Search Results (1,096)

Background: Entrustable Professional Activities (EPAs) that transform competencies into distinct, assessable clinical tasks have not yet been developed for US nephrology fellowships. We created and achieved consensus on a set of nephrology-specific EPAs and aligned them with Accreditation Council for Graduate Medical Education (ACGME) competency standards. Methods: This study was conducted within the University of New Mexico nephrology fellowship program. An initial EPA list was generated by the study team using program objectives, a literature review, and clinician insight. Study participants included eight faculty nephrologists and one nephrology fellow, who completed an online-based three-round modified Delphi consensus-building processes. Each EPA was rated on a five-point Likert scale with consensus requiring strict criteria. Finalized EPAs were independently mapped to ACGME nephrology program requirements. Results: Nine study participants (100% response rate) completed all survey rounds. Through iterative consensus, utilizing strict criteria, a final list of 22 distinct EPAs was created, covering 10 core domains of practice including dialysis management, acute kidney injury, chronic kidney disease, electrolyte abnormalities, hypertension, kidney stones, glomerular disease, pregnancy, transplant care, and education. Finalized EPAs were mapped to 38 different ACGME-required sub-competencies, showcasing diversity and applicability to national expectations. Conclusions: We developed the first consensus-based set of EPAs geared for US nephrology fellowship programs, providing a foundation for standardized assessment and curriculum development that could be implemented across nephrology fellowship programs nationally. Full article

Background: Vitamin D deficiency is a common global health problem and remains highly prevalent in Türkiye, where limited food fortification and heterogeneous clinical practices contribute to variability in testing and supplementation strategies. Aims: To provide Türkiye-specific best practice recommendations for defining clinically relevant serum 25-hydroxyvitamin D [25(OH)D] thresholds, identifying adult risk groups for targeted testing, and recommending evidence-based prevention, treatment, and monitoring approaches while minimizing under-treatment and inappropriate high-dose use. Methods: This national expert consensus document was developed by endocrinologists from across Türkiye using a structured, modified Delphi methodology. Draft statements informed by systematic literature reviews were rated via online surveys using a 9-point Likert scale, followed by two Delphi rounds and a face-to-face consensus meeting in İstanbul in October 2025. Results: Recommendations addressed sun exposure, laboratory assessment, screening, supplementation, treatment, and follow-up. Serum 25(OH)D <20 ng/mL was defined as deficiency and <12 ng/mL as severe deficiency, with a target range of 20–50 ng/mL. Routine population-wide screening was not recommended; instead, targeted testing in high-risk adults and symptom-driven biochemical evaluation were endorsed. Empiric supplementation was recommended for selected high-risk groups, with cholecalciferol as the preferred agent. Higher individualized doses were suggested in obesity or malabsorption, while loading regimens were reserved for specific clinical indications, such as severe deficiency or certain medical conditions that impair vitamin D metabolism. Reassessment of 25(OH)D at 8–12 weeks was recommended. Conclusion: These consensus-based recommendations provide a practical, context-specific framework for assessing, preventing, treating, and monitoring vitamin D deficiency in adults in Türkiye. Full article

Objective: Surgical simulation has been shown to improve efficiency, performance, and time to mastery for complicated procedures, but simulation training is not always considered when introducing new devices or products. As part of a performance improvement project, we sought to design and evaluate simulation training for the skin cell suspension autograft (SCSA) with surgery residents during their Burn rotation. Methods: Residents were asked to read instructional materials and watch training videos before coming into the simulation lab for the training session supervised by a Burn surgeon. A qualitative survey was designed and administered after completion of the rotation. Results: Twelve residents have completed the training thus far. Their feedback from the training session was rated on a five-point Likert scale and indicated that the simulation activity was an appropriate length (4.6/5.0), was thorough (4.8/5.0), and led to more confidence (4.4/5.0) and less apprehension (4.4/5.0) when performing the procedure on live patients. This was followed by their use of the product in the operating room with complete success. Conclusions: The novel SCSA training shows great promise for improving the confidence and performance of surgical residents. This could allow for a shorter time for residents to become independent in its use, thereby allowing for increased operative efficiency with the opportunity to significantly improve trainee expertise. Full article

Intimate partner violence (IPV) is a significant global public health issue that requires accessible, scalable, and contextually appropriate interventions. Mobile health (mHealth) technologies provide a promising platform to deliver support, information, and safety planning tools for individuals at risk of IPV. This study aimed to develop and pilot-test iCanPlan, a mobile application designed to support IPV prevention in Thailand. The application evaluates IPV risk, identifies indicators of danger, and provides a countrywide list of assistance sources. iCanPlan consists of four main components: (1) an IPV risk assessment tool, (2) a list of support resources, (3) educational materials presented in the form of infographics, and (4) encouraging quotes from well-known public figures. The app features a clean, user-friendly interface with intuitive navigation and color-coded components to enhance usability. In addition, a preliminary study was conducted with 30 experts from multidisciplinary fields, including gender-based violence research, social work, psychology, public health, and non-governmental organizations. Participants used the application for one month and subsequently evaluated it using a structured questionnaire based on heuristic evaluation principles. The questionnaire assessed usability, safety features, content quality, cultural appropriateness, language clarity, ethical considerations, and overall evaluation using a five-point Likert scale. Data was analyzed using descriptive statistics (mean and standard deviation) in SPSS. The findings demonstrated excellent performance across all domains, with high mean scores for usability (M = 4.93), safety features (M = 4.73), and content quality (M = 4.82), while cultural appropriateness, language clarity, ethical considerations, and overall evaluation achieved perfect scores (M = 5.00). These results indicate strong agreement among experts regarding the application’s usability, safety, and relevance. The study highlights the potential of iCanPlan as a culturally appropriate and user-friendly digital intervention for IPV prevention. Further research involving the target population is needed to evaluate its effectiveness and long-term impact on help-seeking behavior and IPV-related outcomes. Full article

This study aimed to evaluate the suitability of previously validated Entrustable Professional Activities (EPAs) for pharmacists carrying out medication management reviews (MMRs) and further align them with nationally accepted performance outcomes (POs). This was a two-phase study involving Subject-Matter Experts (SMEs). Phase 1 aimed at assessing the relevance of 14 EPAs previously validated for use in a provisionally registered pharmacist training programme to the role of MMR pharmacists utilising a five-point Likert scale. A 70% threshold for “agree” and “strongly agree” was used to qualify an EPA for phase 2. During phase 2, EPAs were mapped for their ability to demonstrate APC performance outcomes for MMRs and the results were descriptively reported. Of the eight SMEs recruited, seven completed both surveys. Of the 14 EPAs, 12 qualified for phase 2. In phase 2, the 12 EPAs were mapped against 24 POs. Five EPAs were matched with 100% agreement to six POs. To our knowledge, this is the first study of its kind to map pre-validated EPAs to POs for credentialing in the scope of MMR pharmacists in Australia. A total of 12 EPAs were applicable to pharmacists working in the MMR space, and five were able to be directly mapped to six POs. Full article

Objectives: Coronary artery bypass grafting (CABG) remains a fundamental surgical treatment for advanced coronary artery disease. With the increasing use of large language models to obtain health information, patients are increasingly turning to these systems to understand surgical options. However, their performance in generating patient-oriented CABG information has not been sufficiently evaluated. Therefore, this study aimed to compare the responses generated by ChatGPT and DeepSeek-R1 to patient questions about CABG in terms of scientific accuracy, comprehensibility, and level of unnecessary detail. Methods: Forty patient-oriented questions were developed based on online sources and clinical experience. Responses were obtained from ChatGPT and DeepSeek under standardized conditions. A blinded panel of four cardiovascular surgeons evaluated the responses using a five-point Likert scale across three domains. Statistical analyses were performed using paired tests. Results: DeepSeek generated significantly longer responses than ChatGPT (212.88 ± 48.13 vs. 188.7 ± 50.34 words; p < 0.001). Accuracy scores were higher for DeepSeek (median 4.5 vs. 4.25; p = 0.004), whereas comprehensibility and unnecessary detail scores were similar between the models. Overall scores were high for both models (4.32 ± 0.28 vs. 4.27 ± 0.30; p = 0.34). Conclusions: The responses generated by both models were generally evaluated favorably by the expert panel, with only limited differences observed between them. DeepSeek demonstrated higher accuracy, whereas ChatGPT tended to produce shorter and more concise responses. However, given the variability observed at the individual-question level, these findings should be interpreted with caution. Large language models may support patient information delivery but should not be considered reliable stand-alone sources for clinical decision-making or patient counseling. Full article

This study presents a two-phase framework for selecting clinically plausible explainable artificial intelligence (XAI) heatmaps for retinal optical coherence tomography (OCT) classification. A six-class Swin Transformer model was trained and validated using a combined dataset consisting of a subset of the public OCT-C8 dataset and private data from a Greek tertiary hospital and externally evaluated on an independent dataset from a private ophthalmological institute. Diagnostic performance was high, achieving 97% accuracy in cross-validation and 91.82% on external evaluation. In Phase 1, one ophthalmologist and one artificial intelligence (AI) specialist independently assessed 100 heatmaps per method based on visual quality and anatomical plausibility, reducing the candidate methods to three. In Phase 2, 21 specialists evaluated the selected methods across multiple cases using a five-point Likert scale reflecting agreement between highlighted regions and the model diagnosis. The proposed Token contRAST map (TRAST) achieved the highest ratings, followed by Gradient-weighted Class Activation Mapping (Grad-CAM++), while Cosine-Grad Fusion Map (CGFM) showed the lowest performance. These findings reflect clinical plausibility rather than direct model interpretability and indicate that effective XAI in OCT imaging requires not only technical performance but also structured expert evaluation. The proposed framework provides a practical approach for selecting explanation methods suitable for clinical use in ophthalmology. Full article

Purpose: Access to assistive technology (AT) is a fundamental human right and a critical component of Universal Health Coverage (UHC). In the Middle East and North Africa (MENA) region, the scarcity of trained professionals remains a significant barrier to AT service provision. This study evaluates the effectiveness and perceived usefulness of the Assistive Technology Training Program (ATTP), a specialized continuing education initiative designed to build workforce capacity among rehabilitation and healthcare professionals. Methods: A convergent mixed methods design was used to analyze quantitative pre/post-test scores and qualitative focus group open-ended responses. Quantitative data were gathered from 386 participants across 11 MENA countries using a pre- and post-test assessment of AT knowledge. Qualitative utility and participant satisfaction were assessed through a 5-point Likert scale survey evaluating content relevance, trainer expertise, and facilities. Association tests (ANOVA and t-tests) were conducted to identify factors influencing knowledge gain. Results: Participants demonstrated a statistically significant improvement in AT knowledge, with the overall mean score increasing from 3.67 ± 1.13 to 7.50 ± 1.25 (p < 0.001). High levels of satisfaction were reported, with 92% of participants rating the training as “Very Good” or “Excellent” regarding its relevance to clinical needs. Association tests revealed that professional background (p < 0.001), employment status (p = 0.0017), level of education (p = 0.011), and prior training experience (p = 0.026) were significant factors in the magnitude of improvement, although all subgroups achieved significant learning gains. Qualitative thematic analysis per the focus group discussions using the WHO-GATE 5 P framework identified three major themes: (1) Structural Challenges: Issues with Products and Provision point toward a need for better infrastructure and localized supply chains. (2) Human Capital: Personnel barriers emphasize that training shouldn’t just be for professionals, but should extend to caregivers as well. (3) Systemic and Social Change: Policy and People focus on the “soft” side of AT moving toward user-involved guidelines and fighting social stigma to ensure rights are upheld. Conclusions: The ATTP is an impactful educational intervention that significantly enhances the foundational competencies of healthcare professionals in the MENA region. By addressing knowledge gaps and fostering practical skills, the program serves as a preliminary model that demonstrates potential for building regional capacity and supporting the United Nations’ Sustainable Development Goal (SDG) #3 related to health and wellbeing and SDG #4 related to quality education and lifelong learning opportunities for all. Further research is required to evaluate its long-term scalability and clinical impact. Full article

Background/Objectives: Telemedicine-enabled medication dispensing and delivery was formally regulated in Jordan in 2025, but the Jordan Pharmacists Association publicly rejected the pharmacy-related provisions because of concerns about safety, liability, and the pharmacist’s professional role. This study evaluated community pharmacists’ acceptance of the regulated model and identified perceived patient safety risks, implementation barriers, and required safeguards. Methods: A convergent mixed-methods design was used. A cross-sectional online survey was completed by 350 licensed community pharmacists (response rate 83.3%). The questionnaire assessed willingness to participate, perceived patient safety risks, implementation barriers, and facilitators using 5-point Likert scales. Multivariable logistic regression examined predictors of willingness. Semi-structured interviews were conducted with 22 purposively sampled pharmacists until thematic saturation. Quantitative and qualitative findings were integrated using joint displays. Results: Only 28.3% of pharmacists were willing to participate under current conditions, 46.9% were unwilling, and 24.9% expressed conditional acceptance; 52.0% opposed national implementation. Patient safety concerns were great (mean 4.4 ± 0.6/5), especially regarding remote patient assessment (91.4%) and medication errors (88.9%). Implementation barriers were severe (mean 4.5 ± 0.5/5), mainly regulatory ambiguity (92.0%) and unclear liability (89.7%). Facilitators were strongly endorsed (mean 4.7 ± 0.4/5), particularly mandatory pharmacist verification (94.6%) and clear legal protections (93.4%). Qualitative findings reinforced pharmacists’ role as the “final safety checkpoint” and showed acceptance depended on strong safeguards. Conclusions: Jordanian pharmacists showed principled resistance to the current model. Acceptance depends on pharmacist oversight, legal clarity, and infrastructure readiness. Full article

Background/Objectives: Pharmacists play an essential role in healthcare delivery across Europe, yet growing professional demands, organisational constraints, and evolving practice models may negatively affect job satisfaction and professional fulfilment. This study aimed to evaluate job satisfaction and professional perception among pharmacists across multiple European countries and to identify sociodemographic and workplace-related factors associated with these outcomes. Methods: A cross-sectional, web-based survey was conducted between October 2023 and January 2024 among licensed pharmacists from 17 European countries. Eligible participants were pharmacists employed in community pharmacies, hospitals, clinical pharmacy services, or the pharmaceutical industry. The questionnaire, developed and administered in English, collected sociodemographic and professional data and included two composite measures: the Job Satisfaction Scale (12 items) and the Pharmacist Professional Perception Scale (6 items). Responses were recorded using 5-point Likert scales. Descriptive statistics and inferential analyses were performed using SPSS version 27.0. Results: A total of 789 pharmacists participated (median age 40 years; 80.1% female). The mean job satisfaction score was 3.26 (SD 0.88), with the lowest scores related to staffing adequacy and salary, and the highest to collegial relationships. The mean professional perception score was 3.08 (SD 0.81), indicating moderate perceived professional recognition. Significant associations were identified between both scales and workplace setting, income level, employment status, geographical region, education, and professional experience (p < 0.05). Conclusions: In this multi-country convenience sample, pharmacists reported moderate levels of job satisfaction and professional perception, with variation across workplace and sociodemographic factors. These findings should be interpreted cautiously, as the sample is not representative of all European pharmacists; however, they suggest that staffing conditions, remuneration, professional recognition, and career development opportunities may be relevant areas for further investigation and policy attention. Full article

Background: Healthcare chatbots have emerged as a promising application of artificial intelligence in healthcare, offering potential benefits in accessibility, efficiency, and patient engagement. However, despite their growing adoption, limited research has examined the factors that determine their success from the user’s perspective. Objective: This study aimed to evaluate the success of a health chatbot service by applying the updated DeLone and McLean Information Systems Success Model augmented with a trust construct, examining the effects of information quality, system quality, service quality, and trust on intention to use, user satisfaction, and net benefits. Methods: An online survey design was employed, utilizing a structured questionnaire with 28 items measuring seven constructs on a seven-point Likert scale. Data were collected electronically from residents of Saudi Arabia between July and September 2024 using convenience sampling. Eligible participants were adults aged 18 years or older who had previously used the health chatbot service. A total of 321 valid responses were obtained. Partial Least Squares Structural Equation (PLS-SEM) was conducted using SmartPLS 3.3 software for measurement and structural model analysis. Results: The measurement model demonstrated acceptable reliability and validity, with composite reliability values exceeding 0.90 and average variance extracted values above 0.70 for all constructs. Structural model analysis supported eight of ten hypotheses. Trust exhibited the strongest effect on intention to use (β = 0.359, p < 0.001), followed by system quality (β = 0.234, p < 0.001) and information quality (β = 0.147, p < 0.01). Intention to use significantly predicted user satisfaction (β = 0.620, p < 0.001) and net benefits (β = 0.278, p < 0.001). User satisfaction demonstrated a strong positive effect on net benefits (β = 0.610, p < 0.001). The model explained 67.6% of the variance in intention to use, 72.7% in user satisfaction, and 71.4% in net benefits. Conclusions: Trust emerged as the most influential factor affecting intention to use the healthcare chatbot service, underscoring its critical role in user acceptance of health chatbot services. Information quality, system quality, and service quality exerted small to moderate effects on behavioral outcomes. These findings suggest that healthcare organizations deploying chatbot services should prioritize building user trust alongside ensuring high system and information quality to maximize user satisfaction and realized net benefits. Full article

Background: The expanding interest in chatbots within the medical domain underscores the imperative for a comprehensive understanding of their capabilities and limitations, particularly in the context of anatomical education. Chatbots possess the potential to comprehend intricate anatomical concepts, deliver both advanced and contextually relevant information, and serve as a valuable resource for medical students and educators. This study aimed to evaluate the proficiency and constraints of chatbots in the domain of neuroanatomy. Methods: We developed 30 questions and administered them to ChatGPT-4, Google Gemini, Microsoft Copilot, and Perplexity.ai in their open versions. Questions were collaboratively constructed by the research team, selected through a semi-randomized process within the domain of neuroanatomy. Chatbots’ responses were evaluated in a blinded manner for validity and appropriateness, utilizing a 5-point Likert scale. Results: The highest observed performance among the evaluated chatbots was exhibited by ChatGPT-4 and Perplexity.ai, which achieved scores of 4.6 ± 0.5 and 4.5 ± 0.5, respectively. Microsoft Copilot (4.4 ± 0.5) and Google Gemini (4.1 ± 1.0) followed. The least successful performance was observed in the task of generating a neuroanatomical structure: only Microsoft Copilot attempted to fulfil the request, albeit with a dramatically flawed outcome. Conversely, Google Gemini and Perplexity.ai provided web links to anatomical illustrations. Conclusions: Despite technological advancements, AI models have not yet reached a level of sophistication sufficient to entirely supplant the role of educators or facilitators in a neuroanatomy course; however, they can serve as valuable adjunct tools for medical educators and students when utilized with careful consideration. Full article

Power electronics is a cornerstone of modern electrical engineering, underpinning technologies from renewable energy systems to electric vehicles. Traditional lecture-based methods often emphasise rote learning and procedural skills but provide limited opportunities for higher-order thinking or experiential practice. To meet the needs of Generation Z learners and align with industry expectations, new pedagogical frameworks are required that combine cognitive rigour with authentic, technology-enhanced learning. This study introduces a Higher-Order Thinking Skills with Real-Time Simulation pedagogical framework to enhance learning outcomes in diploma-level power electronics. A quasi-experimental mixed-methods design was applied with 40 students divided into control and experimental groups. The control group received lectures, while the experimental group engaged with the HOTS–RTS framework across four topics: rectifiers, converters, inverters, and applications. Pre- and post-tests, Likert-scale surveys, reflections, and instructor observations provided data for both quantitative (t-tests, effect sizes) and qualitative thematic analysis. The experimental group achieved higher post-test gains (20.1 vs 9.5 points), with a large effect size (d = 1.9). Surveys revealed that 65 per cent of respondents rated RTS as highly effective, and Likert scores improved by 1 or more points in HOTS-related skills. Reflections emphasised clarity, confidence, and collaboration. HOTS–RTS effectively integrates cognitive rigour with real-time practice, aligning with STREAMS principles and equipping learners with next-generation industry competencies. Full article

The rapid development of artificial intelligence (AI) and Generative AI (GenAI) based on large language models (LLMs) is reshaping teaching practices, assessment criteria, and ethical questions regarding authenticity, source reliability, and educational responsibility. Understanding teachers’ attitudes toward AI is crucial for identifying acceptance, resistance, and professional development needs. This study aimed to adapt and validate, for the Italian context, the questionnaire developed by Alsudairy and Eltantawy for assessing teachers’ attitudes toward AI in education, and to explore attitudinal differences according to selected socio-professional variables. A convenience sample of 682 in-service teachers from different school levels and Italian regions completed the 36-item questionnaire on a 3-point Likert scale. Exploratory factor analysis suggested an interpretable two-factor structure, although some items showed weak, non-salient, or cross-loadings. A confirmatory factor analysis conducted on a refined 32-item ordinal model supported a correlated two-factor solution with good global fit indices. However, the strong correlation between the two latent factors and the presence of selected weak indicators suggest that further refinement and cross-validation are needed. Educational attainment was the only socio-professional variable significantly associated with attitudes toward AI, although the effect size was small. Post hoc analyses showed a significant difference between teachers holding a postgraduate Master’s degree and those holding only a high school diploma, whereas other differences should be interpreted as descriptive trends. Taken together, these findings provide preliminary support for the Italian adaptation of the instrument and offer initial insight into the role of professional characteristics in shaping teachers’ attitudes toward AI in educational settings. Full article

Introduction: Artificial intelligence (AI)-based chatbots are becoming an increasingly popular source of health information, particularly for common dermatological conditions such as scabies. However, concerns remain about the accuracy, reliability, quality and readability of the information they provide. Objectives: The aim of this study was to evaluate the accuracy, reliability, quality and readability of responses generated by different AI chatbots in answer to patient questions about scabies. Methods: Scabies-related questions were collected from Quora, a publicly accessible question-and-answer platform, and screened for relevance. Following expert review, 20 representative questions were selected. Responses were generated by three large language models: ChatGPT-5.2, DeepSeek and Claude Sonnet 4.5. The outputs were evaluated by expert reviewers using the hallucination rate, modified DISCERN (mDISCERN), Global Quality Score (GQS), Flesch Reading Ease Score (FRES), and an accuracy assessment based on a 5-point Likert scale. Results: In this study, it was found that ChatGPT-5.2 demonstrated the highest information quality (mDISCERN: 33.6 ± 1.8) and readability (FRES: 63.25 ± 11.5). DeepSeek achieved the highest global quality score (GQS: 5.00 ± 0.00) and accuracy score (5.00 ± 0.00). Claude Sonnet 4.5 had lower scores across most metrics. There were significant differences in hallucination rates among the models (p = 0.003), with DeepSeek exhibiting higher rates. Overall, statistically significant differences were observed among the models in terms of quality, readability and accuracy. Conclusions: AI chatbots provide generally informative but variable-quality responses to scabies-related questions. While DeepSeek demonstrated higher accuracy and overall quality, it also showed higher hallucination rates, whereas ChatGPT-5.2 provided more readable and reliable responses. These findings highlight variability across models and the need for cautious use. AI tools should be considered supportive resources rather than substitutes for professional medical advice. Full article