Yoxas/statistical_literacy huggingface dataset:
As elsewhere, disadvantaged children in Senegal are those most affected by dental diseases and difficulties in obtaining dental care. Studies conducted mainly in developed countries suggest that a low level of mothers' OHL is correlated with poor oral health of their children. The objective of this study is to estimate the level of mothers' OHL in Senegal and its relationto the dental health of their children. This cross-sectional epidemiological survey took place among 315 children aged from 3 to 9 years old and their mothers. It estimated the children's dental health status by clinical examination which used a disposable examination kit and a headlamp, took place at the child's home, in the mother's presence. Examiners interviewing the mothers administered the Oral Health Literacy-Adult Questionnaire to determine their OHL and questioned them further about their social characteristics and their children's dental health behaviour. Logistic regression and correlations were used for the statistical analysis. The OHL score ranges from 0 to 17; mothers' mean score was 6.5 (±3.1) and 56.5% had a low score (below the median). The prevalence of dental caries in children was 64.8%. Mothers' high OHL is associated with children caries free and low prevalence of dental caries. The logistic regression showed a significantly protective relation between children's dental caries and mothers' high OHL scores (mean score 12-17) (OR = 0.51, 95% CI: 0.29-0.88), high educational level (OR = 0.42, 95% CI: 0.23-0.76) and a high level of social contact (OR = 0.31, 95% CI: 0.15-0.63). The structural analysis showed that OHL was significantly correlated with both the mothers' social position (r = 0.61 and P<0.001) and the children's caries (r = -0.26 and P<0.001). The OHL level of Senegalese mothers was significantly associated with their children's dental caries. Improving mothers' OHL might therefore help strengthen their capacities to promote oral health, thus helping to improve their children's dental health and reduce inequalities.
As flipped classroom has received much attention from researchers and educators, some scholars have investigated the effectiveness of this teaching mode in various English as a foreign language (EFL) settings; however, such an instruction mode has been under-investigated in the Chinese EFL context. Therefore, the current study examined a flipped course's impact on Chinese EFL learners' writing performance and anxiety utilizing a pretest-posttest non-equivalent group quasi-experimental design. First, it selected a sample of 50 Chinese EFL learners from two intact language school classes as the participants via the convenience sampling method. Then, it randomly assigned participants of these two intact classes to a control group (n = 24), taught based on the traditional method of writing instruction, and an experimental group (n = 26), instructed based on social media-supported flipped instruction. The study used two writing tasks and a writing anxiety inventory to gather the data from the participants. The descriptive and inferential statistics results showed that the experimental group-taught based on flipped writing instruction-significantly enhanced their writing performance. Moreover, they revealed that the flipped course substantially reduced participants' writing anxiety. Implications of such findings have been elaborated for EFL research and practice.
As healthcare and especially health technology evolve rapidly, new challenges require healthcare professionals to take on new roles. Consequently, the demand for health informatics competencies is increasing, and achieving these competencies using frameworks, such as Technology Informatics Guiding Reform (TIGER), is crucial for future healthcare. The study examines essential health informatics and educational competencies and health informatics challenges based on TIGER Core Competency Areas. Rather than examine each country independently, the focus is on uncovering commonalities and shared experiences across diverse contexts. Six focus group interviews were conducted with twenty-one respondents from three different countries (Germany (n = 7), Portugal (n = 6), and Finland (n = 8)). These interviews took place online in respondents' native languages. All interviews were transcribed and then summarized by each country. Braun and Clarke's thematic analysis framework was applied, which included familiarization with the data, generating initial subcategories, identifying, and refining themes, and conducting a final analysis to uncover patterns within the data. Agreed upon by all three countries, competencies in project management, communication, application in direct patient care, digital literacy, ethics in health IT, education, and information and knowledge management were identified as challenges in healthcare. Competencies such as communication, information and communication technology, project management, and education were identified as crucial for inclusion in educational programs, emphasizing their critical role in healthcare education. Despite working with digital tools daily, there is an urgent need to include health informatics competencies in the education of healthcare professionals. Competencies related to application in direct patient care, IT-background knowledge, IT-supported and IT-related management are critical in educational and professional settings are seen as challenging but critical in healthcare.
As healthcare continues evolving in the age of digital technology, the integration of artificial intelligence has emerged as a powerful force, particularly in wound care. The education of healthcare professionals in wound care is crucial for ensuring they acquire the necessary knowledge and skills, optimizing patient outcomes. This paper outlines the protocol for a scoping review with the goal of mapping and analyzing the current scientific evidence regarding the potential impact of artificial intelligence in wound care education. The current protocol follows the JBI methodological framework. The search was conducted in December 2023 in the following databases: CINAHL Complete (via EBSCOhost), MEDLINE (via PubMed), Cochrane Library, Academic Search Complete, Scientific Electronic Library Online (Scielo), Scopus, and Web of Science. Electronics searches were conducted in the Scientific Open Access Scientific Repositories of Portugal (RCAAP) and ProQuest Dissertations and Theses, OpenAIRE, and Open Dissertations databases to access gray literature. Additionally, searches were performed on Google Scholar and specific journals such as the International Wound Journal, Skin Research and Technology, Journal of Wound Care, and Wound Repair and Regeneration. The initial database searches retrieved a total of 11,323 studies. After removing duplicates, a total of 6450 studies were submitted for screening. Currently, 15 studies are included in this review, and data charting and analysis are underway. The findings of this scoping review will likely provide insights into the application of artificial intelligence in wound care education.
As hemodialysis is a long-term treatment method requiring significant self-management skills, it affects both the patient and the caregiver in many ways. It is inevitable that changes in the burden perceived by the caregiver over time will not affect the patient's health care outcomes. The aim of this study was to develop an up-to-date scale by examining the psychometric properties of items created specifically for the care burden perceived by individuals who provide care for patients receiving hemodialysis treatment. This study is a methodological study with the use of the COSMIN Checklist. The sample of the study included 404 individuals who were the primary caregivers of patients receiving hemodialysis treatment. Data were collected using descriptive characteristics form and the draft form of the Primary Caregiver Burden Scale Individuals Receiving Hemodialysis Treatment. Exploratory and confirmatory factor analysis and predictive validity were used to evaluate validity. The Cronbach's alpha reliability coefficient, item analysis, and test-retest method were employed to evaluate reliability. According to exploratory factor analysis, a scale structure with four subdimensions was determined, and goodness of fit was achieved using confirmatory factor analysis. The Cronbach's alpha coefficient of the final form of the scale consisting of 34 items was found to be 0.95 for the total scale, 0.93 for the self-management support subscale, 0.91 for the psychological support subscale, 0.91 for the caregiver symptom subscale, and 0.93 for the caregiver individual coping subscale. The Primary Caregiver Burden Scale Individuals Receiving Hemodialysis Treatment, which was found to meet the validity and reliability criteria, has a four-point Likert-type scoring structure, 34 items, and four subdimensions. It is thought that the scale can make significant contributions to the international literature if its validity and reliability are established with nursing practices and research.
As Industry 4.0 debuted roughly a decade ago, it is now necessary to examine how it affects various aspects of the discipline. It is the responsibility of the education sector to guarantee that the next generation is equipped mentally, physically, and cognitively to face unforeseen challenges. Numerous educational institutions are outfitted with Industry 4.0 technology-based learning. Industry 4.0 fosters advancements in learning methodologies, especially for language enhancements. Learners may gain knowledge at their base, providing them an opportunity for independent study. The majority of subjects have been acquired through Industry 4.0. This research chapter explores the intersection of Industry 4.0 and education, specifically focusing on the SmallTalk AI tool. It investigates how technological and digital innovations within the context of Industry 4.0 can serve as powerful tools to enhance language learning outcomes. This article presents a comprehensive analysis of statistical data and empirical evidence to support the positive impact of Industry 4.0 technology of SmallTalk on language acquisition particularly speaking. The study also determines the relationship among participants' usage through the technology acceptance model (TAM). Furthermore, it examines the challenges and opportunities associated with integrating these innovations into language learning pedagogies, offering insights for educators and policymakers to harness the potential of Industry 4.0 in fostering language proficiency. The research employs quantitative analysis. The data obtained from educational institutions has been analyzed using the SPSS and AMOS software. The results indicate that Industry 4.0 has had an important effect on English language acquisition. This self-supported adaptable system of education facilitates effective student learning. This study also suggests that future research into the utility of Industry 4.0 be conducted elsewhere internationally.
As mathematical competence is linked to educational success, professional achievement, and even a country's economic growth, researchers have been interested in early predictors for quite some time. Although there have been numerous studies on domain-specific numerical abilities predicting later mathematical competence in preschool children, research in toddlers is scarce, especially regarding additional influential aspects, such as domain-general cognitive abilities and the children's social background. Using a large-scale dataset, the present study examined predictive effects of numeracy skills in 17-month-olds for later mathematical achievement. We found small, positive effects, even when controlling for child-related variables (i.e., age and sex) and the children's social background (i.e., maternal education and household language). Additionally, we compared results with a domain-general categorization task and found no distinct effect on mathematical competence. The present results are discussed with regard to the specificities of the dataset, as well as implications for future studies on predictors of mathematical competence.
As medical educators grapple with the consistent demand for high-quality assessments, the integration of artificial intelligence presents a novel solution. This how-to article delves into the mechanics of employing ChatGPT for generating Multiple Choice Questions (MCQs) within the medical curriculum. Focusing on the intricacies of prompt engineering, we elucidate the steps and considerations imperative for achieving targeted, high-fidelity results. The article presents varying outcomes based on different prompt structures, highlighting the AI's adaptability in producing questions of distinct complexities. While emphasizing the transformative potential of ChatGPT, we also spotlight challenges, including the AI's occasional "hallucination", underscoring the importance of rigorous review. This guide aims to furnish educators with the know-how to integrate AI into their assessment creation process, heralding a new era in medical education tools.
As medical schools move to integrate the Core Entrustable Professional Activities for Entering Residency (EPAs) into curricula and address the transition from student to resident, residency preparatory courses have become more prevalent. The authors developed an experiential learning EPA-based capstone course for assessment to determine impact on learner self-assessed ratings of readiness for residency and acquisition of medical knowledge. All fourth-year students from the classes of 2018-2020 completed a required course in the spring for assessment of multiple EPAs, including managing core complaints, performing basic procedures, obtaining informed consent, and providing patient handoffs. Learners selected between three specialty-based parallel tracks - adult medicine, surgery, or pediatrics. Students completed a retrospective pre-post questionnaire to provide self-assessed ratings of residency preparedness and comfort in performing EPAs. Finally, the authors studied the impact of the course on knowledge acquisition by comparing student performance in the adult medicine track on multiple choice pre- and post-tests. Four hundred and eighty-one students were eligible for the study and 452 (94%) completed the questionnaire. For all three tracks, there was a statistically significant change in learner self-assessed ratings of preparedness for residency from pre- to post-course (moderately or very prepared: adult medicine 61.4% to 88.6% [p-value < 0.001]; surgery 56.8% to 81.1% [p-value < 0.001]; pediatrics 32.6% to 83.7% [p-value 0.02]). A similar change was noted in all tracks in learner self-assessed ratings of comfort from pre- to post-course for all studied EPAs. Of the 203 students who participated in the adult medicine track from 2019-2020, 200 (99%) completed both the pre- and post-test knowledge assessments. The mean performance improved from 65.0% to 77.5% (p-value < 0.001). An experiential capstone course for the assessment of EPAs can be effective to improve learner self-assessed ratings of readiness for residency training and acquisition of medical knowledge.
As more people across Generations X, Y, and Z continue to use the Internet meet their information needs, this study assesses the characteristics influencing the online health information seeking behaviour (OHISB) of non-medical university students. Previous studies on the subject in Nigeria have largely assessed the concept of OHISB among university students without examining the determinants of such behaviour. To determine the predictors of OHISB among non-medical undergraduate students of a Nigerian private university. Adopting a cross-sectional research design, the study utilized a semi-structured questionnaire administered via Google forms. Some 361 non-medical undergraduate students were recruited purposively with a response rate of 84.2%. The questionnaire obtained data on students' socio-demographics, Internet usage, and OHISB. Presence of a chronic condition, gender, and college showed significant associations with students' OHISB. 'Mothers' occupation' and 'prior experience' became significant predictors of students' OHISB. The results align with studies that stressed that chronically ill persons would search for health information on the internet and that presence of chronic illness is among the predictors of OHISB. These findings add to evidence from other countries that had identified health literacy and presence of chronic illness as significant predictors of OHISB among students.
As of 2020, 20% of people residing in the United States of America (U.S.) lived in rural communities. Despite rural residents tending to be older, poorer, and having greater disease burden than their urban counterparts, the number of rural primary care providers continues to decline. Nearly 66% of U.S. Primary Care Health Professional Shortage Areas are designated as rural. Pharmacists can help address this shortage of rural primary care providers, often serving as providers of first-contact care; however, only 12% of U.S. pharmacists practice in rural communities. To help address this gap, in 2022 an elective Rural Pharmacy course was created at the University of Minnesota College of Pharmacy by a faculty member who has rural practice experience. The course combines formal lectures, guest presentations by rural pharmacists and student interviews with additional rural pharmacists. For the 42 students enrolled in the course in 2022 and 2023, non-parametric statistics were used to compare the percentage of students who were raised in rural communities or who otherwise had extensive exposure to rural, and compare student interest ratings (1 to 7) about practicing/living rural at the beginning and end of the course. Students also wrote end-of-course reflection papers, commenting on the course and their interviews with rural pharmacists. Across both years, 45% of the enrolled students had previous experience in rural communities. The net change in Rural Interest scores among students completing both questionnaires was + 5 in 2022 and + 2 in 2023, both non-significant differences. The largest shifts in student interest were from "Not Sure" at the start of the course to "Interested" or "Not Interested" at the end of the course, and from "Interested" to "Very Interested." In their reflection papers nearly 60% of students reported being most impressed by their interviews with rural pharmacists. A course addressing the benefits and challenges of practicing pharmacy in rural communities was well-received by pharmacy students. Even students who have little interest in living in a rural community can benefit from being introduced to rural culture, enabling them to provide more culturally-responsive care for patients from rural communities.
As of today, data analysis focuses primarily on the findings to be made inside the data and concentrates less on how those findings relate to the domain of investigation. Contemporary visualization as a field of research shows a strong tendency to adopt this data-centrism. Despite their decisive influence on the analysis result, qualitative aspects of the analysis process such as the structure, soundness, and complexity of the applied reasoning strategy are rarely discussed explicitly. We argue that if the purpose of visualization is the provision of domain insight rather than the depiction of data analysis results, a holistic perspective requires a qualitative component to to be added to the discussion of quantitative and human factors. To support this point, we demonstrate how considerations of qualitative factors in visual analysis can be applied to obtain explanations and possible solutions for a number of practical limitations inherent to the data-centric perspective on analysis. Based on this discussion of what we call qualitative visual analysis, we develop an inside-outside principle of nested levels of context that can serve as a conceptual basis for the development of visualization systems that optimally support the emergence of insight during analysis.
As one of many fundamental sports techniques, the landing maneuver is also frequently used in clinical injury screening and diagnosis. However, the landing patterns are different under different constraints, which will cause great difficulties for clinical experts in clinical diagnosis. Machine learning (ML) have been very successful in solving a variety of clinical diagnosis tasks, but they all have the disadvantage of being black boxes and rarely provide and explain useful information about the reasons for making a particular decision. The current work validates the feasibility of applying an explainable ML (XML) model constructed by Layer-wise Relevance Propagation (LRP) for landing pattern recognition in clinical biomechanics. This study collected 560 groups landing data. By incorporating these landing data into the XML model as input signals, the prediction results were interpreted based on the relevance score (RS) derived from LRP. The interpretation obtained from XML was evaluated comprehensively from the statistical perspective based on Statistical Parametric Mapping (SPM) and Effect Size. The RS has excellent statistical characteristics in the interpretation of landing patterns between classes, and also conforms to the clinical characteristics of landing pattern recognition. The current work highlights the applicability of XML methods that can not only satisfy the traditional decision problem between classes, but also largely solve the lack of transparency in landing pattern recognition. We provide a feasible framework for realizing interpretability of ML decision results in landing analysis, providing a methodological reference and solid foundation for future clinical diagnosis and biomechanical analysis.
As online health information-seeking surges, concerns mount over the quality and safety of accessible content, potentially leading to patient harm through misinformation. On one hand, the emergence of Artificial Intelligence (AI) in healthcare could prevent it; on the other hand, questions raise regarding the quality and safety of the medical information provided. As laryngeal cancer is a prevalent head and neck malignancy, this study aims to evaluate the utility and safety of three large language models (LLMs) as sources of patient information about laryngeal cancer. A cross-sectional study was conducted using three LLMs (ChatGPT 3.5, ChatGPT 4.0, and Bard). A questionnaire comprising 36 inquiries about laryngeal cancer was categorised into diagnosis (11 questions), treatment (9 questions), novelties and upcoming treatments (4 questions), controversies (8 questions), and sources of information (4 questions). The population of reviewers consisted of 3 groups, including ENT specialists, junior physicians, and non-medicals, who graded the responses. Each physician evaluated each question twice for each model, while non-medicals only once. Everyone was blinded to the model type, and the question order was shuffled. Outcome evaluations were based on a safety score (1-3) and a Global Quality Score (GQS, 1-5). Results were compared between LLMs. The study included iterative assessments and statistical validations. Analysis revealed that ChatGPT 3.5 scored highest in both safety (mean: 2.70) and GQS (mean: 3.95). ChatGPT 4.0 and Bard had lower safety scores of 2.56 and 2.42, respectively, with corresponding quality scores of 3.65 and 3.38. Inter-rater reliability was consistent, with less than 3% discrepancy. About 4.2% of responses fell into the lowest safety category (1), particularly in the novelty category. Non-medical reviewers' quality assessments correlated moderately (r = 0.67) with response length. LLMs can be valuable resources for patients seeking information on laryngeal cancer. ChatGPT 3.5 provided the most reliable and safe responses among the models evaluated.