Yoxas/statistical_literacy huggingface dataset:
The increasing amount of e-waste and poor participation of individuals in proper recycling or disposal has become a big concern for policymakers. Therefore, it is essential to understand the factors that may facilitate or inhibit individuals from adopting e-waste recycling. The present research examines the attitude and intentions of individuals by applying the theoretical lens of Behavioral Reasoning Theory (BRT). The study uses a mixed-method approach and has been conducted in two phases: Phase 1- qualitative study (one-to-one interview: 30 respondents) and Phase 2: quantitative study (survey-based questionnaire- 348 responses). The qualitative part employs thematic analysis, and the quantitative study has been analyzed using SEM. Through the qualitative research, the study has identified self-image perceived negative effect and salvage value as the 'reasons for', whereas inconvenience, lack of support system, and emotional attachment have been identified as the 'reasons against' e-waste recycling. The findings show that moral and social norms significantly impact individual reasoning ('reason for'/'reason against') and attitude toward e-waste recycling. The result has also confirmed the moderation effect on self-efficacy among reasoning, attitude, and intention. The findings offer interesting insights to the government and policymakers for increasing individuals' participation in e-waste recycling.
The increasing burden of lifestyle-related diseases highlights the need to address unhealthy dietary habits. This study aims to explore the latest dietary patterns in Japan following the COVID-19 pandemic, focusing on trends in health-promoting food choices. A web-based survey was conducted among 27,154 Japanese adults, selected via quota sampling to mirror national demographics. The study evaluated dietary diversity, measured through the Dietary Variety Score (Outcome 1), and the prioritization of nutritional and health considerations in food selection, assessed via a Likert scale (Outcome 2). Uniform Manifold Approximation and Projection (UMAP) and Ordering Points To Identify the Clustering Structure (OPTICS) algorithms were used to delineate patterns in health-centric food selections. OPTICS clustering revealed four distinct clusters for each outcome. Cluster 3, with a diverse diet, comprised older, predominantly female individuals with higher well-being and lower social isolation compared to Cluster 4, which lacked distinct dietary patterns. Cluster 3 also engaged more in snacking, treat foods, home cooking, and frozen meals. Similarly, a divide emerged between those prioritizing dietary considerations (Cluster C) and those indifferent to such aspects (Cluster D). The findings underscore the need for holistic post-COVID-19 public health initiatives addressing socioeconomic and cultural barriers to healthier dietary practices.
The increasing numbers of students studying human anatomy at Makerere University are beginning to overwhelm available resources, which presents challenges in learning and necessitates an evaluation of alternative ways to enhance anatomy learning.The increasing numbers of students overwhelm resources available and presents challenges in learning. This necessitates an evaluation of alternatives to enhance gross anatomy learning at Makerere University College of Health Sciences. The study aimed to assess the usage of visual aids and generate necessary information to enhance learning. A cross-sectional study employing a concurrent triangulation mixed method design was conducted among 44 graduate students actively participating in cadaveric dissection. Data was collected using self-administered questionnaires and two focus group discussions. Descriptive statistics and thematic analysis were used to summarize data. Most participants were male (77.3%) with a mean age of 31.5 years (SD 3.9, Range, 27-45 years). A majority of graduate students reported using non-traditional methods (online sources) to supplement learning. Students commonly used hard copies of Cunningham Manual of Practical Anatomy (88.6%) supplemented with well-illustrated textbooks (79.5%) and online dissection videos (72.7%). Students expressed most satisfaction with the use of videos and well-illustrated text books in supplementing learning. The availability of these electronic resources was limited by factors such as poor internet connectivity and the need to pay for online licenses and subscription fees. Graduate students in the Department of Anatomy at MakCHS are using non-traditional methods to supplement their learning. However, there are several challenges to accessing digital resources. There is a need to support students with accessing visual aids through integrating newer teaching modalities and modern technology to promote interest and retention of anatomical knowledge.
The increasing prevalence of autism spectrum disorders (ASD) has led to worldwide interest in factors influencing the age of ASD diagnosis. Parents or caregivers of 237 ASD children (193 boys, 44 girls) diagnosed using the Autism Diagnostic Observation Schedule (ADOS) completed a simple descriptive questionnaire. The data were analyzed using the variable-centered multiple regression analysis and the person-centered classification tree method. We believed that the concurrent use of these two methods could produce robust results. The mean age at diagnosis was 5.8 ± 2.2 years (median 5.3 years). Younger ages for ASD diagnosis were predicted (using multiple regression analysis) by higher scores in the ADOS social domain, higher scores in ADOS restrictive and repetitive behaviors and interest domain, higher maternal education, and the shared household of parents. Using the classification tree method, the subgroup with the lowest mean age at diagnosis were children, in whom the summation of ADOS communication and social domain scores was ≥ 17, and paternal age at the delivery was ≥ 29 years. In contrast, the subgroup with the oldest mean age at diagnosis included children with summed ADOS communication and social domain scores < 17 and maternal education at the elementary school level. The severity of autism and maternal education played a significant role in both types of data analysis focused on age at diagnosis.
The increasing prevalence of inflammatory bowel disease is emerging as a significant global healthcare concern due to its recurrent episodes of intestinal inflammation. This study aims to explore the relationship between fear of progression and health-related quality of life in inflammatory bowel disease patients, investigating the sequential mediating roles of health literacy and self-care. A cross-sectional study. Eligible participants diagnosed with inflammatory bowel disease were recruited through convenience sampling method at a tertiary hospital in Shenyang, China, between May 2022 and May 2023. A total of 241 participants completed the questionnaires regarding fear of progression, health literacy, self-care and quality of life. Data analysis was conducted using SPSS 25.0 and PROCESS plug-ins. (1) Fear of progression was found to be negatively associated with quality of life and had a directly predictive effect on quality of life. (2) Health literacy and self-care could independently mediate the relationship between fear of progression and quality of life. (3) There was a significant chain mediating effect of health literacy and self-care in the relationship between fear of progression and quality of life. Fear of progression could not only directly predict the quality of life in inflammatory bowel disease patients but also indirectly affect their quality of life through the chain mediation of health literacy and self-care. This study highlights the importance of nursing staff focusing on the interplay between fear of progression, health literacy, self-care and quality of life in caring for inflammatory bowel disease patients. Understanding the potential correlation mechanism underlying quality of life may help develop targeted care interventions to synergistically enhance health literacy and self-care in these patients. STROBE guidelines. No public or patient contribution.
The increasing prevalence of large-scale data collection in modern society represents a potential threat to individual privacy. Addressing this threat, for example through privacy-enhancing technologies (PETs), requires a rigorous definition of what exactly is being protected, that is, of privacy itself. In this work, we formulate an axiomatic definition of privacy based on quantifiable and irreducible information flows. Our definition synthesizes prior work from the domain of social science with a contemporary understanding of PETs such as differential privacy (DP). Our work highlights the fact that the inevitable difficulties of protecting privacy in practice are fundamentally information-theoretic. Moreover, it enables quantitative reasoning about PETs based on what they are protecting, thus fostering objective policy discourse about their societal implementation.
The increasing prevalence of type 2 diabetes in working-age people imposes a substantial societal burden. Although physical activity is crucial for diabetes management, limited evidence exists to inform optimal strategies for promoting physical activity in this population. We aimed to determine and compare the effectiveness of interventions for increasing physical activity level in working-age people with diabetes. In this systematic review and meta-analysis, we searched Web of Science, the Cochrane Library, Medline, Embase, PsycINFO, ClinicalTrials.gov, and ICTRP for papers published between Jan 1, 1931, and June 30, 2022, in English. Search terms included "physical activity", "diabetes", and "randomised controlled trial". We included trials reporting the effects of interventions on physical activity level (objectively or subjectively measured) in people with type 2 diabetes aged 18-60 years. Two independent reviewers conducted summary data extraction and quality assessment. We used pairwise random-effects, frequentist network meta-analyses, and meta-regression to obtain pooled effects. Heterogeneity was evaluated using I2 statistic. The risk of bias and certainty of evidence were assessed using the Cochrane risk-of-bias 2 tool and the Grading of Recommendations Assessment, Development, and Evaluation. This study is registered with PROSPERO (CRD42022323165). We identified 52 trials (6257 participants) from 21 countries (32 Asia, ten North America, eight Europe, one Australia, one Africa). The overall risk of bias was classified as "some concerns" for included studies. Four types of interventions (structured exercise training, physical activity education, psychological intervention, physical activity education plus psychological intervention) were identified. Compared with control groups, the interventions showed significant effects in objectively measured (standardised mean difference 0·77, 95% CI 0·27-1·27, low certainty), subjectively measured (0·88, 0·40-1·35, very low certainty), and overall physical activity (0·82, 0·48-1·16, moderate certainty). Physical activity education exerted large effect in overall physical activity compared with control groups. Psychological intervention exerted large effects in overall physical activity compared with other interventions. Heterogeneity was high (I2=96-97%). Intervention setting (p=0·04) and facilitator (p=0·03) showed effects on heterogeneity. Psychologically modelled education might be the most beneficial way of promoting physical activity. Intervention setting and facilitator type should be considered when designing interventions for improving physical activity level in working-age people with type 2 diabetes. Limitations of this review include restriction to the English language and considerable heterogeneity between studies. King's-China Scholarship Council PhD Scholarship (202108440151).
The increasing ubiquity of data in everyday life has elevated the importance of data literacy and accessible data representations, particularly for individuals with disabilities. While prior research predominantly focuses on the needs of the visually impaired, our survey aims to broaden this scope by investigating accessible data representations across a more inclusive spectrum of disabilities. After conducting a systematic review of 152 accessible data representation papers from ACM and IEEE databases, we found that roughly 78% of existing articles center on vision impairments. In this paper, we conduct a comprehensive review of the remaining 22% of papers focused on underrepresented disability communities. We developed categorical dimensions based on accessibility, visualization, and human-computer interaction to classify the papers. These dimensions include the community of focus, issues addressed, contribution type, study methods, participants, data type, visualization type, and data domain. Our work redefines accessible data representations by illustrating their application for disabilities beyond those related to vision. Building on our literature review, we identify and discuss opportunities for future research in accessible data representations. All supplemental materials are available at https://osf.io/ yv4xm/?view_only=b36a3fbf7a14b3888029966faa3def9.
The increasing use of Artificial Intelligence (AI) in medicine has raised ethical concerns, such as patient autonomy, bias, and transparency. Recent studies suggest a need for teaching AI ethics as part of medical curricula. This scoping review aimed to represent and synthesize the literature on teaching AI ethics as part of medical education. The PRISMA-SCR guidelines and JBI methodology guided a literature search in four databases (PubMed, Embase, Scopus, and Web of Science) for the past 22 years (2000-2022). To account for the release of AI-based chat applications, such as ChatGPT, the literature search was updated to include publications until the end of June 2023. 1384 publications were originally identified and, after screening titles and abstracts, the full text of 87 publications was assessed. Following the assessment of the full text, 10 publications were included for further analysis. The updated literature search identified two additional relevant publications from 2023 were identified and included in the analysis. All 12 publications recommended teaching AI ethics in medical curricula due to the potential implications of AI in medicine. Anticipated ethical challenges such as bias were identified as the recommended basis for teaching content in addition to basic principles of medical ethics. Case-based teaching using real-world examples in interactive seminars and small groups was recommended as a teaching modality. This scoping review reveals a scarcity of literature on teaching AI ethics in medical education, with most of the available literature being recent and theoretical. These findings emphasize the importance of more empirical studies and foundational definitions of AI ethics to guide the development of teaching content and modalities. Recognizing AI's significant impact of AI on medicine, additional research on the teaching of AI ethics in medical education is needed to best prepare medical students for future ethical challenges.
The increasing utilization of large language models (LLMs) in Generative Artificial Intelligence across various medical and dental fields, and specifically orthodontics, raises questions about their accuracy. This study aimed to assess and compare the answers offered by four LLMs: Google's Bard, OpenAI's ChatGPT-3.5, and ChatGPT-4, and Microsoft's Bing, in response to clinically relevant questions within the field of orthodontics. Ten open-type clinical orthodontics-related questions were posed to the LLMs. The responses provided by the LLMs were assessed on a scale ranging from 0 (minimum) to 10 (maximum) points, benchmarked against robust scientific evidence, including consensus statements and systematic reviews, using a predefined rubric. After a 4-week interval from the initial evaluation, the answers were reevaluated to gauge intra-evaluator reliability. Statistical comparisons were conducted on the scores using Friedman's and Wilcoxon's tests to identify the model providing the answers with the most comprehensiveness, scientific accuracy, clarity, and relevance. Overall, no statistically significant differences between the scores given by the two evaluators, on both scoring occasions, were detected, so an average score for every LLM was computed. The LLM answers scoring the highest, were those of Microsoft Bing Chat (average score = 7.1), followed by ChatGPT 4 (average score = 4.7), Google Bard (average score = 4.6), and finally ChatGPT 3.5 (average score 3.8). While Microsoft Bing Chat statistically outperformed ChatGPT-3.5 (P-value = 0.017) and Google Bard (P-value = 0.029), as well, and Chat GPT-4 outperformed Chat GPT-3.5 (P-value = 0.011), all models occasionally produced answers with a lack of comprehensiveness, scientific accuracy, clarity, and relevance. The questions asked were indicative and did not cover the entire field of orthodontics. Language models (LLMs) show great potential in supporting evidence-based orthodontics. However, their current limitations pose a potential risk of making incorrect healthcare decisions if utilized without careful consideration. Consequently, these tools cannot serve as a substitute for the orthodontist's essential critical thinking and comprehensive subject knowledge. For effective integration into practice, further research, clinical validation, and enhancements to the models are essential. Clinicians must be mindful of the limitations of LLMs, as their imprudent utilization could have adverse effects on patient care.
The increasing utilization of the Internet to access health-related information is believed to heighten anxiety and trigger cyberchondria due to the presence of conflicting and sometimes overly detailed information. To investigate the levels of cyberchondria and intolerance of uncertainty among university students and their relationship with different online health information sources. Between June and July 2022, 420 university students (mean age = 21.5 ± 2 years, 54% female) participated in this cross-sectional study. The socio-demographic form, Uncertainty Intolerance Scale (IUS), and Cyberchondria Severity Scale (CSS) were used in data collection. Pearson correlation test and binary logistic regression analysis were used to identify factors associated with cyberchondria. Forums and private hospital/clinic websites were the most frequently utilized online health information sources, while governmental websites were the least. The mean scores for CSS and IUS were 81.5 and 41.0, respectively. There was no significant gender-based difference in CSS and IUS scores. Students who used newspapers/magazines and social media for health information had significantly higher scores on both scales. Cyberchondria had a negative association with age [odds ratio (OR) = 0.90], a positive significant relationship with newspaper/magazine website use (OR = 7.24), and IUS score (OR = 1.09). There was a positive and moderate correlation between CSS and IUS scores (r = 0.39, P < 0.001). Our results underline the susceptibility to cyberchondria and intolerance of uncertainty among young adults who used less reliable online health information sources and highlight promoting online health literacy to reduce vulnerabilities and the need for further research on socio-demographic determinants in both mental problems.
The increasing volume and intricacy of sequencing data, along with other clinical and diagnostic data, like drug responses and measurable residual disease, creates challenges for efficient clinical comprehension and interpretation. Using paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) as a use case, we present an artificial intelligence (AI)-assisted clinical framework clinALL that integrates genomic and clinical data into a user-friendly interface to support routine diagnostics and reveal translational insights for hematologic neoplasia. We performed targeted RNA sequencing in 1365 cases with haematological neoplasms, primarily paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) from the AIEOP-BFM ALL study. We carried out fluorescence in situ hybridization (FISH), karyotyping and arrayCGH as part of the routine diagnostics. The analysis results of these assays as well as additional clinical information were integrated into an interactive web interface using Bokeh, where the main graph is based on Uniform Manifold Approximation and Projection (UMAP) analysis of the gene expression data. At the backend of the clinALL, we built both shallow machine learning models and a deep neural network using Scikit-learn and PyTorch respectively. By applying clinALL, 78% of undetermined patients under the current diagnostic protocol were stratified, and ambiguous cases were investigated. Translational insights were discovered, including IKZF1plus status dependent subpopulations of BCR::ABL1 positive patients, and a subpopulation within ETV6::RUNX1 positive patients that has a high relapse frequency. Our best machine learning models, LDA and PASNET-like neural network models, achieve F1 scores above 97% in predicting patients' subgroups. An AI-assisted clinical framework that integrates both genomic and clinical data can take full advantage of the available data, improve point-of-care decision-making and reveal clinically relevant insights promptly. Such a lightweight and easily transferable framework works for both whole transcriptome data as well as the cost-effective targeted RNA-seq, enabling efficient and equitable delivery of personalized medicine in small clinics in developing countries. German Ministry of Education and Research (BMBF), German Research Foundation (DFG) and Foundation for Polish Science.
The Indian pharmaceutical market is flooded with different fixed drug combinations (FDCs), many of which lack a rational justification. The study aimed to assess the knowledge, attitude, and practice (KAP) regarding FDCs among the physicians of a tertiary-care teaching center. The target sample size for this cross-sectional study was calculated as 75, and the study was conducted between February and August 2020 by using a content-validated questionnaire. Descriptive statistics had been utilized for data analysis, and Chi-square test had been applied for intergroup comparison (with P < 0.05 considered to be significant). The mean age of the physicians who participated in this survey was 33.2 years. While 44% of them could identify all the potential advantages of using FDCs, only 09% could correctly recognize all the disadvantages associated with the same. Among the list of rational and irrational FDCs, only 49% could single out the irrational ones. And though the majority (83%) of the respondents do check for the rationality of FDCs before prescribing them, still out of 25 most commonly prescribed FDCs, 16 lacked any scientific justification for combination, and 09 (out of 16) had been banned by the Government of India. The outcome of this cross-sectional questionnaire-based study reflects the deficit in the knowledge regarding FDCs, as well as incoherence among the knowledge, attitude, and prescription practice. Though the physicians have cultivated a healthy attitude towards prescribing FDCs, the same is not reflected in their practice. In order to rectify these disparities, the authors have proposed certain recommendations within this article.