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Three-dimensional (3D)printed skulls for neurosurgical training are increasingly being used due to the widespread access to 3D printing technology, their low cost and accuracy, as well as limitations and ethical concerns associated with using human cadavers. However, little is known about the risks of airborne particles or volatile organic compounds (VOCs) released while drilling into 3D-printed plastic models. The aim of this study is to assess the level of exposure to airborne contaminants while burr hole drilling.

3D-printed skull samples were produced using three different materials (polyethylene terephthalate glycol [PETG], white resin and Bone^STN) across three different 3D print processes (fused filament fabrication, stereolithography [SLA] and material jetting). A neurosurgeon performed extended burr hole drilling for 10 min on each sample. Spot measurements of particulate matter (PM2.5 and PM10) were recorded, and air samples were analysed for approximately 90 VOCs.

The particulate matter for PETG was found to be below the threshold value for respirable particles. However, the particulate matter for white resin and Bone^STN was found to be above the threshold value at PM10, which could be harmful for long periods of exposure without personal protective equipment (PPE). The VOC measurements for all materials were found to be below safety thresholds, and therefore not harmful.

To the best of the authors’ knowledge, this is the first study to evaluate the safety of 3D-printed materials for burr hole surgical drilling. It recommends PETG as a safe material requiring minimal respiratory control measures, whereas resin-based materials will require safety controls to deal with airborne particles.

This article aims to address the need for a more structured partnership between civilian and military healthcare, particularly in the context of cross-border threats in the EU. While both systems are driven by the same goal of providing high-quality healthcare services and achieving optimal patient outcomes, they operate under different national approaches and resources.

Two recent crises are presented as examples that highlight the necessity of cooperation between civilian and military medical systems. The Covid-19 Pandemic and the Ukrainian Conflict are described based on the experience gathered by the author as a member of the NATO Centre of Excellence for Military Medicine and form the base to shape a broader perspective on the future of civil-military interaction in healthcare at the European Union level.

The ability to deliver coordinated responses during crises depend on the level of interoperability, preparation and mutual understanding. To improve synergies, a structured partnership should be established, prioritizing common standards of care and shared best practices. Integrating military and civilian healthcare pathways can be especially beneficial in situations where patients are moved from the point of injury or sickness across different military and civilian structures to receive the most appropriate treatment and rehabilitation for their conditions.

The relationship between military and civilian healthcare systems is often discussed at multinational level, but a clear focus is lacking concerning their shared mission, distinct functions and potential for cross-border collaboration.

The future increase of chronic diseases in the world requires new challenges in the health domain to improve patients' care from the point of view of the organizational processes, clinical pathways and technological solutions of digital health. For this reason, the present paper aims to focus on the study and application of well-known clinical practices and efficient organizational approaches through an innovative model (TALIsMAn) to support new care process redesign and digitalization for chronic patients.

In addition to specific clinical models employed to manage chronic conditions such as the Population Health Management and Chronic Care Model, we introduce a Business Process Management methodology implementation supported by a set of e-health technologies, in order to manage Care Pathways (CPs) digitalization and procedures improvement.

This study shows that telemedicine services with advanced devices and technologies are not enough to provide significant changes in the healthcare sector if other key aspects such as health processes, organizational systems, interactions between actors and responsibilities are not considered and improved. Therefore, new clinical models and organizational approaches are necessary together with a deep technological change, otherwise, theoretical benefits given by telemedicine services, which often employ advanced Information and Communication Technology (ICT) systems and devices, may not be translated into effective enhancements. They are obtained not only through the implementation of single telemedicine services, but integrating them in a wider digital ecosystem, where clinicians are supported in different clinical steps they have to perform.

The present work defines a novel methodological framework based on organizational, clinical and technological innovation, in order to redesign the territorial care for people with chronic diseases. This innovative ecosystem applied in the Italian research project TALIsMAn is based on the concept of a continuum of care and digitalization of CPs supported by Business Process Management System and telemedicine services. The main goal is to organize the different socio-medical activities in a unique and integrated IT system that should be sustainable, scalable and replicable.

Aboriginal and Torres Strait Islander (Aboriginal) people are overrepresented in Australian prisons, where they experience complex health needs. A model of care was designed to respond to the broad needs of the Aboriginal prisoner population within the nine adult prisons across South Australia. The purpose of this paper is to describe the methods and findings of the Model of Care for Aboriginal and Torres Strait Islander Prisoner Health and Wellbeing for South Australia.

The project used a qualitative mixed-method approach, including a rapid review of relevant literature, stakeholder consultations and key stakeholder workshop. The project was overseen by a Stakeholder Reference Group, which met monthly to ensure that the specific needs of project partners, stakeholders and Aboriginal communities were appropriately incorporated into the planning and management of the project and to facilitate access to relevant information and key informants.

The model of care for Aboriginal prisoner health and wellbeing is designed to be holistic, person-centred and underpinned by the provision of culturally appropriate care. It recognises that Aboriginal prisoners are members of communities both inside and outside of prison. It notes the unique needs of remanded and sentenced prisoners and differing needs by gender.

Supporting the health and wellbeing of Indigenous prison populations can improve health outcomes, community health and reduce recidivism.

Only one other model of care for Aboriginal prisoner health exists in Australia, an Aboriginal Community Controlled Health Organisation-initiated in-reach model of care in one prison in one jurisdiction. The South Australian model of care presents principles that are applicable across all jurisdictions and provides a framework that could be adapted to support Indigenous peoples in diverse prison settings.

A good workplace well-being (WWB) has many positive impacts on individuals and organizations. Prior studies indicate that physical, psychological and social well-being factors positively influence WWB. Nevertheless, it is essential to acknowledge that these factors may exhibit variations across different regions, cultural contexts and workplace environments. Therefore, this study aims to explore and validate the relationships between physical, psychological and social well-being factors and WWB at construction sites across different regions, using Malaysia and India as case studies.

A conceptual model was proposed between physical, psychological and social well-being factors and WWB at construction sites. Then, a questionnaire survey was developed based on the proposed model and distributed to construction industry practitioners in both countries. In total, 316 responses were collected and analyzed using confirmatory factor analysis and multigroup analysis.

The analyses indicate that the proposed model on physical, psychological and social well-being factors and WWB at construction sites is valid. Also, the model has no significant differences between the two countries. Thus, the findings show that the physical, psychological and social well-being factors are similarly affecting WWB at construction sites in both countries.

The originality of the study lies in its holistic and cross-regional examination of WWB at construction sites. The insights gained from this study provide evidence for promoting good health and well-being in the construction industry. Moreover, this study seeks to provide insights that transcend geographical boundaries, offering valuable implications for promoting WWB practices in construction projects worldwide.

Computer vision and deep learning (DL) methods have been investigated for personal protective equipment (PPE) monitoring and detection for construction workers’ safety. However, it is still challenging to implement automated safety monitoring methods in near real time or in a time-efficient manner in real construction practices. Therefore, this study developed a novel solution to enhance the time efficiency to achieve near-real-time safety glove detection and meanwhile preserve data privacy.

The developed method comprises two primary components: (1) transfer learning methods to detect safety gloves and (2) edge computing to improve time efficiency and data privacy. To compare the developed edge computing-based method with the currently widely used cloud computing-based methods, a comprehensive comparative analysis was conducted from both the implementation and theory perspectives, providing insights into the developed approach’s performance.

Three DL models achieved mean average precision (mAP) scores ranging from 74.92% to 84.31% for safety glove detection. The other two methods by combining object detection and classification achieved mAP as 89.91% for hand detection and 100% for glove classification. From both implementation and theory perspectives, the edge computing-based method detected gloves faster than the cloud computing-based method. The edge computing-based method achieved a detection latency of 36%–68% shorter than the cloud computing-based method in the implementation perspective. The findings highlight edge computing’s potential for near-real-time detection with improved data privacy.

This study implemented and evaluated DL-based safety monitoring methods on different computing infrastructures to investigate their time efficiency. This study contributes to existing knowledge by demonstrating how edge computing can be used with DL models (without sacrificing their performance) to improve PPE-glove monitoring in a time-efficient manner as well as maintain data privacy.

Developing nursing leadership has become a key policy priority to achieve universal health coverage. This study aims to explore the current status, developing trends and research frontiers in the field of nursing leadership.

In total, 1,137 articles and reviews on nursing leadership from 1985 to 2022 were retrieved from the Web of Science Core Collection database. Trends of publications, journals, countries/regions, institutions, documents and keywords were visualized and analyzed using Microsoft Excel and CiteSpace software.

Nursing leadership research showed an overall increase in number despite slight fluctuations in annual publications. The USA was the leading country in nursing leadership research, and the University of Alberta was the most productive institution. The Journal of Nursing Management was the most widely published journal that focused on nursing leadership, followed by the Journal of Nursing Administration. Keyword analysis showed that the main research hotspots of nursing leadership are improvement, practice and impact of nursing leadership.

This article summarizes the current state and frontiers of nursing leadership for researchers, managers and policy makers, as well as follow-up, development and implementation of nursing leadership. More research is needed that focuses on the improvement, practice and impact of nursing leadership, which are cyclical, complementary and mutually reinforcing. Longitudinal and intervention studies of nursing leadership, especially on patient prognosis, are also particularly needed.

The present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.

The rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

This study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support.

A systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Scopus, PubMed and Google Scholar. The review identified peer-reviewed papers published in the English Language from January 2010 to April 2023, targeting well-defined implementation of EHRS with decision-support capabilities in healthcare. To comprehensively address the research question, we ensured that all potential sources of evidence were considered, and quantitative and qualitative studies reporting primary data and systematic review studies that directly addressed the research question were included in the review. By including these studies in our analysis, we aimed to provide a more thorough and reliable evaluation of the available evidence.

The findings suggest that the success of EHRS implementation is determined by organizational and human factors rather than technical factors alone. Successful implementation is dependent on a suitable implementation framework and management of EHRS. The review identified the capabilities of Clinical Decision Support (CDS) tools as essential in the effectiveness of EHRS in supporting decision-making.

This study contributes to the existing literature on EHRS implementation models and identifies successful models for decision support. The findings can inform future implementations and guide decision-making in healthcare facilities.

This study aims to explore the perception of students from conflicted groups studying nursing together regarding the intergroup encounter. Specifically, this study focused on Jewish and Arab students in a nursing undergraduate program at an Israeli college. This study focused on the association between two factors [students’ cultural intelligence (CQ) level and their satisfaction with the support provided by the college] and the degree of closeness or social interaction Arab and Jewish students experience.

Accordingly, 362 students completed three anonymous questionnaires during December 2022, focused on (1) CQ (2) satisfaction with the academic setting and (3) experience of intergroup social interaction.

The results revealed that (1) Arab students (minority group) demonstrated higher CQ than Jewish students, especially regarding awareness of cultural differences and motivation for intercultural encounters. (2) Arab students experienced the intergroup encounter with Jewish students as closer and warmer than Jewish students did. (3) Despite being a minority group in Israel, Arab students’ satisfaction with the college’s support was higher than that of Jewish students. (4) Satisfaction with the support provided by the college was the main factor associated with the sense of social interaction, having a higher correlation with it than the degree of CQ.

Fostering CQ through curricula alone is insufficient in shaping intergroup experiences of students studying together in a divided society. To encourage social interaction between students in academia in divided societies, educational institutions need to ensure their students’ sense of support is high.