Search Results (6,236)

Background/Objectives: Temporal bone surgery in children is technically challenging due to their smaller anatomical structures, developmental differences, and the closer proximity of critical neurovascular structures. The limited availability of conventional training materials and pediatric cadaveric specimens has led to greater enthusiasm for simulation-based methods. The aim of this systematic review was to identify existing otologic simulation models and evaluate their anatomical accuracy, teaching effectiveness, and supporting evidence. Methods: In accordance with PRISMA guidelines, the PubMed, Embase, Scopus, and Cochrane Library databases were searched for studies reporting simulation tools for pediatric otologic surgery. Articles describing three-dimensional printed (3DP) models, virtual reality (VR) platforms, cadaver specimens, and animal models were included. Studies focusing on children and providing educational outcomes were selected. The extracted data were synthetized and analytically discussed. Results: Thirteen studies met the inclusion criteria: nine on 3DP models and four on VR environments. No research involving cadavers or animals was identified. 3DP models exhibited consistent anatomical accuracy and notable educational advantages. Five studies used surveys for their evaluations, and three relied on expert observer assessments. The studies including validation analyses showed a high correlation between printed models and computed tomography (CT) images. VR systems supported anatomical reconstruction and segmentation tasks, as well as guided simulation exercises. However, most of the research consisted of feasibility studies with limited participant groups. Conclusions: Simulation-based training with 3DP and VR models could be ethical and accurate methods for obtaining relevant skills in pediatric otologic surgery. The reviewed data suggest that these tools may be suitable as a first-line step within an integrated, multimodal training pathway prior to direct patient contact. Full article

Background: Effective communication improves patient satisfaction and reduces stress for both patients and physicians. Surveys consistently highlight the importance of strong communication skills among physicians, especially in oncologic settings. Yet, communication training is neither ubiquitous nor standardized in medical studies or residency, and physicians report that this task represents a burden for them. Given the limited data addressing the observations and expectations of patients with gynecologic malignancies when receiving bad news, this survey aimed to assess their perspective on this topic. Methods: We examined throughout an anonymous questionnaire how patients with gynecological and breast cancer experienced the delivery of bad news. Data were collected in Germany from July 2024 to September 2025. The questionnaire was available online and in paper form in four languages (German, English, Turkish, Arabic), with the purpose of recording culture-specific data. Results: A total of 249 patients completed the survey. Regarding the overall need for improvement in delivering bad news, 222 women (94.5%) declared that improvement was necessary, with 92 (39.1%) of them indicating that substantial improvement was required. While 67.9% of patients were content with the physician’s professional competence, 30.5% stated a lack of empathy, and 32.9% stated insufficient time for conversation. When comparing satisfied and dissatisfied patients, significant differences were observed across several aspects, such as consultation length, nonverbal communication, calmness of the setting, stress level after the conversation, and the offer to bring a trusted person or arrange a follow-up conversation. Conclusions: This patient survey highlights a persistent gap between patients’ expectations and physicians’ performance when it comes to delivering bad news. The findings underline the urgent need for the implementation of systematic training programs and structured communication protocols in gynecologic oncology. Full article

In the last few years, universities have increasingly implemented online learning environments, allowing students to study at their own pace. These environments utilize technological tools and implement methods to support training, deliver content, and promote the acquisition of new knowledge and skills. As an example of these technologies, eye tracking has emerged as a powerful tool for studying visual attention, cognitive processes, and learning behaviors. The main aim of this study is to provide a scoping review of recent eye-tracking research across diverse learner populations, ranging from K-12 students to university-level learners and educators. The present study examined recent advances in eye-tracking technologies, focusing on their potential, especially when combined with artificial intelligence (AI) techniques such as machine learning. It analyzed 54 empirical studies in the last few years, highlighting their applicability, strengths, and limitations. The research findings highlight the promise of eye-tracking technology to transform educational practices by providing data-driven insights regarding student behavior and cognitive processes. Future research must address implementation and data-analysis challenges to maximize the educational benefits of eye tracking. Full article

Cardiovascular diseases are a growing public health challenge in Ethiopia, worsened by limited access to diagnostics, including ECG, and shortages of specialized expertise. AI-powered ECG offers potential to improve diagnostic accuracy, efficiency, and access in resource-limited settings, but its adoption is influenced by policy, regulatory, financing, and governance factors, which are not well understood in Ethiopia. This study explored these system-level determinants using qualitative methods from September to October 2025 across federal institutions, four regions, and five tertiary hospitals. Twenty-five stakeholders, including policymakers, regulators, digital health experts, and hospital leaders, were interviewed. Data were transcribed verbatim, coded inductively, and analyzed thematically. Six themes emerged: policy and governance, regulatory frameworks, financing and cost considerations, data governance and bias, integration barriers, and sustainability recommendations. Findings showed AI-powered ECG interpretation aligns with Ethiopia’s digital health and noncommunicable disease priorities, but the country lacks AI-specific health policies, clear regulations, and dedicated budgets. Financing is largely donor-dependent, data governance and algorithmic bias remain concerns, and infrastructure gaps and digital skill shortages limit readiness. Study participants recommended learning from prior digital health projects, coordinated scale-up, phased implementation, and continuous monitoring. Effective adoption will require context-specific policies, sustainable financing, robust regulation, strong data governance, and careful system integration to ensure equitable, responsible, and sustainable use. Full article

Background: Spirituality and religious diversity are increasingly recognized as essential components of holistic nursing care in global healthcare systems. However, their integration into undergraduate nursing education remains heterogeneous and often insufficiently structured, creating a gap between professional values and students’ preparedness to address spiritual needs in culturally diverse clinical environments. This study aimed to explore nursing students’ perceptions, attitudes, and perceived competencies regarding spirituality, religion, and spiritual care. Methods: A descriptive exploratory survey was conducted on a sample of 69 third-year nursing students (69.6% female; majority aged ≤24 years) enrolled in two universities in Rome, Italy. Data were collected between May and July 2025 using the Nursing Care and Religious Diversity Scale (NCRDS), consisting of 31 items. Statistical analyses included descriptive statistics, internal reliability analyses, group comparisons, and multivariate regression models. Results: Students showed moderate levels of attention to patients’ spiritual needs (mean = 3.11, SD = 0.88) and integration of spirituality into care practice, while high importance was attributed to spiritual care skills. University education was perceived as only partially adequate. Multivariate analyses showed that students’ personal spirituality is positively associated with the assessment of spiritual needs and the perception of competence, while exposure to contexts characterized by greater religious diversity is associated with a lower perception of preparedness. Conclusions: The results highlight a discrepancy between professional values and perceived operational skills, suggesting the need to systematically integrate spirituality and religious diversity into nursing curricula. These findings highlight the need for a structured integration of spirituality and religious diversity into nursing curricula through targeted educational strategies in order to strengthen students’ competencies and promote truly holistic and person-centered care. Full article

Artificial intelligence has been considered as a transformative element capable of reshaping STEM education into equitable, resource-efficient, and scalable learning environments. However, realizing this potential requires striking a careful balance between technological innovation, pedagogical considerations, and ethical concerns. This study sought to examine the implementation of artificial intelligence (AI) tools by STEM university faculty members in Saudi Arabia to promote Sustainable Development Goal 4 (quality education). While doing so, the study attempted to explore how Saudi STEM university faculty members integrated AI tools in their instructional practices and analyze their perceptions towards these tools. To achieve these goals, the study employed an explanatory sequential mixed-methods design. In the first phase of data collection, a close-ended questionnaire was applied to a random sample of (324) STEM university faculty members. The second phase involved gathering qualitative data using a semi-structured interview administered to 12 purposively selected experts. Key quantitative findings revealed an overall AI integration at a medium level with a mean of (2.71) and standard deviation of (0.36) across three instructional practices, namely planning, implementation, and assessment. The highest integration level was in assessment (M = 2.93, medium) while the lowest was in planning (M = 2.61, medium). The results also revealed that the participants’ perceptions towards integrating AI tools were highly positive (M = 4.00, high), albeit with some concerns regarding the effect of excessive and unguided use of AI tools on students’ higher-order thinking skills, particularly the risk of AI functioning merely as an information delivery mechanism rather than serving its more pedagogically valuable role as a brainstorming scaffold. Furthermore, the study unveiled a number of barriers to integrating AI tools, including the weakness of digital infrastructure, lack of professional development, the limited credibility of AI-generated content, and ethical concerns related to academic integrity and copyrights. The research suggests the establishment of a sustainable digital environment by improving the infrastructure, providing specific training in accordance with the principles of sustainability, and implementing policies that promote equitable, transparent, and responsible integration of AI. These strategies can coordinate the growth of technology with the larger needs of the quality of education, inclusion, and sustainability of STEM education in the long term. Full article

Integrating artificial intelligence (AI) into design education is no longer optional; it has become an essential tool for enhancing innovative design and preparing students for data-driven practice and rapid technological acceleration. However, ignoring AI risks professional irrelevance; it introduces a range of concerns about students’ cognitive skills and comes with many drawbacks in the education process, as it threatens the attainment of learning outcomes, renders a fair assessment process unachievable, and places academic integrity in a vulnerable position. Using a qualitative case study approach, this research employs semi-structured interviews with 27 senior-year students in the interior design department to gain in-depth academic insights into how AI influenced their design process in their term project and its impact on their cognitive development and decision -making. Instructors’ observations on students’ skills, their pace in the project, and their end-products were documented. This study demonstrates that integrating AI into design education cannot be avoided, making a new paradigm for addressing design education inevitable. Based on the analysis, the paper proposes a conceptual framework outlining key dimensions in teaching and assessing strategies in design education adopting AI, focusing on analysis, critical thinking, reasoning, and process rather than on the end-product and its presentation. Full article

This study investigates the structural relationships between maternal phubbing, toddlers’ language development, and their social development over a three-month interval. The sample consisted of 239 toddlers (aged 16–36 months) residing in Seoul and Gyeonggi-do, along with their mothers and classroom teachers. Data collection was conducted in two phases: the primary survey in May 2025 assessed maternal phubbing and toddlers’ language development through maternal reports, followed by a secondary survey in August 2025, where classroom teachers evaluated the toddlers’ social development. The findings revealed that while maternal phubbing did not significantly predict toddlers’ language development, it exerted a significant negative impact on their social development three months later. Additionally, toddlers’ language development was found to positively influence their subsequent social development. These results suggest that maternal phubbing and toddlers’ language development operate through independent pathways to influence social outcomes. By employing a three-month short-term longitudinal design (time-lagged design), this study identifies a direct path from maternal phubbing to social development and reaffirms the critical role of early language skills in fostering social competence during toddlerhood. Full article

This study evaluates GNSS water-vapor tomography across the Lisbon metropolitan area and explores how increasing network density with low-cost receivers improves three-dimensional humidity fields for meteorological applications. Three configurations were tested for December 2022, a month characterized by several rainfall events, including a severe urban-impacting one: (i) a hybrid setup combining permanent and low-cost stations (TOMO_PL), (ii) a dense network of only low-cost stations (TOMO_L), (iii) a sparse arrangement using only permanent stations (TOMO_P). Tomographic water vapor density fields were compared with independent references from the Weather Research and Forecasting (WRF) model, ERA 5 reanalysis, and radiosonde data. All products show the expected exponential decline in water vapor with increasing altitude. Tomography consistently underestimates moisture in the lowest 2.0 to 2.5 km and tends to overestimate it at higher levels, with a weaker correlation above mid-tropospheric heights. Vertical RMSE remains below 2 g m⁻³ for all solutions, but TOMO_P performs the worst due to weak and uneven spatial geometry. Time–height analysis reveals that densified setups capture the changing moisture in the lower atmosphere, including increased near-surface humidity during December 11–13, when rainfall exceeded 120 mm in 24 h, although mid-level intrusions and dry layers observed by radiosondes are not captured. Mean PWV patterns show realistically low points over the Sintra mountain range and align best with TOMO_PL (spatial RMSE 0.6 g m⁻³, bias 0.4 g m⁻³, correlation 0.9), while TOMO_P creates artifacts that mimic mesoscale gradients. Categorized skill analysis shows the highest accuracy under high-moisture conditions and limited ability to detect dry conditions, with TOMO_PL showing the best overall performance against both ERA5 and WRF. Overall, low-cost densification significantly enhances boundary-layer humidity and PWV retrievals, supporting their use for urban heavy-rain monitoring and, with error-aware integration, for short-term forecasting. Full article

Generative artificial intelligence (AI) is increasingly integrated into sustainability-oriented entrepreneurial practices, raising important questions about its role in shaping human creativity and innovation. This qualitative study examines how postgraduate social entrepreneurship students engage with generative AI during the creativity phase of sustainable startup development. Drawing on Amabile’s componential theory of creativity, this study explores how AI is perceived to relate to domain-relevant skills, creativity-relevant processes, task motivation, and social–contextual factors. Data were collected through an AI-assisted ideation task, followed by semi-structured interviews, and analyzed using reflexive thematic analysis. The findings reveal that generative AI was perceived as supporting information access and associative thinking, while being unable to replicate human intuition and the “aha” moment associated with deep creativity. Moreover, AI was perceived to have limited influence on intrinsic motivation, which remains driven by personal values and contextual responsibility. Socially, AI was consistently described as a tool rather than a teammate, with emotional responses regarded as superficial. The study further suggests that AI may be understood as a social–contextual condition and highlights a perceived trade-off between efficiency and creativity in AI-assisted ideation. These insights extend the application of creativity theory to AI-supported sustainability contexts and offer practical implications for fostering responsible, human-centered innovation in entrepreneurship education. Full article

Artificial Intelligence (AI) is increasingly used in recruitment, performance management, and algorithmic work management, with potentially divergent implications for worker inclusion, exclusion, and discrimination. This systematic review synthesizes peer-reviewed evidence on (i) which AI applications in labor-market settings are linked to inclusion/exclusion outcomes, (ii) the mechanisms and contextual moderators shaping these effects, and (iii) governance and human-resource management responses proposed in the literature. Guided by PRISMA 2020, we searched Scopus and Web of Science (Title/Abstract/Keywords) for English-language journal articles published between 2015 and 2025. Nineteen studies met the eligibility criteria and were analyzed using qualitative thematic synthesis. The evidence indicates an ambivalent pattern: AI can support inclusion through assistive technologies and improved matching, but it can also exacerbate occupational polarization, digital exclusion, and discriminatory outcomes when models are trained on biased data or deployed without transparency and accountability. Outcomes depend on complementary organizational practices, workers’ access to skills, and the regulatory environment. Based on an evidence map of the included studies, we propose a hybrid governance model combining technical and organizational audits, inclusive upskilling/reskilling, participatory regulation, and responsible HR policies to align AI innovation with decent and inclusive work. Given the focused Title/Abstract/Keywords query and the small, heterogeneous corpus, the findings are interpreted as a scoped evidence map rather than an exhaustive census of all AI-and-work research. The model’s contribution lies in integrating four interdependent governance layers—technical, organizational, workforce, and regulatory—within a single labor-market framework. Accordingly, the review should be read as a focused qualitative evidence synthesis, and the proposed model as an evidence-informed conceptual framework that warrants future empirical validation. Full article

The study examines the impact of Work Social Media Usage (WSMU) on employee skills through the mediating roles of perceived relatedness, perceived autonomy, and perceived competence. A comprehensive questionnaire was formulated to measure WSMU and employee skills, based on self-determination theory, along with collecting data using a five-scale Likert questionnaire. The study focused on local and international consulting and training firms that are registered and operational in Jordan. Managers, trainers, marketers, evaluators, consultants, and academics were the main participants of the sample, which was chosen with convenience sampling. There was a total of 518 employees in the sample. The results obtained through structural equation modeling show that WSMU greatly increases employee skills, with perceived competence being the strongest mediator. The research illustrates how social media is useful in promoting the sharing of knowledge, emotional care, and skill acquisition, which can be helpful to organizations in making use of digital interactions for the development of employees. Full article

Surface ozone monitoring remains challenging due to sparse ground networks and limited satellite boundary-layer sensitivity. This study evaluates, for the first time, China’s Environmental Trace Gases Monitoring Instrument II (EMI-II) for estimating surface ozone over the Beijing–Tianjin–Hebei (BTH) region. EMI-II total ozone columns (TOCs) are retrieved using the differential optical absorption spectroscopy (DOAS) algorithm and validated against the TROPOspheric Monitoring Instrument (TROPOMI) (R = 0.96), Geostationary Environment Monitoring Spectrometer (GEMS) (R = 0.97), and the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) ground measurements (R > 0.92, bias < 4%). TOCs are then combined with ERA5 meteorology, satellite NO₂/HCHO, and surface observations within machine learning models, achieving cross-validated R² of 0.94 and RMSE of 12.05 μg/m³ for surface ozone estimation. EMI-II estimates show strong agreement with independent observations (R = 0.91, RMSE = 10.83 μg/m³) and reproduce seasonal gradients, with summer concentrations (131 μg/m³) more than double winter levels (61 μg/m³). Estimation skill is regime-dependent: performance comparable to TROPOMI occurs under strong photochemical activity, while reduced sensitivity occurs under weak radiation and stable boundary layers—consistent with averaging kernel diagnostics. This first comprehensive validation demonstrates that EMI-II, despite vertical sensitivity limitations, provides meaningful surface ozone constraints under favorable atmospheric conditions. The framework is potentially applicable to other regions and sensors under similar conditions, providing a case study for integrating national satellite products into multi-source surface ozone estimation. Full article

Do teacher education programs improve students’ confidence in their field practice teaching skills? Despite a growing interest in how student teachers’ practice self-efficacy (PSE) develops, we know little about the impact of the various components of teacher education programs on PSE. The present study examined whether the first year of teacher education, and particularly the field practice in schools which is directed at training and learning teacher practices, is associated with practice self-efficacy using a targeted measure of PSE for student teachers. Using independent sample t-tests and one-way analysis of variance with survey data from 338 students, we show that second-year students have higher PSE than first-year students on most PSE dimensions, with the largest differences being on the PSE dimensions of Planning and preparation, Teaching in itself, and Evaluation and development. In contrast, first-year students scored higher on Adult collaboration PSE. Further exploratory analyses showed that English majors had lower Planning and preparation and Teaching in itself PSE than other majors, whereas Mathematics majors had higher Adult collaboration PSE. We also conducted item analysis for the purpose of validating the PSE for both first- and second-year students. The findings advance our knowledge of differences in practice self-efficacy over the first year of teacher education. Full article

Positioned in the context of the COVID-19 crisis and subsequent recovery, the research investigates how digital infrastructures and skills contribute to strengthening governance quality and urban economic resilience across Central and Eastern European (CEE) countries over the 2019–2024 timeframe. Using a panel data framework, the analysis incorporates Harris–Tzavalis unit root tests, multicollinearity diagnostics, and comparative fixed and random effects estimations selected via the Hausman test, with additional robustness checks for serial correlation. The findings indicate that digitalization exerts a positive and statistically significant effect on economic resilience, particularly when supported by effective corporate governance structures. At the same time, disparities in digital access highlight persistent structural vulnerabilities across CEE urban areas. The results emphasize the importance of integrating digital transformation and governance reforms into comprehensive resilience strategies. The study contributes to the emerging literature on post-pandemic urban recovery by offering empirical evidence from a heterogeneous regional context and providing policy-relevant insights for sustainable and inclusive urban development. Full article