Yoxas/statistical_literacy huggingface dataset:
The Virtual Access to STEM Careers (VASC) project is an intertwined classroom and virtual reality (VR) curricular program for third through fourth graders. Elementary school students learn about and take on the roles and responsibilities of STEM occupations through authentic, problem-based tasks with physical kits and immersive VR environments. This article reports on a round of curriculum and virtual environment development and in-classroom experimentation that was guided by preliminary results gathered from our initial VASC prototyping and testing. This specific iteration focuses on curriculum for learning about sea turtles and tasks regularly completed by park rangers and marine biologists who work with these creatures and a new backend data collection component to analyze participant behavior. Our results showed that educators were able to setup and integrate VASC into their classrooms with relative ease. Elementary school students were able to learn how to interface with our system quickly and enjoyed being in the environment, making a positive link to STEM education.
The Vision and Change report called for biology educators to transform undergraduate biology education. The report recommended educators transparently state what students should know and be able to do and create assessments to measure student learning. Using backward design, learning objectives (LOs) can serve as the basis for course transformation. In this essay, we present a roadmap for planning successful course transformations synthesized from the literature. We identified three categories of critical features for successful course transformation. First, establishing a sense of urgency and offering faculty incentives to engage in this time-consuming work creates a needed climate for change. Second, departments are empowered in this process by including key stakeholders, building faculty teams to work collaboratively to identify LOs used to drive pedagogical change, develop assessment strategies, and engage in professional development efforts to support the process. Third, there must be intentional effort to manage resistance and ensure academic freedom and creativity in the classroom. General recommendations as well as areas for further research are discussed.
The volume of diagnostic imaging studies performed in the United States is rapidly increasing resulting from an increase in the number of patients as well as an increase in the volume of studies per patient. Concurrently, the number and complexity of images in each patient data set are also increasing. Nuclear medicine physicians and radiologists are required to master an ever-expanding knowledge base whereas the hours available to master this knowledge base and apply it to specific tasks are steadily shrinking. The convergence of an expanding knowledge base and escalating time constraints increases the likelihood of physician errors. The problem is particularly acute for low-volume studies such as MAG3 diuresis renography where many imagers may have had limited training or experience. To address this problem, renal decision support systems (DSS) are being developed to assist physicians evaluate suspected obstruction in patients referred for diuresis renography. Categories of DSS include neural networks, case-based reasoning, expert systems and statistical systems; RENEX and CART are examples of renal DSS currently in development. RENEX (renal expert) uses a set of rules obtained from human experts to analyze a knowledge base of expanded quantitative parameters obtained from diuresis MAG3 scintigraphy whereas CART (classification and regression tree analysis) is a statistical method that grows and prunes a decision tree based on an analysis of these quantitative parameters in a training data set. RENEX can be queried to provide the reasons for its conclusions. Initial data show that the interpretations provided by RENEX and CART are comparable to the interpretations of a panel of experts blinded to clinical information. This project should serve as a benchmark for the scientific comparison and collaboration of these 2 fields of medical decision-making. Moreover, we anticipate that these DSS will better define the essential interpretative criteria, foster standardized interpretation, teach trainees to better interpret renal scans, enhance diagnostic accuracy and provide a methodology applicable to other diagnostic problems in radiology and medicine.
The volume of literature about serious gaming in dental education has increased, however, none of the previous studies have developed a serious game for closing the gap between preclinical and clinical training. Virtual Dental Clinic (VDC) is a serious game that was created to help develop clinical reasoning skills in dental students. This study aimed to evaluate VDC as an educational tool and its effectiveness on clinical skill and knowledge gain among clerkship dental students. The following three stages of VDC design and testing were addressed from 2016 to 2020: development, validation, and application. The VDC was developed using Unity game engine. In the validation stage, the content validity was reviewed by five visiting staff; construct validity and face validity were examined by 9 postgraduate-year dentists and 14 clerkship dental students. Concurrent validity and predictive validity were examined by 34 fifth-year dental students during their clerkship from September, 2018 to May, 2019, the associations between VDC experiences, clerkship performance, and the score on a national qualification test were explored. In the application stage, the VDC was set up as a self-learning tool in the Family Dentistry Department from August, 2019, quantitative and qualitative analyses were conducted using the 92 clerkship students' feedback. The VDC showed good validity and a high potential for education in practice. Students who have used VDC received significantly higher scores on qualification test (p = 0.029); the VDC experiences significantly predicted higher performance score on periodontics (p = 0.037) and endodontics (p = 0.040). After the outbreak of COVID-19 pandemic, significantly higher proportion of students confirmed the value of VDC as an assistant tool for learning clinical reasoning (p = 0.019). The VDC as an educational tool, and the effectiveness on clinical reasoning skills and knowledge gain among clerkship dental students has been validated and confirmed in this study.
The volunteers approached for participation in a clinical trial should be given detailed and understandable information about the study through an informed consent form (ICF) before enrollment. In this study, we evaluated clinical trial files submitted to the Turkish Medicines and Medical Devices Agency (TITCK) to investigate the compliance to legal legislation and readability of ICFs as well as the factors affecting them. This is a descriptive, cross-sectional study. We evaluated 160 ICFs in the phase II-IV clinical trial files submitted to TITCK in 2016 to determine their compliance to legislation (n = 160) and to assess their readability (n = 152) using Atesman formula. Overall compliance score was calculated. ICFs were also evaluated in terms of written format (font size, line spacing, section headings) and page count. Statistical analysis was performed with chi-square, Student's t test, analysis of variance, Mann-Whitney U, and Kruskal Wallis analysis. Compliance to legislation and suitability of written format of international trial ICFs were significantly higher than those of national trial ICFs. Most of the national trials were investigator initiated. Readability was low in both national and international trial ICFs where the text was longer in the latter. Results showed that researchers need easy-to-read ICF writing training that fits legal regulations.
The Wealth Accumulation and Losses in Later life Early Cognitive Transitions (WALLET) study data was used to examine correlates with excess spending in older adults who do and do not have early memory loss. The WALLET study collected detailed financial information from participants' primary checking account statements (n = 150). Information on participant sociodemographic, health, and disability status, memory functioning, financial decision-making, and financial literacy was also collected. Participants either had no memory problems or early memory loss. Bivariate and multiple regression analyses were conducted. The early memory loss group had significantly higher excess spending than those with no memory loss. Financial decision-making and higher-risk financial behaviors were also linked to higher excess spending. Early memory loss was no longer statistically significant after accounting for financial stressors and resources. The multidimensional nature of financial capacity assessment has long been known. The WALLET study data is unique, however, in that it demonstrates the links between excess spending with decision-making, early memory loss, and a set of specific financial behaviors. Real-world assessments of financial management and financial decision-making yield important information about how older adults are managing money and making key financial decisions. Checking account reviews can be used to determine excess spending.
The Web is an important source of health information, but the quality of such online information is highly variable. This study evaluates the quality of health articles published on Naver News, Korea's most popular portal, using the Health Information Quality Assessment Tool (HIQUAL). We collected 712 health-related articles published on Naver News from May 1 to 7, 2023. After applying exclusion criteria, we selected 116 articles for analysis. Two clinicians independently assessed the quality of these articles using the HIQUAL, which scores articles based on five domains: "reliability," "usefulness," "understandability," "sufficiency," and "transparency." Overall article quality was generally considered recommendable (mean±standard deviation: 7.52±2.00). "Usefulness," one item of "reliability," and "understandability" were the three items with the highest levels of satisfaction. "Sufficiency" criteria for costs, risks, and benefits received low scores. Quality scores for articles focused on health risk factors and intervention showed a statistically significant difference. While the overall quality of health information in Korean online newspaper articles is acceptable, room for improvement remains in some areas, particularly with regard to the fair presentation of costs, risks, and benefits. The study highlights the need for ongoing quality improvement and evaluation initiatives for online health information.
The WHO Global Antimicrobial Resistance Surveillance System (GLASS) aims to describe antimicrobial resistance (AMR) patterns and trends in common bacterial pathogens, but data remain limited in many low and middle-income countries including Indonesia. We systematically searched Embase, PubMed and Global Health Database and three Indonesian databases for original peer-reviewed articles in English and Indonesian, published between January 1, 2000 and May 25, 2023, that reported antimicrobial susceptibility for the 12 GLASS target pathogens from human samples. Pooled AMR prevalence estimates were calculated for relevant pathogen-antimicrobial combinations accounting for the sampling weights of the studies (PROSPERO: CRD42019155379). Of 2182 search hits, we included 102 papers, comprising 19,517 bacterial isolates from hospitals (13,647) and communities (5870). In hospital settings, 21.6% of Klebsiella pneumoniae isolates, 18.3% of Escherichia coli isolates, 35.8% of Pseudomonas aeruginosa isolates and 70.7% of Acinetobacter baumannii isolates were carbapenem-resistant; 29.9% of Streptococcus pneumoniae isolates were penicillin-resistant; and 22.2% of Staphylococcus aureus isolates were methicillin-resistant. Hospital prevalence of carbapenem-resistant K. pneumoniae and E. coli, and penicillin-resistant S. pneumoniae increased over time. In communities, 28.3% of K. pneumoniae isolates and 15.7% of E. coli isolates were carbapenem-resistant, 23.9% of S. pneumoniae isolates were penicillin-resistant, and 11.1% of S. aureus isolates were methicillin-resistant. Data were limited for the other pathogens. AMR prevalence estimates were high for critical gram-negative bacteria. However, data were insufficient to draw robust conclusions about the full contemporary AMR situation in Indonesia. Implementation of national AMR surveillance is a priority to address these gaps and inform context-specific interventions. Wellcome Africa Asia Programme Vietnam.
The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.
The widespread adoption of Competency-Based Medical Education (CBME) has resulted in a more explicit focus on learners' abilities to effectively demonstrate achievement of the competencies required for safe and unsupervised practice. While CBME implementation has yielded many benefits, by focusing explicitly on what learners are doing, curricula may be unintentionally overlooking who learners are becoming (i.e., the formation of their professional identities). Integrating professional identity formation (PIF) into curricula has the potential to positively influence professionalism, well-being, and inclusivity; however, issues related to the definition, assessment, and operationalization of PIF have made it difficult to embed this curricular imperative into CBME. This paper aims to outline a path towards the reconciliation of PIF and CBME to better support the development of physicians that are best suited to meet the needs of society. To begin to reconcile CBME and PIF, this paper defines three contradictions that must and can be resolved, namely: (1) CBME attends to behavioral outcomes whereas PIF attends to developmental processes; (2) CBME emphasizes standardization whereas PIF emphasizes individualization; (3) CBME organizes assessment around observed competence whereas the assessment of PIF is inherently more holistic. Subsequently, the authors identify curricular opportunities to address these contradictions, such as incorporating process-based outcomes into curricula, recognizing the individualized and contextualized nature of competence, and incorporating guided self-assessment into coaching and mentorship programs. In addition, the authors highlight future research directions related to each contradiction with the goal of reconciling 'doing' and 'being' in medical education.
The widespread application of high-throughput sequencing technology has generated massive sequences of B-cell receptor (BCR) immune repertoires. Computational analysis of these data has gained significant attention due to the increasing importance of immunotherapy and precision medicine. It not only reveals the diversity and dynamic changes in immune responses, contributing to the study of associated diseases, but also provides valuable information for immunodiagnostics and drug development. Recently, we introduced a BCR-specific multiple sequence alignment (MSA) method along with a comprehensive platform software called Abalign, which stands out as an excellent choice for analyzing BCR immune repertoires due to its unique high-throughput processing capability. It offers ultra-fast MSA functionality and a wide range of analytical features, including BCR/antibody extraction, clonal grouping, lineage tree construction, mutation profiling, diversity statistics, VJ gene assignment, antibody humanization, and more. Importantly, users can perform these analyses using the graphical user interface without any programming skills or scripts. In this article, we present a series of protocols that integrate Abalign's analysis modules into a cohesive workflow. This step-by-step workflow provides detailed instructions for software installation, data preparation, and comprehensive analysis of BCR immune repertoires. This workflow facilitates the efficient acquisition of comprehensive results in profiling BCR immune repertoires, offering insights into the impacts of infectious diseases, allergies, autoimmune disorders, tumor immunology, and antibody drugs. Abalign is freely available at http://cao.labshare.cn/abalign/. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Resource preparation Basic Protocol 2: Analyzing BCR immune repertoires Support Protocol 1: Aiding antibody humanization Support Protocol 2: Constructing B-cell lineage trees Alternate Protocol: Running with Linux command line Basic Protocol 3: Comparing BCR immune repertoires.
The widespread presence of micro(nano)plastics (MNPs) in the environment threatens ecosystem integrity, and thus, it is necessary to determine and assess the occurrence, characteristics, and transport of MNPs between ecological components. However, most analytical approaches are cost- and time-inefficient in providing quantitative information with sufficient detail, and interpreting results can be difficult. Alternative analyses integrating novel measurements by imaging or proximal sensing with signal processing and machine learning may supplement these approaches. In this review, we examined published research on methods used for the automated data interpretation of MNPs found in the environment or those artificially prepared by fragmenting bulk plastics. We critically reviewed the primary areas of the integrated analytical process, which include sampling, data acquisition, processing, and modeling, applied in identifying, classifying, and quantifying MNPs in soil, sediment, water, and biological samples. We also provide a comprehensive discussion regarding model uncertainties related to estimating MNPs in the environment. In the future, the development of routinely applicable and efficient methods is expected to significantly contribute to the successful establishment of automated MNP monitoring systems.
The widespread use of artificial intelligence, such as ChatGPT (OpenAI), is transforming sectors, including health care, while separate advancements of the internet have enabled platforms such as China's DingXiangYuan to offer remote medical services. This study evaluates ChatGPT-4's responses against those of professional health care providers in telemedicine, assessing artificial intelligence's capability to support the surge in remote medical consultations and its impact on health care delivery. We sourced remote orthopedic consultations from "Doctor DingXiang," with responses from its certified physicians as the control and ChatGPT's responses as the experimental group. In all, 3 blindfolded, experienced orthopedic surgeons assessed responses against 7 criteria: "logical reasoning," "internal information," "external information," "guiding function," "therapeutic effect," "medical knowledge popularization education," and "overall satisfaction." We used Fleiss κ to measure agreement among multiple raters. Initially, consultation records for a cumulative count of 8 maladies (equivalent to 800 cases) were gathered. We ultimately included 73 consultation records by May 2023, following primary and rescreening, in which no communication records containing private information, images, or voice messages were transmitted. After statistical scoring, we discovered that ChatGPT's "internal information" score (mean 4.61, SD 0.52 points vs mean 4.66, SD 0.49 points; P=.43) and "therapeutic effect" score (mean 4.43, SD 0.75 points vs mean 4.55, SD 0.62 points; P=.32) were lower than those of the control group, but the differences were not statistically significant. ChatGPT showed better performance with a higher "logical reasoning" score (mean 4.81, SD 0.36 points vs mean 4.75, SD 0.39 points; P=.38), "external information" score (mean 4.06, SD 0.72 points vs mean 3.92, SD 0.77 points; P=.25), and "guiding function" score (mean 4.73, SD 0.51 points vs mean 4.72, SD 0.54 points; P=.96), although the differences were not statistically significant. Meanwhile, the "medical knowledge popularization education" score of ChatGPT was better than that of the control group (mean 4.49, SD 0.67 points vs mean 3.87, SD 1.01 points; P<.001), and the difference was statistically significant. In terms of "overall satisfaction," the difference was not statistically significant between the groups (mean 8.35, SD 1.38 points vs mean 8.37, SD 1.24 points; P=.92). According to how Fleiss κ values were interpreted, 6 of the control group's score points were classified as displaying "fair agreement" (P<.001), and 1 was classified as showing "substantial agreement" (P<.001). In the experimental group, 3 points were classified as indicating "fair agreement," while 4 suggested "moderate agreement" (P<.001). ChatGPT-4 matches the expertise found in DingXiangYuan forums' paid consultations, excelling particularly in scientific education. It presents a promising alternative for remote health advice. For health care professionals, it could act as an aid in patient education, while patients may use it as a convenient tool for health inquiries.
The willingness to consume healthy foods has highlighted the growing importance of health, even more so when it comes to food choice, and predicting the willingness to consume foods of a healthy brand represents an action that leads to the practice of conscious eating habits, but what is behind this willingness? To answer this question and based on previous studies such as the theory of planned behavior and nutritional literacy, this study aimed to build a predictive model through an empirical study to examine the influence of nutritional literacy (NL) on attitude (ATT), subjective norm (SN) and perceived behavioral control (PBC), as well as to determine the influence of the three variables of the theory of planned behavior (TPB) on the willingness to consume healthy brand foods (WCHBF) in the Peruvian market. The research focused on the population that stated that they were consumers of the Unión brand (a brand whose value proposition is the sale of healthy foods), obtaining 482 consumers. The study was conducted under a quantitative, non-experimental, cross-sectional design approach. The results support the existence of a positive and significant effect of NL on ATT, SN, and PBC, finding the exact behavior of SN and PBC in WCHBF; however, in the proposed model, it is observed that ATT has no impact on WCHBF. Applying strategies that lead to a change in consumer behavior towards healthy brands is a matter of time and will. In this context, the findings indicate that nutritional literacy plays an essential role in the willingness to consume healthy foods, which sheds more light on the design of educational interventions and awareness campaigns that independently inform about nutritional benefits and empower consumers, allowing them to make informed and healthy choices.