Yoxas/statistical_literacy huggingface dataset:
With the onset of puberty, youth begin to choose their social environments and develop health-promoting habits, making it a vital period to study social and biological factors contextually. An important question is how pubertal development and behaviors such as physical activity and sleep may be differentially linked with youths' friendships. Cross-sectional statistical network models that account for interpersonal dependence were used to estimate associations between three measures of pubertal development and youth friendships at two large US schools drawn from the National Longitudinal Study of Adolescent to Adult Health. Whole-network models suggest that friendships are more likely between youth with similar levels of pubertal development, physical activity, and sleep. Sex-stratified models suggest that girls' friendships are more likely given a similar age at menarche. Attention to similar pubertal timing within friendship groups may offer inclusive opportunities for tailored developmental puberty education in ways that reduce stigma and improve health behaviors.
With the pending implementation of the Closing the Gap 2020 recommendations, there is an urgent need to better understand the contributing factors of, and pathways to positive educational outcomes for both Aboriginal and non-Aboriginal children. This deeper understanding is particularly important in the Northern Territory (NT) of Australia, in which the majority of Aboriginal children lived in remote communities and have language backgrounds other than English (i.e. 75%). This study linked the Australian Early Development Census (AEDC) to the attendance data (i.e. government preschool and primary schools) and Year 3 National Assessment Program for Literacy and Numeracy (NAPLAN). Structural equation modelling was used to investigate the pathway from self-regulation and executive function (SR-EF) at age 5 to early academic achievement (i.e. Year 3 reading/numeracy at age 8) for 3,199 NT children. The study confirms the expected importance of SR-EF for all children but suggests the different pathways for Aboriginal and non-Aboriginal children. For non-Aboriginal children, there was a significant indirect effect of SR-EF (β = 0.38, p<0.001) on early academic achievement, mediated by early literacy/numeracy skills (at age 5). For Aboriginal children, there were significant indirect effects of SR-EF (β = 0.19, p<0.001) and preschool attendance (β = 0.20, p<0.001), mediated by early literacy/numeracy skills and early primary school attendance (i.e. Transition Years to Year 2 (age 5-7)). This study highlights the need for further investigation and development of culturally, linguistically and contextually responsive programs and policies to support SR-EF skills in the current Australian education context. There is a pressing need to better understand how current policies and programs enhance children and their families' sense of safety and support to nurture these skills. This study also confirms the critical importance of school attendance for improved educational outcomes of Aboriginal children. However, the factors contributing to non-attendance are complex, hence the solutions require multi-sectoral collaboration in place-based design for effective implementation.
With the premise that physical education classes should promote physical activity by teaching and learning fundamental motor skills, this study aimed to evaluate the fundamental motor skill proficiency of primary school students and determine the level of achievement of established learning outcomes for fundamental motor skills, as specified in the Polish National Physical Education Curriculum. A cross-sectional design was used for this study. The sample consisted of 2605 children and adolescents enrolled in grades 1-3 (ages 7-9, n = 1165), 4-6 (ages 10-12, n = 837), and 7-8 (ages 13-14, n = 603), including 1353 boys and 1252 girls. The Fundamental Motor Skills in Sport test, a qualitative and process-oriented assessment tool, was used to evaluate fundamental motor skills. The Fundamental Motor Skills in Sport test evaluates the following movement skills: hurdles, jumping rope, forward roll, ball bouncing, ball throwing and catching, and kicking and stopping a ball. The desired level of overall fundamental motor skill proficiency was achieved by only 2 % of students. An elementary level of fundamental motor skill proficiency was demonstrated by an additional 3.5 % of students. Further, the results showed that only 10-30 % of students had achieved mastery or were close to achieving mastery in a given fundamental motor skill. The skill with the lowest level of proficiency was jumping rope, which only 11 % of students had mastered or were near to mastering. The present study of a large, nationally representative sample of primary school students in Poland indicates that the vast majority (approximately 94 %) of them demonstrated insufficient fundamental motor skill proficiency. This may greatly hinder effective, safe, and healthy participation in lifelong physical activity.
With the prevalent problem of nurse retention and the lack of confidence new nurses experience, it is critical for nurse residency programs to have supportive factors that enhance residency experiences. By incorporating mentorship, preceptors, and debriefing components into these programs, skills in communication, organization, clinical reasoning, stress management, and self-confidence increase. This quantitative study was a descriptive analysis of secondary data using a cross-sectional design. Two instruments were used to gather data, the Demographic Information Survey and the Evaluation of the RN Residency Survey. The sample was comprised of 1078 graduate nurses completing nurse residency programs throughout the United States. The findings indicated that higher residency ratings were significantly related to higher preceptor and debriefing experiences. Results also showed no significant interaction effects of reduced stress due to mentoring on the relationship between preceptorship experiences or debriefing experiences and ratings of nurse residency. Consequently, it is important that new nurses are given the opportunity to take part in programs offering mentorship, preceptors, and debriefing elements, as nursing residents have found them to be highly beneficial. Furthermore, debriefing sessions strengthened their skills and built confidence during their first year of employment.
With the rapid aging of the domestic population, China has a strong incentive to increase the statutory retirement age. How retirement affects the health of the elderly is crucial to this policymaking. The health consequences of retirement have been debated greatly. This study aims to investigate the effects of retirement on physical and mental health among Chinese elderly people. The data we use in this study comes from four waves (2011, 2013, 2015, and 2018) of the Harmonized China Health and Retirement Longitudinal Study (Harmonized CHARLS), a prospective cohort. We use the nonparametric fuzzy regression discontinuity design to estimate the effects of retirement on physical and mental health. We test the robustness of our results with respect to different bandwidths, kernel functions, and polynomial orders. We also explore the heterogeneity across gender and education. Results show that retirement has an insignificant effect on a series of physical and mental health outcomes, with and without adjusting several sociodemographic variables. Heterogeneity exists regarding gender and education. Although stratified analyses indicate that the transition from working to retirement leaves minimal effects on males and females, the effects go in the opposite direction. This finding holds for low-educated and high-educated groups for health outcomes including depression and cognitive function. Most of the results are stable with respect to different bandwidths, kernel functions, and polynomial orders. Our results suggest that it is possible to delay the statutory retirement age in China as retirement has insignificant effects on physical and mental health. However, further research is needed to assess the long-term effect of retirement on health.
With the rapid aging of the population structure, and the suicide ideation rate also increasing year by year, the ratio of people over 65 to the total number of deaths is increasing yearly. The study provides a reference for researchers interested in older adults' care to explore SI further affecting older adults in the future and provide a reference for qualitative research methods or interventional measures. The objective of this study is to explore the influence of mental health status, life satisfaction, and depression status on suicidal ideation (SI) among hospitalized older adults. In a cross-sectional correlation study, taking inpatients over 65 years old in a regional teaching hospital in eastern Taiwan, and the BSRS-5 ≧ 5 points of the screening cases, a total of 228 older adults agree to conduct data analysis in this study. Mainly explore the influence of personal characteristics, mental health status, life satisfaction, and depressed mood on SI among the hospitalized older adults. The basic attributes of the cases used in the data, mental health status, cognitive function, quality of life, depression, and suicide ideation, the data obtained were statistically analyzed with SPSS 20/Windows, and the descriptive statistics were average, standard deviation, percentage, median, etc. In the part of inference statistics, independent sample t-test, single-factor analysis of variance, Pearson performance difference correlation, and logistic regression analysis were used to detect important predictors of SI. Research results in (1) 89.5% of hospitalized older adults have a tendency to depression. 2.26.3% of the older adults had SI. (2) Here are significant differences in the scores of SI among hospitalized older adults in different economic status groups and marital status groups. (3) The age, marital status, and quality of life of the hospitalized older adults were negatively correlated with SI; economic status, self-conscious health, mental health, and depression were positively correlated with SI. (4) The results of the mental health status and SI is (r = .345, p < .001), higher the score on the BSRS-5 scale, the higher the SI. The correlation between the depression scale score (SDS-SF) and SI was (r = .150, p < .05), the higher the depression scale score, the higher the SI. The results of the study found that there was a statistically significant correlation between SI in older adults and age, marital status, economic status, mental health, quality of life, and depression, and also showed that they might interact with each other; the older adults in BSRS-5, GDS-SF, quality of life scale scores have statistically significant differences as essential predictors of SI. The results of this study suggest that medical staff can use the BSRS-5 scale to quickly screen and evaluate the mental health status of older adults, hoping to detect early and provide preventive measures, thereby improving the quality of life of older adults.
With the rapid development of artificial intelligence (AI) and related technologies, AI algorithms are being embedded into various health information technologies that assist clinicians in clinical decision making. This study aimed to explore how clinicians perceive AI assistance in diagnostic decision making and suggest the paths forward for AI-human teaming for clinical decision making in health care. This study used a mixed methods approach, utilizing hierarchical linear modeling and sentiment analysis through natural language understanding techniques. A total of 114 clinicians participated in online simulation surveys in 2020 and 2021. These clinicians studied family medicine and used AI algorithms to aid in patient diagnosis. Their overall sentiment toward AI-assisted diagnosis was positive and comparable with diagnoses made without the assistance of AI. However, AI-guided decision making was not congruent with the way clinicians typically made decisions in diagnosing illnesses. In a quantitative survey, clinicians reported perceiving current AI assistance as not likely to enhance diagnostic capability and negatively influenced their overall performance (β=-0.421, P=.02). Instead, clinicians' diagnostic capabilities tended to be associated with well-known parameters, such as education, age, and daily habit of technology use on social media platforms. This study elucidated clinicians' current perceptions and sentiments toward AI-enabled diagnosis. Although the sentiment was positive, the current form of AI assistance may not be linked with efficient decision making, as AI algorithms are not well aligned with subjective human reasoning in clinical diagnosis. Developers and policy makers in health could gather behavioral data from clinicians in various disciplines to help align AI algorithms with the unique subjective patterns of reasoning that humans employ in clinical diagnosis.
With the rapid development of artificial intelligence, automated endoscopy-assisted diagnostic systems have become an effective tool for reducing the diagnostic costs and shortening the treatment cycle of patients. Typically, the performance of these systems depends on deep learning models which are pre-trained with large-scale labeled data, for example, early gastric cancer based on endoscopic images. However, the expensive annotation and the subjectivity of the annotators lead to an insufficient and class-imbalanced endoscopic image dataset, and these datasets are detrimental to the training of deep learning models. Therefore, we proposed a Swin Transformer encoder-based StyleGAN (STE-StyleGAN) for unbalanced endoscopic image enhancement, which is composed of an adversarial learning encoder and generator. Firstly, a pre-trained Swin Transformer is introduced into the encoder to extract multi-scale features layer by layer from endoscopic images. The features are subsequently fed into a mapping block for aggregation and recombination. Secondly, a self-attention mechanism is applied to the generator, which adds detailed information of the image layer by layer through recoded features, enabling the generator to autonomously learn the coupling between different image regions. Finally, we conducted extensive experiments on a private intestinal metaplasia grading dataset from a Grade-A tertiary hospital. The experimental results show that the images generated by STE-StyleGAN are closer to the initial image distribution, achieving a Fréchet Inception Distance (FID) value of 100.4. Then, these generated images are used to enhance the initial dataset to improve the robustness of the classification model, and achieved a top accuracy of 86 %.
With the rapid development of information technology, artificial intelligence has demonstrated great potential in promoting educational transformation. In November 2022, the release of the artificial intelligence product ChatGPT attracted widespread attention, particularly in the field of education, sparking heated discussions among scholars. As a language processing tool, ChatGPT can not only answer user questions but also complete user-specified tasks and even continuously optimize task performance. However, while possessing powerful features, ChatGPT also has some shortcomings that need improvement, such as the accuracy of answering questions, data pollution issues, ethical and safety concerns, and the risk of knowledge plagiarism. In the process of promoting school education reform, the application of ChatGPT brings both opportunities and challenges. Moreover, ChatGPT's emergence offers teachers an opportunity to reflect on their professional value and sets higher demands for them.
With the rapid development of mobile Internet information technology, automated search text has occupied a leading position in many industries. This article not only makes a detailed case study on the basic working principles of text feature extraction and classification methods but also makes in-depth case analysis on the extraction algorithm and its basic concepts as well as some problems that may be encountered in text feature classification and explained their advantages and disadvantages in detail. Aiming at the shortcomings of various algorithms, a sparse Bayesian probability model is proposed, so that it can better meet the requirements of database and text classification and further improve related technologies. Nowadays, the evaluation of China's goodwill value, whether in theory or in practice, usually simply adopts traditional fixed asset evaluation methods. However, traditional methods have the disadvantages of ignoring comparisons with the same industry and failing to take into account different factors that affect corporate goodwill. This article adopts a new method that combines traditional methods to evaluate goodwill and tries to improve the results obtained by this traditional method to make the evaluation results more accurate. Then, by studying the adaptability of traditional Chinese risk assessment and forecasting models, a comprehensive comparison is made. Aiming at the embarrassing situation that the current methods of corporate excess asset return risk assessment difficult to predict in practice, the new gray factors evaluation models are creatively studied.
With the rapid development of societal information, electronic educational resources have become an indispensable component of modern education. In response to the increasingly formidable challenges faced by secondary school teachers, this study endeavors to analyze and explore the application of artificial intelligence (AI) methods to enhance their cognitive literacy. Initially, this discourse delves into the application of AI-generated electronic images in the training and instruction of middle school educators, subjecting it to thorough analysis. Emphasis is placed on elucidating the pivotal role played by AI electronic images in elevating the proficiency of middle school teachers. Subsequently, an integrated intelligent device serves as the foundation for establishing a model that applies intelligent classification and algorithms based on the Structure of the Observed Learning Outcome (SOLO). This model is designed to assess the cognitive literacy and teaching efficacy of middle school educators, and its performance is juxtaposed with classification algorithms such as support vector machine (SVM) and decision trees. The findings reveal that, following 600 iterations of the model, the SVM algorithm achieves a 77% accuracy rate in recognizing teacher literacy, whereas the SOLO algorithm attains 80%. Concurrently, the spatial complexities of the SVM-based and SOLO-based intelligent literacy improvement models are determined to be 45 and 22, respectively. Notably, it is discerned that, with escalating iterations, the SOLO algorithm exhibits higher accuracy and reduced spatial complexity in evaluating teachers' pedagogical literacy. Consequently, the utilization of AI methodologies proves highly efficacious in advancing electronic imaging technology and enhancing the efficacy of image recognition in educational instruction.
With the rapid development of the era of artificial intelligence, the application ability of programming is also highlighted. As one of the necessary abilities of social talents in the future, primary and secondary schools pay more and more attention to this, and programming education is also in full swing. Therefore, based on previous studies, this paper further clarifies the current situation when the current situation of programming education in primary and secondary schools is ambiguous. This paper is aimed at a wide range of primary and secondary school teachers. With 1500 teachers who participated in the online training class for programming teachers as the object in Chinese primary, middle and high school stages, mainly from the three levels of schools, teachers, and students. The questionnaire with good reliability and validity test was used as the research method, the survey data were statistically described and analyzed, and differences were analyzed using Microsoft Excel2019, SPSS26.0 and so on, it investigates and analyzes the current situation of programming education in primary and secondary schools. Results indicate that the overall quality of programming education offerings in elementary and secondary schools is subpar, and the construction of programming education curriculum in schools requires improvement. Nevertheless, schools prioritize improving students' comprehensive abilities, and teachers hold a positive attitude towards programming education and teaching. Although students demonstrate a strong interest in learning, their foundation is weak, resulting in poor learning outcomes. Consequently, the author provides specific recommendations regarding programming education's working mechanism, curriculum standard system, teacher training, and educational resources sharing to better develop programming education in primary and secondary schools.
With the rapid expansion of genomic medicine, more citizens are compelled to think about genetics in their daily lives. This study aims to explore appropriate types of educational media and methods to enlighten activities for genetics and hereditary cancer. We presented an 18-min YouTube video on genetics and hereditary cancer to participants at a scientific event, Science Agora 2020, and administered a web questionnaire to investigate their opinions about when and how citizens should start learning about genetics and hereditary cancer. We recruited 133 participants who watched the video, and 26.3% (35/133) responded to the questionnaire. Most of them were evaluated to understand and appreciate the contents of the video. They identified websites, or videos as suitable learning media, irrespective of their sex, age, or profession. They highlighted upper elementary school or junior high school as appropriate educational stages to start learning about genetics and hereditary cancer to facilitate collecting their own genetic information by themselves. Our findings show that educational institutions should provide opportunities to learn about genetics and hereditary cancers, especially for upper elementary school and junior high school students, using learning media, such as videos, depending on their level or demand.
With the rapid urbanization in many countries, more attention is being paid to the relationship between urbanization and mental health, especially depression. However, in countries with rapid urbanization, few empirical studies exist on the relationship between urbanization and adolescent depression. Nationally representative survey data from the China Family Panel Studies in 2012, 2016 and 2018 were used. Data of 1,588 adolescents were obtained from 25 provinces. Depression was measured using the Center for Epidemiology Studies of Depression 20-item score. The urbanization rate was obtained from the National Bureau of Statistics of China. The generalized estimating equation was used to estimate the statistical relationship. The participants' mean age at baseline was 15 years, and 51.2% (813/1,588) of participants were male. After adjusting for all covariates (gender, age, ethnicity, level of education, marital status, urban/rural areas, body mass index, self-rated health, academic pressure, smoking, drinking and exercise), the rate of urbanization was monotonically and negatively associated with adolescent depression (odds ratio 0.34, 95% CI [0.14-0.79]). Compared with female adolescents, male adolescents had a lower risk of depression (odds ratio 0.80, 95% CI [0.67-0.97]). In the context of China, urbanization has a positive effect on the mental health of adolescents. Female adolescents are more likely to experience depression than male adolescents.