2023) to understand how SMMs have been investigated in virtual settings and methods for measuring them. Studies were coded based on information and communication technology (ICT) tools, team characteristics, types of SMMs, and the Input-Process-Output (I-P-O) framework. Findings show diverse SMMs impact team functioning, but measurement methods lack standardization, highlighting the need for innovative approaches. This study underscores gaps in the literature and suggests avenues for future exploration.

Target-based care (TBC) uses institutional data to create a shared mental model of anticipated postoperative milestones. This study evaluated the impact of a clinical effectiveness strategy, combining TBC with a clinical pathway and decision support, on outcomes in patients undergoing pectus excavatum repair. This was a prospective study at a quaternary children's hospital between 2022 and 2024. Patients undergoing repair from 2018 to 2021 were historical controls. Target-based care included displaying bedside targets for length of stay (LOS) (outcome metric), Foley catheter and patient-controlled analgesia (PCA) discontinuation (process metrics), and a multidisciplinary evidence-based clinical pathway with an electronic order set. Overall, 91 patients were included: 52 preintervention and 39 postintervention. Median LOS decreased from 3 to 1.8 days (95% confidence interval [CI] 0.8-1.6, p < .05). The proportion of patients who met the LOS target of 2 days increased from 44.2% to 91.8% (p < .05). The mean time to PCA discontinuation decreased from 1.6 to 0.8 days (95% CI 34.8-118.7, p < .05). The time to Foley catheter removal diminished from 22.2 to 17.1 hour (95% CI 0.6-9.6, p < .05). A data-driven TBC with a clinical pathway had an immediate and sustained impact on patient care. Length of stay, PCA discontinuation, and time to Foley discontinuation decreased after TBC.

The impact of personality traits on scrum team effectiveness: Insights from Vietnamese software development companies