Search Results (1,064)

Objective: This study aims to evaluate pharmacy students’ perceptions regarding the integration of generative artificial intelligence (GenAI) into pharmacy curricula, providing evidence to inform future curriculum development. Methods: A cross-sectional survey of Doctor of Pharmacy (PharmD) students at a single U.S. College of Pharmacy was conducted in April 2025. Students from all four professional years (P1–P4) were invited to participate. The 10-item survey assessed four domains: (1) General GenAI Use, (2) Knowledge and Experience with GenAI Tools, (3) Learning Preferences with GenAI, and (4) Perspectives on GenAI in the curriculum. Results: A total of 110 students responded (response rate = 12.4%). Most were P1 students (56/110, 50.9%). Many reported using GenAI tools for personal (65/110, 59.1%) and school-related purposes (64/110, 58.1%) sometimes, often, or frequently. ChatGPT was the most used tool. While 40% (40/99) agreed or strongly agreed that GenAI could enhance their learning, 62.6% (62/99) preferred traditional teaching methods. Open-ended responses (n = 25) reflected a mix of positive, neutral, and negative views on GenAI in education. Conclusions: Many pharmacy students in this cohort reported using GenAI tools and demonstrated a basic understanding of GenAI functions, yet students also reported that they preferred traditional learning methods and expressed mixed views on incorporating GenAI into teaching. These findings provide valuable insights for faculty and schools of pharmacy as they develop strategies to integrate GenAI into pharmacy education. Full article

Manual wheelchair users with spinal cord injury (SCI) often experience upper-limb strain and pain due to repetitive propulsion. A detachable dual-propulsion add-on device has been developed to mitigate this issue by offering an alternative propulsion mechanism, but its user acceptability and practical benefits must be rigorously evaluated. This study will implement a structured mixed-methods usability assessment of the new device with 30 adult wheelchair users with SCI. The evaluation will combine quantitative surveys, objective task-based performance metrics, and qualitative interviews to capture a comprehensive picture of usability. We will conduct a single-arm mixed-methods protocol using a device-specific 45-item usability questionnaire and semi-structured interviews, followed by convergent triangulation to integrate quantitative scores and qualitative themes. Participants will use the dual-propulsion device in realistic scenarios and then complete a 45-item questionnaire covering effectiveness, efficiency, safety, comfort, and psychosocial satisfaction. In addition, semi-structured interviews will explore users’ experiences, perceived benefits, challenges, and suggestions. During a standardized mobility task course (doorway navigation, ramp ascent, threshold crossing, and 50 m level propulsion), objective performance indicators—including task completion time, task success/error rate, number of lever strokes, and self-selected speed—will be recorded as secondary usability outcomes. The use of both a standardized questionnaire and in-depth interviews will ensure both broad and nuanced assessment of the device’s usability. Data from the survey will be analyzed for usability scores across multiple domains, while interview transcripts will undergo thematic analysis to enrich and validate the quantitative findings. This protocol is expected to provide robust evidence of the device’s usability, inform iterative improvements in its design, and highlight the importance of structured usability evaluations for assistive technologies. Full article

Background/Objectives: The university stage is a critical period for consolidating dietary habits that influence future health. University canteens therefore play a key role in providing menus aligned with nutritional recommendations. As menu composition shapes students’ access to healthy food, its evaluation also has equity implications. This study aimed to apply a newly designed questionnaire—the COMUNI questionnaire—intended to provide a rapid, user-friendly, and transferable method for evaluating the dietary quality of lunch menus offered in university canteens. Methods: Two versions of the 13-item COMUNI questionnaire were developed: COMUNI-1 for single-option menus and COMUNI-2 for menus offering multiple first- and second-course choices. The tool evaluates the frequency of key food groups, the availability of water and wholegrain bread, and the variety of foods and culinary techniques. To test the questionnaire, it was applied to 34 menu templates from university residences, colleges, and cafeterias. Results: 85.3% of menus showed deficient dietary quality, and 14.7% were rated as improvable; none achieved an optimal score. Menus managed by catering companies obtained significantly higher scores than those under direct management. Most frequently shortcomings included insufficient offerings of vegetables, legumes, fish, and wholegrain bread, alongside a frequent presence of refined carbohydrate sources and fried or ultra-processed foods. Conclusions: Universities should incorporate adherence to dietary recommendations as a key criterion in food-service procurement. The COMUNI questionnaire provides a simple and operational tool for assessing menu quality, supporting both diagnosis and monitoring of university food-service, once formally validated. Its use may also help identify structural disparities in access to healthy foods across campus settings, supporting more equitable food-service policies. Full article

Background/Objectives: Ecological momentary assessment (EMA) is a methodology that offers a real-time approach to monitoring clinician well-being, but its utility during high-intensity operational periods remains underexplored. This study examines the feasibility and performance of an EMA-based system for tracking clinical responsibilities and burnout among healthcare workers during the first year of the COVID-19 pandemic. Methods: Utilizing an intensive longitudinal design, 398 healthcare workers, including physicians, physician assistants, nurses, and trainees, completed brief EMA surveys every five days from April 2020 to March 2021. Burnout was assessed with a validated single-item measure and analyzed in relation to redeployment status and hospital caseloads. Results: The EMA approach successfully captured meaningful temporal fluctuations in burnout. Redeployment was associated with higher burnout levels (b = 0.125; p = 0.01), and rising caseloads amplified this effect (interaction b = 0.169; p = 0.001). Nurses showed the strongest caseload-related increases in burnout (b = 0.359; p < 0.001). These patterns persisted even after individuals returned to their usual roles. Conclusions: This study demonstrates that EMA is a scalable and sensitive approach for continuous burnout surveillance, capable of detecting role-specific and context-dependent stress responses in real time. EMA-based monitoring can support early identification of at-risk groups, guide staffing and redeployment decisions, and inform timely organizational interventions during crises and other periods of operational strain. Full article

Background: Recent research has focused on psychological constructs that maintain psychopathology, along with the constraints of single-diagnosis protocols that have contributed to the expansion of process-based psychotherapy. Therefore, the Process-Based Assessment Tool (PBAT) was developed for identifying relevant psychological processes and facilitating personalized approaches. The aim of the present study was the adaptation of the PBAT for the Romanian population. Methods: Participants (n = 637) encompassed a non-clinical and a clinical sample who filled in the PBAT. In addition, within the non-clinical group, the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 were used for measuring the level of depression and anxiety, respectively. Results: The correlations between different PBAT processes were found in the expected direction, according to the valence of individual items. The outcomes of the network analyses revealed the centrality of several items within both samples. Also, the results of the Boruta analysis showed the predictive role of some processes in relation to anxiety and depression. Conclusions: By resembling the findings of other PBAT validation studies, the present investigation demonstrated that the instrument can be reliably applied for exploring psychological processes correlated with adaptation and maladaptation within the Romanian population. Full article

In the context of post-pandemic remote work normalization and the emergence of the metaverse, virtual workspaces have attracted significant attention as critical digital infrastructure with promising application prospects. While virtual workspaces enable efficient task performance, compared with traditional ones, the lack of emotional connection between humans and machines adversely affects participants’ mental health. The emergence of affective computing has made it possible to endow virtual workspaces with “affective intelligence”. Therefore, this study aims to clarify the relationship between color and participants’ emotions in virtual workspaces through an experiment involving 48 participants, and eight virtual workspaces were constructed, incorporating four color conditions (red, blue, yellow, and green) and two workspace types (shared and single). Data were synchronously collected using the Positive and Negative Affect Schedule (PANAS), a questionnaire item on arousal, electrodermal activity (EDA), and heart rate variability (HRV). The results successfully established specific associations between colors and emotions: red with “anxious”, yellow with “happy”, and blue with “calm”. Although no specific emotion word was identified for green, this study successfully achieved the emotion classification of virtual workspaces and constructed a corresponding dataset. These findings provide a theoretical foundation for the development of affective computing models. Full article

This study assessed whether permitting certified recycled aggregate companies to assign both quality and environmental management responsibilities to a single individual affects the effectiveness of post-certification quality management. Using data from 242 post-certification audits conducted in 2023, six regulatory audit items were quantified using a binary scoring scheme to produce a six-point score for each company. Audit outcomes were compared between companies employing dedicated quality managers (n = 147) and those operating with concurrently appointed managers (n = 95). Before conducting hypothesis testing, skewness, kurtosis, and F-tests were used to verify approximate normality and homogeneity of variances. Two-sample t-tests assuming equal variances revealed no statistically significant differences between the two personnel structures, and the effect size (Cohen’s d = 0.072) indicated negligible practical differences. Additionally, 52 companies (22%) experienced changes in their quality management personnel during the audit period. A separate comparison between companies with and without such changes also showed no statistically significant differences, with a small effect size (d = 0.276). These results suggest that the 2022 regulatory revision authorizing concurrent appointments did not exert any discernible adverse influence on post-certification audit performance and that additional administrative requirements for managing personnel changes may be unnecessary. The findings also highlight recurring deficiencies—particularly in quality testing and equipment management—which warrant continued attention from policymakers, certification bodies, and certified companies seeking to enhance the effectiveness of the recycled aggregate quality certification system. Full article

Background and Objectives: Parental fears and misconceptions about topical corticosteroids (TCS), known as TCS phobia, can hinder adherence and lead to poor eczema control in children. Despite its clinical relevance, few instruments capture this phenomenon using modern psychometric principles. This study aimed to develop and validate the Parental Topical Corticosteroid Phobia Scale (PTCPS), a brief tool grounded in Rasch measurement theory. Methods: A cross-sectional survey was conducted among 678 parents of children with eczema in Saudi Arabia. The five-item PTCPS was designed to reflect cognitive, emotional, and behavioral components of corticosteroid phobia. Rasch analysis using WINSTEPS assessed item fit, person and item separation and reliability, unidimensionality, and category functioning. Exploratory factor analysis (EFA) and principal component analysis (PCA) of residuals further evaluated structural validity. Results: All five items fit the Rasch model well (infit/outfit MnSq: 0.8–1.2), with strong item reliability (0.96) and clear item separation (4.67), indicating a well-defined item hierarchy. Person reliability was lower (0.40), suggesting limited precision in distinguishing between respondent levels. The scale showed functioning dichotomous response categories with no disordered thresholds. The eigenvalue of the first residual contrast (1.78) supported unidimensionality. Exploratory factor analysis confirmed a single-factor solution accounting for 53.0% of total variance, with substantial factor loadings (0.68–0.76) across all items, supporting structural coherence of the scale. Conclusions: The PTCPS is a psychometrically robust, unidimensional instrument for assessing TCS phobia in parents. Future research should validate its use across cultures, explore longitudinal stability, and assess its predictive value for treatment adherence. Full article

Large Language Models (LLMs) are increasingly used for rubric-based assessment, yet reliability is limited by instability, bias, and weak diagnostics. We present EvalCouncil, a committee-and-chief framework for rubric-guided grading with auditable traces and a human adjudication baseline. Our objectives are to (i) characterize domain structure in Human–LLM alignment, (ii) assess robustness to concordance tolerance and panel composition, and (iii) derive a domain-adaptive audit policy grounded in dispersion and chief–panel differences. Authentic student responses from two domains–Computer Networks (CNs) and Machine Learning (ML)–are graded by multiple heterogeneous LLM evaluators using identical rubric prompts. A designated chief arbitrator operates within a tolerance band and issues the final grade. We quantify within-panel dispersion via MPAD (mean pairwise absolute deviation), measure chief–panel concordance (e.g., absolute error and bias), and compute Human–LLM deviation. Robustness is examined by sweeping the tolerance and performing leave-one-out perturbations of panel composition. All outputs and reasoning traces are stored in a graph database for full provenance. Human–LLM alignment exhibits systematic domain dependence: ML shows tighter central tendency and shorter upper tails, whereas CN displays broader dispersion with heavier upper tails and larger extreme spreads. Disagreement increases with item difficulty as captured by MPAD, concentrating misalignment on a relatively small subset of items. These patterns are stable to tolerance variation and single-grader removals. The signals support a practical triage policy: accept low-dispersion, small-gap items; apply a brief check to borderline cases; and adjudicate high-dispersion or large-gap items with targeted rubric clarification. EvalCouncil instantiates a committee-and-chief, rubric-guided grading workflow with committee arbitration, a human adjudication baseline, and graph-based auditability in a real classroom deployment. By linking domain-aware dispersion (MPAD), a policy tolerance dial, and chief–panel discrepancy, the study shows how these elements can be combined into a replicable, auditable, and capacity-aware approach for organizing LLM-assisted grading and identifying instability and systematic misalignment, while maintaining pedagogical interpretability. Full article

Background: Daily energy intake from snacking behaviors has increased over the past few decades, during which the prevalence of obesity and metabolic syndrome has risen to epidemic proportions. There remains considerable room for improvement in the overall quality of dietary intakes of the U.S. population when compared to national recommendations. Food cravings may contribute to the types of snacks chosen for consumption, and thus, the frequency of foods and food groups consumed, and the overall nutritional quality of the diet. Methods: Eighty-four young (28.5 ± 4.3 years) adults with at least one metabolic syndrome risk factor participated in a parallel-arm single-blind randomized trial designed to compare effects of consuming a mix of tree nuts versus typical high-carbohydrate food items as between-meal snacks for 16 weeks. Cravings for 28 common foods via the Food Craving Inventory, short-term dietary intakes via 24 h multi-pass methodology, food group frequency via the Rapid Eating Assessment for Participants, usual hunger and fullness via visual analog scales, appetite-regulating hormones, and diet quality via the Healthy Eating Index—2015 were measured at baseline and end of study. Results: Participants in the TNsnack group had significant decreases in cravings for high sweet items and fast-food items, which were associated with decreased frequency of desserts and salty foods along with increased intake of higher protein items. In contrast, no significant reductions in food cravings or preference for sweets were observed in the CHOsnack group. Decreased cravings for sweets by TNsnack participants were associated with increased total GLP-1 levels: cake (r = −0.35, p = 0.03), brownies (r = −0.44, p = 0.02), candy (r = −0.36, p = 0.03) and ice cream (r = −0.33, p = 0.04). Overall, the total diet quality score improved by 19% among TNsnack participants. Conclusions: Replacing more typical between-meal snacks with tree nuts may reduce food cravings, particularly for sweeter food items that are likely to be nutrient poor and energy dense. By reducing cravings and frequency of intake, consuming tree nuts as snacks could facilitate having a higher quality, more nutrient-dense diet and mitigate potential negative effects of snacking on metabolic health in young adults. Full article

Prolonged Exposure (PE) is an effective evidence-based psychotherapy for posttraumatic stress disorder (PTSD); however, a small subset of veterans fails to achieve meaningful symptom reduction from PE. Given sleep’s role in memory consolidation, poor sleep quality may adversely affect fear extinction learning central to PE. Existing research on sleep and PE response often relies on single-item or global measures that miss nuanced sleep processes, assesses sleep as static (e.g., at baseline) rather than as a dynamic process, or focuses on concurrent rather than prospective associations. This study used a multidimensional measure of sleep quality to evaluate whether changes across several domains of sleep concurrently and prospectively predicted reduced PTSD symptoms. Sleep quality was assessed pre- and post-treatment and PTSD symptoms were measured pre- and post-treatment and at 3- and 6-month follow-ups. Changes in sleep domains were analyzed as predictors of concurrent and prospective PTSD symptoms. Improvements in overall sleep quality, subjective sleep quality, and daytime dysfunction were associated with improvements in PTSD symptom severity from pre- to post-treatment. Greater improvements in overall sleep quality and daytime dysfunction predicted lower PTSD severity at follow-up; however, greater reductions in daytime dysfunction predicted symptom increases across follow-up. Our findings highlight the importance of overall sleep quality in PE response and suggest that daytime dysfunction due to sleep problems may contribute to diminished long-term outcomes. Targeting these aspects of sleep may enhance treatment efficacy. Full article

Background: Efficient and accurate clinical data management is crucial for stroke care and research; however, for complex stroke cohorts, manual data entry is often complicated by high human error rates and significant inefficiency. This study addressed this gap by developing and evaluating an automated system for constructing high-quality stroke cohorts from electronic health records (EHRs). Methods: This retrospective cohort study was conducted at a single specialty hospital, comparing manual data entry (January–June 2022) with an automated system (January–June 2024). The system uses C# and secure SQL protocols for direct EHR integration. We developed an automated system using the C# programming language to extract 133 items covering the full hospitalization period (from admission to discharge) from EHRs, aligning with the Korean Stroke Registry, the Registry of Stroke Care Quality, and national quality assessment programs. The system’s effectiveness was evaluated by assessing the data entry time, data error rate, and medical record completion rate and comparing the automated method’s performance against conventional data entry. Results: The automated system significantly reduced the data entry time from 35 min to 19 s per patient. Furthermore, the data error rate decreased from 2.32% to 0.15% (p < 0.001), and the rate of missing medical records decreased from 28.9% to 16.2% (p < 0.001). Conclusions: The proposed clinical data collection and cohort construction system effectively improved data quality and efficiency compared to the manual method. This system provides a reliable and scalable data infrastructure that could facilitate research on stroke and quality improvement initiatives. Full article

Background/Objectives: The Developing and Evaluating a Machine Learning Opioid Prediction & Risk-Stratification E-Platform (DEMONSTRATE) trial aims to assess the usability, acceptability, feasibility, and effectiveness of implementing a machine learning (ML)-based clinical decision support (CDS) tool—the Overdose Prevention Alert—which predicts a patient’s risk of opioid overdose within three months. Methods: This single-arm study uses a pre–post implementation design with mixed-methods evaluation in 13 University of Florida Health, Gainesville, internal medicine and family medicine clinics. Eligible patients are aged ≥18 years, received an opioid prescription within the year prior to their upcoming primary care visit, are not receiving hospice care, do not have a malignant cancer diagnosis, and are identified by the ML algorithm as high risk for overdose. The Overdose Prevention Alert triggers when a primary care provider (PCP) signs an opioid order in electronic health records. We will evaluate effectiveness by comparing pre- and post-implementation outcomes using a composite patient-level measure defined by the presence of any of the following 6 favorable indicators: (1) evidence of naloxone access; (2) absence of opioid overdose diagnoses and naloxone administration; (3) absence of emergency department (ED) visits or hospitalizations due to opioid overdose or opioid use disorder (OUD); (4) absence of overlapping opioid and benzodiazepine use within a 7-day window; (5) absence of opioid use ≥50 morphine milligram equivalent daily average; (6) receipt of referrals to non-pharmacological pain management. Additional quantitative metrics will include alert penetration, usage patterns, and clinical actions taken. Usability and acceptability will be assessed using a 12-item questionnaire for PCPs and semi-structured interviews. Expected Results: The trial will provide insights into real-world ML-driven CDS implementation and inform future strategies to reduce opioid-related harm. Full article

Although viral outbreaks are increasing, vaccination rates are decreasing. Our aim was to explain this baffling behavior that seems to contradict rational self-interest, and, thus, be beyond the purview of rational choice theories. We integrated fuzzy-trace theory and major theoretical alternatives and applied them to influenza, testing theoretical predictions in two samples: young adults (who are major viral vectors), N = 722, and community members, N = 185. Controlling for prior knowledge and other psychosocial factors that influence vaccination, explained variance jumped significantly when key predictors from fuzzy-trace theory were added, reaching 62% and 80% for vaccination intentions and 37% and 59% for behavior for each sample, respectively. Single items assessing global gist perceptions of risks and benefits achieved remarkable levels of diagnosticity. Key predictors were intuitive in that they were gisty, imprecise, and non-analytical. In contrast, rational system 2 measures—numeracy and cognitive reflection—were not predictive. These results provide new insights into why individuals vaccinate or not and new avenues for interventions to improve shared clinical decision-making. Full article

Background: Complex diabetic foot ulcers (DFUs) are a leading cause of morbidity and lower-limb amputation, and their management is profoundly challenging. Microvascular free tissue transfer is a primary limb salvage strategy, with the anterolateral thigh (ALT) free-flap recognized as a workhorse reconstructive solution. However, a quantitative summary of its performance specifically within this high-risk patient population is lacking. Methods: A systematic review and single-arm meta-analysis was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Five electronic databases (PubMed/MEDLINE, Embase, Scopus, Cochrane, and Web of Science Core Collection) were searched up to 9 September 2025 to identify studies reporting on outcomes of ALT free-flaps for diabetic foot reconstruction. The risk of bias was assessed using the Methodological Index for Non-Randomized Studies (MINORS) criteria. Primary outcomes were set as the complete and partial flap necrosis rate. Secondary outcomes included functional recovery status and complication rates. All data were synthesized using a random-effects model. Results: Six retrospective cohort studies met the inclusion criteria, including a total of 162 patients. The pooled rate of total flap failure was 5.2% (95% CI: 2.5–10.6%), a finding that was highly consistent across all studies (I² = 0%). The pooled incidence of partial flap necrosis was 13.0% (95% CI: 6.3–25.1%), resulting in an overall weighted flap success rate of 81.8%. Notably, the pooled rate of return to ambulation was 95.2% (95% CI: 88.5–98.1%), which also demonstrated no statistical heterogeneity (I² = 0%). Conclusions: The anterolateral thigh free-flap appears to be a robust and highly reliable strategy for diabetic foot reconstruction, associated with low failure rates, minimal long-term complications, and excellent functional recovery. However, the current evidence is limited to a small number of poor-to-moderate-quality retrospective studies. High-quality, prospective, and comparative multicenter trials are necessary to confirm these findings and establish the ALT flap’s effectiveness in high-risk cohorts. Full article