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The rapid evolution of digital health technologies, particularly within the Web 3.0 framework, has underscored eHealth literacy (eHL) as a critical competency for patients engaging with digital health care platforms. Patients in sustained hospital stays, often in vulnerable conditions, face unique challenges in using eHealth tools effectively. However, existing eHL assessment tools are insufficient to address the intricate and dynamic demands of contemporary health care systems, especially for individuals under continuous hospital care. This study aimed to develop the Adult Inpatient eHealth Literacy Scale (AIPeHLS), a comprehensive, multidimensional tool grounded in the Lily Model, to evaluate eHL among adult inpatients within the context of digital health care innovations. The development of the AIPeHLS followed a systematic, multiphase process. Initial item pool generation was informed by a literature review and then refined using the Delphi method, resulting in a preliminary set of 53 items spanning 6 dimensions of the Lily Model. The scale was refined through a pilot survey among 100 individuals requiring inpatient care, followed by item analysis and exploratory factor analysis (EFA). Validation was achieved via a cross-sectional study with 532 participants, using confirmatory factor analysis (CFA) to verify the scale structure, alongside evaluations of convergent, discriminant, criterion-related, and content validity. Reliability was assessed using Cronbach α, Omega, and split-half reliability. The finalized AIPeHLS comprised 44 items across 6 dimensions: traditional literacy, information literacy, media literacy, health literacy, computer literacy, and scientific literacy, reflecting the skills necessary in the Web 3.0 context. Both EFA and CFA confirmed the 6-factor structure, demonstrating acceptable model fit indices (χ²=1974.654 (df=887), root mean square error of approximation=0.048, comparative fit index=0.957, normed fit index=0.925, and incremental fit index=0.957). The scale exhibited robust content validity, convergent and discriminant validity, criterion-related validity, and high internal consistency, with a Cronbach α of .965, Omega coefficient of 0.962, and a split-half reliability of 0.791 for the entire scale. The 44-item AIPeHLS was found to be a reliable and valid instrument for assessing eHL in adult inpatients in the evolving Web 3.0 context. Its comprehensive framework and strong psychometric properties make it an effective tool for health care providers to understand patients' digital health competencies and tailor interventions accordingly. For researchers, our findings provided opportunities to explore the relationship between eHL and health outcomes, while offering valuable insights into the development of more effective eHealth interventions and policies.

Online questionnaires are widely used for large-scale screening. However, careless responding (CR) from participants can compromise the reliability of screening outcomes. Prior studies have focused on the effects of individual and environmental factors on CR, but the effect of questionnaire type remains underexplored. This study investigates the individual factors influencing CR in online mental health screening and assesses how the effect of these factors varies across different psychological questionnaires. This study analyzed data from 24,367 participants across 4 questionnaires (PHQ-9 [Patient Health Questionnaire-9], PSS [Perceived Stress Scale], ISI [Insomnia Severity Index], and GAD-7 [Generalized Anxiety Disorder-7 Scale]). CR was defined as the proportion of items completed in less than 2 seconds per item. We used a multiple linear regression model to examine the effect of individual factors (sex, age, education, smoking, and drinking) on CR across 4 questionnaires. In addition, response times were visualized to identify patterns between careless and careful responders. Females demonstrate lower levels of CR than males when completing the PHQ-9 (β=-.172, 95% CI -0.104 to -0.089; P<.001), PSS (β=-.234, 95% CI -0.162 to -0.14; P<.001), ISI (β=-.207, 95% CI -0.13 to -0.114; P<.001), and GAD-7 (β=-.177, 95% CI -0.108 to -0.093; P<.001). Older participants demonstrated lower levels of CR on the PHQ-9 (β=-.036, 95% CI -0.007 to -0.003; P<.001), ISI (β=-.036, 95% CI -0.007 to -0.003; P<.001), and GAD-7 (β=-.053, 95% CI -0.009 to -0.005; P<.001), but their age was unrelated to CR on the PSS. Interestingly, compared with participants with an associate-level education, those with a high education (bachelor's, master's, or doctoral degree) demonstrated higher levels of CR, especially those with a master's degree (PHQ-9: β=.098, 95% CI 0.136 to 0.188; P<.001 and GAD-7: β=.091, 95% CI 0.125 to 0.178; P<.001). Smokers exhibited varied patterns, with current smokers demonstrating lower levels of CR on the PHQ-9 (β=-.022, 95% CI -0.064 to -0.016; P=.001) and GAD-7 (β=-.014, 95% CI -0.051 to -0.002; P=.03), whereas occasional smokers demonstrated higher levels of CR on the PSS (β=.019, 95% CI 0.010 to 0.050; P=.003) than nonsmokers. Drinkers demonstrated lower levels of CR than nondrinkers, with the strongest effect among occasional drinkers on the PHQ-9 (β=-.163, 95% CI -0.103 to -0.087; P<.001). Analysis of response times revealed that participants tended to spend less time on PHQ-9 and GAD-7 surveys, and CR on PSS and ISI surveys was characterized by skipping questions. The effect of individual factors on CR varies across questionnaire types. These findings offer valuable insights for questionnaire designers and administrators, highlighting the need for targeted intervention.

Large language models have shown promise in transforming how complex scientific data are analyzed and communicated, yet their application to scientific domains remains challenged by issues of factual accuracy and domain-specific precision. The Laureate Institute for Brain Research-Tulsa University (LIBR-TU) Research Agent (LITURAt) leverages a sophisticated agent-based architecture to mitigate these limitations, using external data retrieval and analysis tools to ensure reliable, context-aware outputs that make scientific information accessible to both experts and nonexperts. The objective of this study was to develop and evaluate LITURAt to enable efficient analysis and contextualization of complex scientific datasets for diverse user expertise levels. An agent-based system based on large language models was designed to analyze and contextualize complex scientific datasets using a \"plan-and-solve\" framework. The system dynamically retrieves local data and relevant PubMed literature, performs statistical analyses, and generates comprehensive, context-aware summaries to answer user queries with high accuracy and consistency. Our experiments demonstrated that LITURAt achieved an internal consistency rate of 94.8% and an external consistency rate of 91.9% across repeated and rephrased queries. Additionally, GPT-4 evaluations rated 80.3% (171/213) of the system's answers as accurate and comprehensive, with 23.5% (50/213) receiving the highest rating of 5 for completeness and precision. These findings highlight the potential of LITURAt to significantly enhance the accessibility and accuracy of scientific data analysis, achieving high consistency and strong performance in complex query resolution. Despite existing limitations, such as model stability for highly variable queries, LITURAt demonstrates promise as a robust tool for democratizing data-driven insights across diverse scientific domains.

Aim To understand how functional traits and evolutionary history shape the geographic distribution of plant life on Earth, we need to integrate high‐quality and global‐scale distribution data with functional and phylogenetic information. Large‐scale distribution data for plants are, however, often restricted to either certain taxonomic groups or geographic regions. Range maps only exist for a small subset of all plant species and digitally available point‐occurrence information is biased both geographically and taxonomically. Floras and checklists represent an alternative, yet rarely used potential source of information. They contain highly curated information about the species composition of a clearly defined area, and together virtually cover the entire global land surface. Here, we report on our recent efforts to mobilize this information for macroecological and biogeographical analyses in the GIFT database, the Global Inventory of Floras and Traits. Location Global. Taxon Land plants (Embryophyta). Methods GIFT integrates plant distributions from regional Floras and checklists with functional traits, phylogenetic information, and region‐level geographic, environmental and socio‐economic data. It contains information about the floristic status (native, endemic, alien and naturalized) and takes advantage of the wealth of trait information in the regional Floras, complemented by data from global trait databases. Results GIFT 1.0 holds species lists for 2,893 regions across the whole globe including ~315,000 taxonomically standardized species names (i.e. c. 80% of all known land plant species) and ~3 million species‐by‐region occurrences. Based on a hierarchical and taxonomical derivation scheme, GIFT contains information for 83 functional traits and more than 2.3 million trait‐by‐species combinations and achieves unprecedented coverage in categorical traits such as woodiness (~233,000 spp.) or growth form (~213,000 spp.). Main conclusions Here, we present the structure, content and automated workflows of GIFT and a corresponding web‐interface (http://gift.uni-goettingen.de) as proof of concept for the feasibility and potential of mobilizing aggregated biodiversity data for global macroecological and biogeographical research.

Purpose: This research aims to implement Lean Manufacturing (LM) techniques and tools (T&T) in a retail distribution center located in Mariano Roque Alonso, Paraguay, targeting waste reduction and efficiency improvements in fresh and frozen product (FFP) handling to reduce long lead times.Design/methodology/approach: This study was conducted in two phases. First, a literature review revealed the key gaps to be addressed, the most commonly faced problems, and the LM tools applicable to solving these issues. Second, a case study was conducted in which LM T&T were applied to reduce the long lead time and propose solutions to address difficulties in the FFP process. Five problem-detection tools (flowchart, brainstorming, Ishikawa diagram, Pareto, and value stream mapping) and three improvement tools (kaizen, 5S, and Plan-Do-Check-Act) were applied to streamline FFP process from order generation to store reception.Findings: The analysis revealed significant inefficiencies: four were workforce-related, ten related to work methods, six environmental, and three product-related. Addressing these issues could substantially reduce operational bottlenecks and improve process throughput.Practical implications: The application of LM techniques significantly minimized waste, enhancing time management and human resource utilization, which led to a 96% improvement in FFP processing efficiency. These changes are expected to shorten lead times, bolster competitive advantage, and increase customer satisfaction.Originality/value: This study underscores the versatility of LM techniques, adapting them from manufacturing to retail distribution. The innovative combination of kaizen with 5S and PDCA offers a robust framework for ongoing improvements, promising for broader adoption in the retail sector.

The digital health literacy instrument (DHLI) was developed in 2017 to measure individuals' ability to access, understand, evaluate, and apply online health information. Since that time, digital health has shifted from desktop-based internet use to mobile devices, and there has been a rapidly expanding range of health apps. Additionally, heightened privacy and data security requirements have increased the complexity of user competencies needed to engage with digital health tools. These developments underscore the need to update the original DHLI. This study aimed to create an updated version of the DHLI (DHLI 2.0) that reflects current digital health practices and to examine its reliability and validity by exploring associations with user characteristics. Additionally, we aimed to develop a short-form version to facilitate broader use in research and practice. The instrument was iteratively updated and pilot-tested to retain the original theoretical framework while reflecting current digital health practices, devices, and emerging challenges such as mobile use and data security. Several items were reworded and a new 2-item subscale on digital safety was added. The full DHLI 2.0 comprises 24 items across 8 skill domains. A 16-item short form was developed by iteratively removing 1 or 2 items per subscale based on the \"α if item deleted\" criterion, while retaining the same subscale structure as the full form. Data to validate the new version of the instrument were collected in June 2024 through an online survey among members of a representative citizen panel in Friesland, a province in the Netherlands (N=2728). Sociodemographics, internet and health-related internet use, general health literacy (measured with the Single Item Literacy Screener), self-reported health, and health care use were assessed. Internal consistency was evaluated using Cronbach α, and construct validity was assessed via Spearman ρ correlations with related constructs. Internal consistency was high for both the full (α=0.94) and short-form (α=0.90) scales. Most subscales showed satisfactory to excellent reliability (α=0.71-0.93), while \"Securing privacy\" and \"Using security measures\" demonstrated moderate reliability (α=0.65-0.66). The DHLI 2.0 total scores were approximately normally distributed (skewness -0.5; kurtosis 0.4). As expected, digital health literacy was negatively correlated with age (ρ=-0.39, P<.001) and positively correlated with education (ρ=0.22, P<.001), income (ρ=0.27, P<.001), time spent online (ρ=0.32, P<.001), and general health literacy (ρ=-0.42, P<.001). The DHLI 2.0 provides an updated, reliable, and valid measure of digital health literacy covering 8 key domains, including data security. The 16-item short form offers a concise alternative suitable for research and possibly practical applications in health and eHealth contexts.

Creating search strategies for systematic reviews, finding the best balance between sensitivity and specificity, and translating search strategies between databases is challenging. Several methods describe standards for systematic search strategies, but a consistent approach for creating an exhaustive search strategy has not yet been fully described in enough detail to be fully replicable. The authors have established a method that describes step by step the process of developing a systematic search strategy as needed in the systematic review. This method describes how single-line search strategies can be prepared in a text document by typing search syntax (such as field codes, parentheses, and Boolean operators) before copying and pasting search terms (keywords and free-text synonyms) that are found in the thesaurus. To help ensure term completeness, we developed a novel optimization technique that is mainly based on comparing the results retrieved by thesaurus terms with those retrieved by the free-text search words to identify potentially relevant candidate search terms. Macros in Microsoft Word have been developed to convert syntaxes between databases and interfaces almost automatically. This method helps information specialists in developing librarian-mediated searches for systematic reviews as well as medical and health care practitioners who are searching for evidence to answer clinical questions. The described method can be used to create complex and comprehensive search strategies for different databases and interfaces, such as those that are needed when searching for relevant references for systematic reviews, and will assist both information specialists and practitioners when they are searching the biomedical literature.

Promoting personal agency may reduce perceived coercion and facilitate recovery in acute psychiatric care. However, no patient-reported tool currently exists to evaluate support for personal agency in this setting. This study aimed to develop a patient-reported tool (the Personal Agency Support Questionnaire [PASQ]) to assess perceived support for personal agency and to evaluate its psychometric properties among inpatients in acute psychiatric wards. We used a literature review and focus group interviews to generate a pool of items for the questionnaire, which was then refined using cognitive interviews and a pretest. We evaluated the construct validity, internal consistency, and test-retest reliability of the newly developed PASQ using a cross-sectional survey of inpatients in acute psychiatric wards. This study was conducted in collaboration with individuals who have lived experiences of mental illness. We analyzed data from 109 respondents (response rate: 109/178, 61.2%; mean age: 52.9, SD 16.9 years; women participants: 59/109, 54.1%; diagnosed with schizophrenia: 61/109, 56%). The 10-item PASQ demonstrated excellent convergent validity and acceptable discriminant validity. Internal consistency was high (Cronbach α=0.92), and test-retest reliability was moderate (intraclass correlation coefficient 0.68). This PASQ is a valuable tool for assessing personal agency support in acute psychiatric wards, demonstrating promise for both clinical use in acute psychiatric wards and clinical research.

The rapid development of large language models (LLMs) such as OpenAI's ChatGPT has significantly impacted medical research and education. These models have shown potential in fields ranging from radiological imaging interpretation to medical licensing examination assistance. Recently, LLMs have been enhanced with image recognition capabilities. This study aims to critically examine the effectiveness of these LLMs in medical diagnostics and training by assessing their accuracy and utility in answering image-based questions from medical licensing examinations. This study analyzed 1070 image-based multiple-choice questions from the AMBOSS learning platform, divided into 605 in English and 465 in German. Customized prompts in both languages directed the models to interpret medical images and provide the most likely diagnosis. Student performance data were obtained from AMBOSS, including metrics such as the \"student passed mean\" and \"majority vote.\" Statistical analysis was conducted using Python (Python Software Foundation), with key libraries for data manipulation and visualization. GPT-4 1106 Vision Preview (OpenAI) outperformed Bard Gemini Pro (Google), correctly answering 56.9% (609/1070) of questions compared to Bard's 44.6% (477/1070), a statistically significant difference (χ2₁=32.1, P<.001). However, GPT-4 1106 left 16.1% (172/1070) of questions unanswered, significantly higher than Bard's 4.1% (44/1070; χ2₁=83.1, P<.001). When considering only answered questions, GPT-4 1106's accuracy increased to 67.8% (609/898), surpassing both Bard (477/1026, 46.5%; χ2₁=87.7, P<.001) and the student passed mean of 63% (674/1070, SE 1.48%; χ2₁=4.8, P=.03). Language-specific analysis revealed both models performed better in German than English, with GPT-4 1106 showing greater accuracy in German (282/465, 60.65% vs 327/605, 54.1%; χ2₁=4.4, P=.04) and Bard Gemini Pro exhibiting a similar trend (255/465, 54.8% vs 222/605, 36.7%; χ2₁=34.3, P<.001). The student majority vote achieved an overall accuracy of 94.5% (1011/1070), significantly outperforming both artificial intelligence models (GPT-4 1106: χ2₁=408.5, P<.001; Bard Gemini Pro: χ2₁=626.6, P<.001). Our study shows that GPT-4 1106 Vision Preview and Bard Gemini Pro have potential in medical visual question-answering tasks and to serve as a support for students. However, their performance varies depending on the language used, with a preference for German. They also have limitations in responding to non-English content. The accuracy rates, particularly when compared to student responses, highlight the potential of these models in medical education, yet the need for further optimization and understanding of their limitations in diverse linguistic contexts remains critical.

The rising prevalence of dementia necessitates a scalable solution to cognitive assessments. The Autonomous Cognitive Examination (ACoE) is a foundational cognitive test for the phenotyping of cognitive symptoms across the primary cognitive domains. However, while the ACoE has been internally validated, it has not been externally validated in a clinical population, and its ability to render accurate appraisals of cognition is unknown. Further, it is unclear if these phenotypic assessments are useful in clinical tasks such as screening patients with and those without impairments. The objective of this study is to validate the ability of the ACoE to reliably phenotype cognition and to act as a screening examination relative to standard paper-based tests. To compare the evaluations of the ACoE to established paper-based tests, 46 patients with neurological disorders were enrolled in a randomized crossover study and received either the ACoE or a standard paper-based cognitive test. Patients received either the Addenbrooke Cognitive Examination-3 (ACE-3; n=35) or the Montreal Cognitive Examination (MoCA; n=11). We evaluated 3 primary metrics of the ACoE's performance relative to paper-based tests: (1) interrater reliability of overall cognitive scores, (2) interrater reliability of cognitive domain scores, and (3) ability to classify patients similarly to paper-based tests. The ACoE's overall cognitive assessments were significantly reliable (ICC [intraclass correlation coefficient]=0.89; P<.001). Each cognitive domain's assessments were also significantly reliable, including attention (ICC=0.74; PFWE<.001), language (ICC=0.89; PFWE<.001), memory (ICC=0.91; PFWE<.001), fluency (ICC=0.74; PFWE<.001), and visuospatial function (ICC=0.78; PFWE<.001). The ACoE was also able to successfully diagnose patients similarly to both paper-based tests (area under the receiver operating characteristic curve=0.96; PFWE<.001). In this study, we evaluated if the ACoE could reliably phenotype cognitive symptoms relative to the assessments of established standard paper-based cognitive assessments. We found that the ACoE reliably phenotypes patient cognition, which can be used to screen patients. In the future, these cognitive phenotypes may be used to diagnose specific etiologies.