Psychological safety is a key component of effective healthcare teams and may impact job satisfaction, commitment and change readiness among critical care nurses. The design of nurse-physician collaboration may influence psychological safety but has not been systematically studied in intensive care settings. To investigate how nurse autonomy, participation in ward rounds, quality of nurse-physician relations and team size relate to psychological safety in critical care nurses, and how psychological safety mediates statistical effects on job satisfaction, affective commitment and commitment to change. We performed a multicentre, cross-sectional, multi-informant survey study conducted between December 2013 and March 2015 using a convenience sample of 22 intensive care units (ICUs) in 19 German hospitals, including university, public and private hospitals. Organisational data were provided by ICU leaders. Validated scales were used to assess psychological safety, job satisfaction, affective commitment to unit and commitment to change (nurse survey), and nurse-physician collaboration (physician survey). An organisational questionnaire was developed to assess nurses' autonomy and structural factors. Linear regression and mediation analyses tested hypothesised associations at the unit level. Of 1216 invited nurses and 379 invited physicians, 600 and 217 participated, respectively. Higher nurse autonomy (0.51 [95% CI: 0.1, 0.91], p = 0.016), consistent participation in ward rounds (0.51 [0.11, 0.91], p = 0.014), better nurse-physician collaboration (0.59 [0.21, 0.97], p = 0.004) and lower number of nurses working in a unit (-0.55 [-0.94, -0.16], p = 0.008) were statistically significantly associated with higher psychological safety in linear regression analysis. Psychological safety mediated effects of all predictors on job satisfaction, affective commitment and commitment to change. Psychological safety among critical care nurses can be enhanced through interprofessional collaboration, nurse autonomy and smaller team structures. These findings inform organisational strategies to improve nurse wellbeing and retention, as well as organisational readiness to change. Fostering interprofessional collaboration and enhancing nurses' autonomy in critical care are key to enhancing psychological safety and thereby enhancing job satisfaction and retention. Increasing team size can have unintended negative consequences that need to be weighted against potential gains. German Clinical Trial Register: DRKS00005357.

Adolescents and young adults (AYAs) with advanced cancer navigate complex medical decisions amid evolving autonomy and family involvement. MyPref, a digital values-clarification tool using adaptive conjoint analysis, helps AYAs articulate care preferences and supports clinicians in initiating preference-based conversations. This study explored pediatric oncology clinicians' experiences with MyPref to identify barriers, facilitators, and strategies for clinical integration. We conducted a qualitative study using semi-structured interviews with attending physicians and nurse practitioners (N = 10) at a single academic pediatric center. Participants had received MyPref summary reports for at least one AYA enrolled in the MyPref Adapt Study. Interviews were audio-recorded and analyzed using conventional content analysis to identify themes related to clinical utility and implementation. Clinicians emphasized MyPref's potential to reveal patient priorities, strengthen communication, and normalize values-based dialogue. Implementation was limited by time constraints, uncertain actionability, and emotional discomfort-particularly during periods of prognostic ambiguity. Barriers reflected structural (workflow integration, competing demands) and relational factors (patient readiness, family involvement, clinician tolerance for uncertainty). Recommended strategies to enhance uptake included leadership endorsement, communication training, psychosocial collaboration, and iterative refinement of MyPref's design and delivery. Clinician insights underscore MyPref's potential to deepen connection and elevate AYAs' voices in advanced cancer care when thoughtfully integrated into practice. Addressing implementation challenges will require flexible timing, reflective spaces for clinicians to navigate uncertainty, and systems-level support to embed preference-based discussions into routine care. Broader success depends on a cultural shift that normalizes anticipatory dialogue and centers AYAs' personhood when navigating serious illness.

This paper presents a comprehensive bibliometric analysis of the literature on institutions and macroe conomic stability. Institutions are considered in a broad sense, encompassing political, legal, and economic institutions that play a critical role in shaping macroeconomic outcomes. Despite growing scholarly interest in this area, no bibliometric study has systematically examined the evolution of the field, its research hotspots, and its intellectual structure. A total of 235 peer-reviewed publications published between 2000 and 2024 were retrieved from the Web of Science database and analyzed using bibliometric tools, including VOSviewer (TM) and Bibliometrix (R). The analysis examines publication trends, leading authors, coun tries, journals, collaboration patterns, and co-citation networks. The findings reveal a growing degree of interdisciplinarity, with major contributions originating from political economy, development studies, and public finance. Thematic evolution analysis indicates a shift in research focus from governance and institu tional quality toward more recent topics such as economic resilience, fiscal policy credibility, and inflation targeting frameworks. This study contributes to the literature by identifying key research clusters, collabora tion patterns, and existing research gaps, including the relative underrepresentation of emerging economies and comparative institutional analyses. The findings provide valuable insights for policymakers, economists, and researchers interested in understanding how institutional arrangements influence macroeconomic stabil ity. The paper concludes by outlining directions for future research and highlighting the potential of cross regional institutional comparisons.

ABSTRACT In 2021, the Dutch Ministry of Education launched a financial incentive to promote virtual international student collaboration projects, known as Virtual Exchange or Collaborative Online International Learning (COIL). The scheme encouraged educational developers’ involvement in COIL design, although their role in international education is not well understood. This case study explores COIL collaboration among lecturers, an educational developer, and a student coach at a Dutch university of applied sciences. Findings reveal that while the educational developer improved constructive alignment within COIL practices, there is a lack of alignment with broader curriculum internationalisation. Moreover, the collaborative learning aspect in COIL was largely implicit; lecturers lacked familiarity with collaborative learning design and it was unclear whose responsibility it was to address this.

The rapid advancement of the artificial intelligence (AI) and Internet of Things has intensified the demand for intelligent gas sensors that not only deliver high sensing performance but also enable efficient and secure data processing. Conventional gas sensing systems, which rely on separate sensing, memory, and computing units, suffer from high energy consumption, significant latency, and inherent security risks. This review provides a systematic overview of recent progress in intelligent gas sensors based on two-dimensional (2D) materials, focusing on two key aspects: improving gas sensing performance through material engineering and enabling neuromorphic data processing. We examine three major categories of 2D materials-graphene and transition metal dichalcogenides (TMDs), MXenes, and porous frameworks [metal-organic frameworks (MOFs) and covalent organic frameworks (COFs)]-and discuss their synthesis methods as well as performance enhancement strategies such as defect engineering and surface functionalization. Additionally, we analyze advanced intelligent data processing techniques, including hardware circuit optimization, sensor-AI collaborative design, and the development of bioinspired olfactory and multimodal perception systems. Finally, we outline current challenges and future research directions in material design and neuromorphic computing strategies to support the evolution of next-generation intelligent gas sensing technologies.

Terminology and core components of co-design intervention in chronic pain management: An international Delphi study.

Drug-target interaction (DTI) prediction is critical for candidate compound screening and elucidation of mechanisms of action in drug discovery and repurposing. However, existing methods often rely on unimodal representations, global fusion, or shallow cross-modal fusion, making it difficult to adequately model the heterogeneous and fine-grained dependencies between drug structures and protein sequences. To address this issue, we propose CM-MTL-DTI, a DTI-oriented collaborative alignment framework, rather than a simple combination of auxiliary modules. The framework employs two independent one-dimensional convolutional neural networks as main-task encoders to extract backbone sequential representations from drug SMILES sequences and protein sequences, respectively, and introduces a GIN-based graph encoder to provide a complementary structural perspective for the drug modality. On this basis, we design an asymmetric bidirectional cross-modal attention mechanism to explicitly model direction-sensitive dependencies between drug substructures and protein residues. Meanwhile, three collaborative objectives─cross-modal masked reconstruction (XMR), graph-sequence consistency learning (GSC), and supervised contrastive learning (SupCon)─are introduced to achieve local semantic recovery, multiview semantic alignment within the drug modality, and discriminative enhancement of interaction representations, respectively. Experimental results on three benchmark data sets show that CM-MTL-DTI delivers stable and competitive performance under both standard and challenging settings, validating the effectiveness of the proposed DTI-oriented collaborative design.

ABSTRACT Rural transport systems typically separate passengers, freight, and parcels, leading to low vehicle utilization and high costs under sparse demand. Existing studies overlook how parcel handling at village stops affects passenger travel time and service coordination in rural demand-responsive transport (DRT). This study models the trade-off between operational cost and passenger inconvenience in rural passenger-freight integration, focusing on the interaction between efficiency and service performance. We formulate an operational decision model for coordinated passenger-parcel activities and apply a heuristic solution to assess service configurations. A case study in Yiyuan County, China, shows that collaborative service design reduces fleet requirements and total operating costs, while the increase in passenger travel time remains moderate. The results also identify conditions under which collaborative services offer greater performance advantages in rural transport systems.

The Crossroads in Spinal Cord Injury Treatment: Present and Future conference, held in Houston, Texas in January 2026, brought together a multidisciplinary group of clinicians, researchers, engineers, and individuals living with spinal cord injury (SCI) to examine future directions of SCI care and research. This narrative summarizes themes, emphasizing the need to align scientific innovation with implementation in clinical settings. The conference was structured around priorities from individuals with SCI, including upper limb function, bowel and bladder management, pain, independence, and psychosocial well-being. Across sessions, speakers highlighted gaps in translation into practice. Major topics included challenges in clinical trial design, advocacy efforts to address policy barriers and inequities, and emerging approaches in neuromodulation, surgical reconstruction, and rehabilitation technologies. Additional focus areas included under-researched domains such as neurogenic bowel and bladder, disparities in care for women, and the clinical impact of autonomic dysfunction. Psychosocial health, pain, mental health, and sexuality were emphasized as central to quality of life. Engineering discussions underscored interdisciplinary collaboration and user-centered design. Overall, the conference highlighted that the field of SCI is at a critical crossroads, where progress will depend on effective implementation, equitable access, and integration of lived experience into research and clinical care.

Although minimally invasive surgery is a standard surgical approach because of its proven benefits to patient outcomes, it imposes increased cognitive demands on surgeons because of limited visualization, lack of tactile feedback, and constraints in instrument manipulation. These challenges have been partially mitigated by robot-assisted surgery, while recent advances in artificial intelligence (AI) have established intraoperative AI as a key technology enabling real-time support of surgical perception, decision-making, and instrument control. Here, we review the key AI technologies used during the intraoperative phase of endo-laparoscopic and robotic surgery, including anatomical and lesion recognition, instrument detection and tracking, surgical phase and workflow analysis, real-time tissue characterization, image-guided and augmented navigation, AI-assisted instrument control, and multimodal event detection. We also discuss key clinical integration challenges and future research directions focused on foundation and self-supervised learning paradigms, and human-AI collaborative system design.

In the product conceptual design process, the designers' ideation is highly dynamic and open-ended. Although existing generative AI expands the design space, it struggles to synchronise with the designer's cognitive state and perform deep collaborative reasoning. To address this, this paper proposes a real-time dynamic graph construction and reasoning method. This method adopts the Requirements-Functions-Logical-Physical (RFLP) ontology as a structural backbone, utilising large language models (LLMs) to extract concepts and relationships to construct structured logical chains. In the graph reasoning phase, the LLM performs path backtracking and node expansion based on the graph's topology to provide suggestions that drive the collaborative evolution of design ideas. We implemented this real-time visualised dynamic graph reasoning method within an interactive whiteboard prototype. A user study involving 24 participants reveals that, compared to a chat-only AI baseline, the proposed structured approach significantly improves the engineering quality of the final design schemes while ensuring the interpretability and controllability of the human-AI collaborative experience.

Purpose The digitalisation of education, accelerated by the COVID-19 pandemic and further reshaped by the rapid adoption of artificial intelligence, has intensified both opportunities and structural challenges within educational systems. These transformations have widened gaps between educational technology (EdTech) companies and educational researchers, as well as between advanced and widening research systems. The study examines how cross-border and cross-sector researcher mobility, implemented through the Horizon Europe EdTech Talents project, contributes to the professional development of educational researchers beyond traditional academic performance indicators. Design/methodology/approach Adopting an exploratory collaborative qualitative research design, the study draws on empirical data collected within a multi-country European mobility program. Empirical data include reflections from project meetings, document analysis, and semi-structured focus group interviews involving academic researchers, university administrative staff, and EdTech company employees. Reflexive thematic analysis and collaborative data analysis were used to analyse empirical data. Findings The findings indicate that cross-sector and cross-border mobility supports professional development through experiential learning, the development of industry-relevant knowledge services, and the expansion of professional networks. The findings highlight that understanding of EdTech innovation processes and shifts in professional identity toward more applied and collaborative roles was increased Key challenges included translating academic expertise into industry-ready solutions, limited institutional recognition of secondment experiences, and the absence of structured tools for assessing professional development outcomes. Overall, the potential of structured mobility programs to foster brain circulation rather than brain drain, particularly in widening research systems. Practical implications The study offers actionable insights for policymakers and research organizations designing mobility schemes aimed at strengthening innovation ecosystems and promoting sustainable knowledge circulation. The findings underscore the need for structured assessment frameworks and institutional recognition mechanisms to support long-term career development and maximize the impact of cross-sector researcher mobility. Originality/value By focusing explicitly on professional development processes within a structured researcher mobility program, this study provides novel, collaborative and reflexive process-oriented insights into mobility-driven knowledge services that strengthen academia–industry collaboration and address disparities between advanced and widening countries.

ABSTRACT Participatory approaches considering diverse age groups are increasingly recognised as essential for inclusive urban landscape design. Yet, limited research explores how contextual urban features shape design preferences across these groups. To address this gap, this study engages children, young people, middle-aged adults, and older adults in a participatory model‑making co‑design process to examine how adjacent elements, such as schools, health facilities, commercial units, bus stops, and exposure to major roads, affect park design visions. The aim is to investigate how contextual factors influence collaborative park design preferences across different age groups. Findings reveal both shared and age-specific spatial strategies. Seating areas along the edge facing a healthcare centre were favoured across all groups, while young people envisioned an additional café located in a more secluded corner and older participants proposed a health station within the park deliberately distanced from the adjacent clinic. These insights illustrate how contextual urban elements influence users’ spatial imaginations and needs, reflected in two response types: measures addressing surroundings and features engaging adjacent uses. By drawing attention to diverse user groups, the study advocates for inclusive, user-informed, and context-aware landscape design approaches and highlights the potential of incorporating diverse user expectations to enrich urban park design.

This study examines pre-service English teachers’ lived experiences of using generative artificial intelligence (GenAI) for multimodal lesson planning. Conducted within a six-week, technology-enhanced pedagogy course in a postgraduate teacher education programme in Hong Kong, the study employed interpretive phenomenological analysis drawing on classroom observations, AI-generated artefacts, and reflective interviews. The findings portray a developmental trajectory in which participants advanced from tentative experimentation toward more confident and critical use of GenAI. Three themes were identified. First, ‘GenAI as a collaborative multimodal lesson design partner’ showed how participants orchestrated heterogeneous tools to create differentiated and multimodal resources. Second, ‘negotiating authorship and professional identity’ revealed ambivalence about originality and creative ownership but also a shift from template adoption to adaptive curation, contextualisation, and principled integration. Third, ‘navigating challenges and ethical boundaries’ highlighted how participants acted as cultural and factual mediators while modelling responsible and transparent AI use to foster critical AI literacy among learners. Framed by the PedAIComp framework, the results show movement from Awareness and Exploration to Integration and, in some practices, Expertise and emerging Leadership. While Innovation was not reached within the intervention, the study points to future opportunities for professional development and collaborative design research to cultivate Innovation competences.

BackgroundEffective clinical communication is essential for medical practice, with standardized patients (SPs) being a reliable standard training method despite resource limitations. While large language models (LLMs) show strong role-playing abilities, current virtual patients (VPs) based on single LLMs face fidelity and interaction challenges. Recent advances in multiagent frameworks, which have demonstrated considerable potential in handling complex tasks, offer a new perspective for creating VPs in medical education.ObjectiveThis study aimed to develop and evaluate a novel multiagent VP framework that simulates SPs through a collaborative agent design, thereby enhancing human-like fidelity and interaction performance in clinical communication training–oriented VP simulation.MethodsOur multiagent framework constructed 5 specialized subagents by simulating the functional partitioning of brain regions, collaboratively simulating the entire process, from case reception to interactive consultation scenarios, designed for medical students. To enhance the interaction performance of VPs, we incorporated retrieval-augmented technology, while deep character reasoning was used to improve response richness and realism. We evaluated the proposed framework through a 2-phase experiment in which the metrics of response quality, role-playing performance, interaction efficiency, information accumulation, and perceived educational utility were applied consistently: first, to compare different base models, and second, to benchmark the complete framework against a single-LLM baseline.ResultsThe multiagent framework outperformed single-LLM baselines across multiple evaluation settings, achieving high information accuracy and role-playing scores under standardized dialogue conditions. Specifically, the GPT-4o–based implementation achieved peak factual consistency of 0.769 (SD 0.04), while all configurations maintained >94% clinical accuracy. The Qwen3-32B–based framework achieved the lowest misleading rate of 1.28% (SD 1.20), compared to 4.72% (SD 1.53%) for single-LLM scoring. In assessments using standard dialogue scripts, the Qwen3-32B–based framework attained the highest role-playing competency score of 39.67 (SD 0.71) and received high expert praise. However, limited discriminative power against specific leading questions on low-quality inquiries indicated that while these findings specifically establish high fidelity under structured conditions, further adaptation is required for authentic student interactions. Interaction efficiency remained practical with acceptable latency (~3 s) based on Qwen3-32B while maintaining a stable information pace during multiturn dialogues. Furthermore, a preliminary exploration of factual consistency and role-playing ability across 5 clinical departments demonstrated potential scalability.ConclusionsThe multiagent framework offers a viable simulation of SPs through the coordinated interaction of multiple LLM-based agents. This approach enhances the performance of VP simulation, providing a customizable and scalable solution for medical communication training, without compromising patient confidentiality. The framework holds substantial potential for advancing medical education approaches.

Large language models (LLMs) are being deployed in clinical settings despite an underdeveloped evidence base regarding their real-world effectiveness. This study employed systematic evidence mapping to characterize outcome measures used in published studies and registered clinical trials (Jan 2022-Jun 2025) evaluating LLM performance. Analysis of 55 included studies revealed a predominance of human-AI collaborative designs (65.5%) for decision support and symptom management. LLM-only interventions focused on functional performance and operational or process impact outcomes (e.g., accuracy and time saving), whereas LLM-assisted interventions showed positive clinical effects, particularly in psychological health endpoints. Critical evidence gaps persist: diagnostic accuracy in randomized trials was notably lower and more variable (range 0.65-0.88) compared to non-randomized studies (typically ≥ 0.80); clinical efficiency impacts were inconsistent, and reporting quality was suboptimal (78.8% mean CONSORT-AI adherence), with critical omissions in handling data quality and performance errors. These findings indicate a heterogeneous and insufficient evidence landscape, necessitating standardized core outcome sets, mandatory use of specialized reporting guidelines, and robust clinical trials to ensure the safe integration of LLMs.

Distributionally robust truck-drone collaborative delivery network design for urban-rural warehouses: A normal-emergency perspective

The rapid advancement of artificial intelligence (AI) is reshaping human collaboration, making trust repair critical in human–AI teams. However, limited guidance exists on how to restore trust after AI-related violations. Drawing on social exchange theory and trust repair theory, this study examined the effects of trust repair strategies in team decision-making. We conducted a 3 × 2 between-subjects experiment with 134 valid participants, manipulating trust repair strategy (commitment, apology, no repair) and team type (human–AI vs. human–human) in an urban risk management task. Results showed that team type significantly affected team performance and job satisfaction, and that repair strategies moderated these effects. Perceived support and collective efficacy mediated, and jointly chain-mediated, the relationships among team type, repair strategy, and outcomes. Commitment-based repair produced the most favorable results, particularly in human–AI teams. These findings advance trust repair theory in human–AI collaboration and inform human-centered AI design.

ABSTRACT This study introduces a co-design model that repositions the interior architecture Graduation Studio as an adult-learning context. Bridging the theory of andragogy with approaches to foster collaborative learning, the model aims to reframe the studio as a space of collective inquiry where design and learning processes mutually inform and enrich one another. Within the scope of Graduation Studio, the co-design model enabled students to develop projects in pairs. After the 15-week studio process, students reflected on their experiences through open-ended surveys. Data from 24 students were analysed using a mixed-method approach: word-frequency analysis followed by qualitative content analysis. Four thematic domains emerged—Collaborative Design Practices, Operational Management, Studio Communication, Reflective and Transformative Learning—each represents a locus where assumptions about adult learning were traced. Findings reveal that fostering collaborative learning in an adult-learning environment enables students to recognise knowledge as purposeful action, exercise self-direction within shared responsibility, recontextualise and draw on prior experience, develop readiness for professional practice, approach learning as an ongoing reflective process, and derive motivation from relational learning. Graduation studio, as a critical threshold between education and professional practice, can better prepare students for this transition when structured in accordance with adult-learning principles.

Microneedles have achieved remarkable breakthroughs in the fields of transdermal drug delivery and minimally invasive diagnosis and treatment. Over the past decades, it has been remarkably improved by developing delivery and diagnostic strategies based on the microneedle (MN) platform and leveraging its characteristic of minimally invasive penetration through the stratum corneum barrier, the bioavailability of drugs and the accuracy of diagnosis and treatment, achieving efficient local therapy while reducing the systemic side effects of drugs. This article first explains and clarifies the paradigm shift in the evolution of microneedle technology from passive delivery tools to active intelligent systems. Subsequently, it systematically reviews the latest advances in the 4D collaborative design of material composition, geometric structure, payload, and functional intelligence for constructing advanced microneedle systems, including tunable bionic/composite matrices, complex structures enabling spatiotemporally programed drug release, loading of multiscale therapeutic agents, and intelligent functions integrating sensing, response, and feedback control. At the end of the paper, the core implementation obstacles and emerging opportunities faced by this technology in clinical translation and personalized medicine are prospectively discussed.